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Accelerating and Widening Knowledge of the Everyday: Reducing Churn for a Financial Service What a Thousand Dollars Can Do that a Million Dollars Cannot

DOI: 10.31038/PSYJ.2023541

Abstract

This paper responds to a Linked In post by Matt Lerner, regarding efforts by PayPal, Inc. to segment the market, identify personas, and move towards more actionable marketing efforts. The reported disappointing results came after thousands of interviews, a period of one year to design the research, collect the data, and analyze the results, with an expenditure of 1mm dollars. Using the same challenge, to provide a company such as PayPal with powerful, actionable information, the study of 100 people using artificial intelligence embedded in Mind Genomics, generated results and insights presented here, doing so in approximately two hours from start to finish, at an out-of-pocket cost slightly above $400. The results are presented as an exemplar of easy-to-create databases of the human mind on topics that range from profound to quotidian events, the everyday situations that escape notice but could contribute to a deeper knowledge of people and society.

Introduction

In early March 2023, the following post appeared in Linked In, a social media site specializing in business connections. The tonality of the post coupled with the specific information provides an implicit challenge to today’s methods to build systematic knowledge databases. Lerner moved from the standard methods of developing personas in segmentation [1] to the important approach called JTDB (jobs to be done), a contribution by the late Harvard business professor, Clayton Christensen [2]. Figure 1 presents a screen shot of the first part of the Linked In post, leaving out the details about the JTDB.

fig 1

Figure 1: Screen shot of post by Matt Lerner regarding PayPal

The post by Lerner immediately generated a cluster of strong reactions, as perhaps it was meant to do. The most important reaction was the sense that here was an opportunity to demonstrate what could be done in an hour or two to solve the same problem, albeit with a different worldview (experimentation rather than hypothesis generation). We chose the road ‘less trodden,’ viz., describe and attempt to provide direct business solutions using a combination of simple thinking, direct experimentation, artificial intelligence, focusing almost on the basis of the business issue for PayPal, namely solving a problem (reducing impediments to customer usage and customer retention).

We offer this paper as an example of what can be done today (2023) in about 1-3 hours, at a cost of a few hundred dollars. This alternative approach involves thinking, reduces the cycle time for learning, demands far lower investments for the knowledge, and produces databases of knowledge, local, generally, in the moment, or over time to provide time-based, geography-based knowledge. Rather than providing a different approach to the specific problem, the authors present a general re-thinking of the issue as one of the ‘production of useful information’. The paper is not a solution as much as a stimulant for discussion. We present our approach to tackling the PayPal issue, this time using Mind Genomics. Mind Genomics is an experimenting science of decision making and behavior, tracing its origins to experimental psychology (psychophysics), statistics (experimental design), and public opinion and consumer research.

Psychophysics, the oldest branch of psychology, is the study of the relation between physical stimuli and subjective reactions to those stimuli. The objective is to measure the perception of the stimulus, viz, a subjective measurement, and then relate that measure to the nature and magnitude of the physical stimulus. Harvard Professor of Psychophysics, S.S Stevens, called this discipline the ‘outer psychophysics’. Mind Genomics focuses on what Stevens called the ‘inner psychophysics,’ the structure and measurement of relations between ideas [3].

Statistics provides a way of dealing with the world, analyzing the measures, finding relations, defining order of magnitude and the evidence of effects of one variable on another. Statistics also allow us to find ‘order’ in nature, and in some cases help us interpret the order. The discipline of experimental design allows us to create test combinations of stimuli, those stimuli being combinations of phrases or ingredient [4], or even combinations of other variables, such as combinations of pictures to study responses to a package [5]. Experimental design lets us understand relations between variables in a clear fashion, moving the world of ‘insights’ out from disciplined description to quasi-engineering. Finally, consumer research and opinion polling focus on the nature of what is being measured. Rather than looking for general principles of behavior, deep behaviors, often needing artificial situations in which these deep principles can be illustrated, consumer research works with the quotidian, the everyday, the granular in which life is lived and experienced [6]. The consumer researcher is interested in the reactions to the world of the everyday, as the world is constituted, rather than concentrating on unusual combination, structured in an unusual fashion to illustrate an effect. Our stated goal for the project was to see how quickly and how inexpensively we could ‘solve’ the problem, or at least contribute materially to the solution. The ‘real’ goal, however, was to create a series of templated steps to solve the problem and offer those steps to the world community as an ‘algorithm’ to approach the creation of new knowledge about decision making, assuming the effort to start with absolutely no knowledge at all. Rather than theorizing about the best steps, opining about what should be done and why, we began with the belief that the best approach would be simply ‘do it’, and see what happens. In this spirit, we offer the reader our templated approach, with results, and with the delight that the effort lasted about two hours, cost about $400 (but could have been less), and that that effort produced clear, understandable, testable results. The final delight is that had the initial effort been less successful there was another two-hour slot immediately afterwards to build on the partially successful first effort.

How Mind Genomics Works

Mind Genomics differs from the traditional questionnaire. In the traditional approach, the researcher presents the respondent with a phrase or other test stimulus and instructs the respondent to rate that single stimulus. The pattern of responses to many such stimuli provides the raw materials. Such a system might at first seem to be the very soul of good research, because the stimulus is isolated, and rated one at a time. In some cases that might be the case, but when we deal with real people we are faced with the ongoing desire for the respondent to ‘game’ the system, to provide what is believed to be the ‘right answer’, perhaps an answer that the respondent feels to be one that the researcher will more readily accept. The published literature recognizes these types of response biases, and has done for at least 60 years, and more like 80 years [7,8].

Mind Genomics operates differently. Mind Genomics works by combining phrases, presenting combinations of these phrases to respondents, obtaining a rating of the combination, and then deconstructing the response to the combination in order to understand how each phrase drives the response. In a Mind Genomics study the respondent evaluates different combinations, generally 24 different combinations of phrases. Each combination or ‘vignette’ in turn comprises 2-4 phrases (elements), with these elements appearing five times in the 24 vignettes evaluated by each respondent and absent 19 times in the 24 vignettes.

Often researchers who look at the Mind Genomics studies complain that it seems to be almost impossible to ‘do this study correctly.’ The inability to ‘guess’ the right answer because of the apparently random combinations of elements irritates many professionals, who feel that the respondent has to cope with a ‘blooming, buzzing confusion,’ the term that psychologist William James used to describe the perceptual world of the newborn child [9]. The reality, however, is that most respondents who think they are guessing actually do quite well, as they negotiate through the 24 vignettes. They pay attention to what is important to them. The result is a clear pattern, often a pattern which might surprise them by its correctness and clarity in the light of their experience with these combinations of messages that seemed so random.

The Mind Genomics Steps – from Chaos to Tentative Structure

We present the Steps in Mind Genomics, assuming that we start with virtually no knowledge at all about the issues involved with PayPal, other than possible customer issues which may or may not end up in ‘churn.’ The reality of the process is far deeper than one might imagine. Virtually all research conducted by author Moskowitz since first starting a career in 1969 has revealed that most researchers in the business community do not really profoundly understand how to solve specific problems, although with a bit of study many learn to discern the relevant aspects of a problem, and eventually move towards a solution, whether that solution be optimal or not. Thus, the need for an algorithmic approach to problem design and problem solution, a solution which can be implemented even by a young person (e.g., age 10 or so).

The authors of this paper are all reasonably senior or beyond. In order to keep to the vision of an algorithmic solution doable quickly and easily by anyone, we have limited all of the effort to working with artificial intelligence as a provider of substantive materials for questions and answers pertaining to PayPal and its issues.

Step 1: Choose a Name (Figure 2, Top Left Panel)

Naming requires that the researcher focus on what is to be studied. Choosing a name is generally simple, but not always. Even in this study there was a bit of hesitation about what to call the study. Such hesitation is revealing. It means that the researcher may have a general idea about the topic but must focus. That focus can be a bit discomforting at first, because it means deliberately limited the effort, almost hypothesizing at the start of the project about what is the real ‘goals’ Figure 2 (top left panel) shows the screen where the respondent names the study.

fig 2

Figure 2: Set-up screen shots. Top Row Left Panel = select a name for the study, Top Row Right panel = Idea Coach input to provide questions. Bottom Row Left panel = 7 of 30 questions generated by Idea Coach, Bottom Row Right Panel = The four questions finally chosen (screen shot shows partial text).

Step 2: Choose Four Questions Which ‘Tell a Story’

The objective here is to lay the groundwork for a set of test elements or messages that will be shown to the respondent in systematically varied combinations. Rather than simply drawing these test elements out of the ‘ether’ and having respondents rate each one, Mind Genomics instructs the research to create a story, beginning with questions flowing in a logical sequence. Those questions will be used to generate answers. A recurrent problem faced by researchers using Mind Genomics is that the ordinary, unskilled professional often gets lost at this early stage. It is daunting to think of questions. Answers are easy; we are accustomed to answering questions from our early and later education. It is the questions which are difficult. We are not accustomed to thinking of good questions, except when we debate in a competitive way, and have to hone down our answers, or perhaps when we begin higher education after college. Before then, college and earlier, our expertise is answering, not asking. It is no wonder that many would-be researchers attempting to follow the steps of Mind Genomics simply throw up their hands at this step.

Our ‘demo study’ on PayPal is a perfect example. We know the problem. But what are four relevant questions that we should ask? We are not accustomed to thinking about questions, and so we need an extra ‘hand’ to pass through this Step 2. The approach we use employs AI, artificial intelligence, embedded in the Idea Coach. The researcher describes the problem (Figure 2, top right panel), lets Idea Coach use the description to produce sets of 30 questions (Figure 2, bottom left), and across several uses of Idea Coach. The research will end up with four questions (Figure 2 bottom right).

The important thing to keep in mind is that the researcher can interact with the AI driven Idea Coach. The briefing given to Idea Coach (Figure 2 top right panel) can be run several times, each time with different questions emerging, along with repeat questions. The briefing can be changed, and the Idea Coach is re-run, again producing different sets of 30 questions. Finally, the questions which emerge from Idea Coach can themselves be changed by the user. Table 1 shows the four questions in their final text form, along with the four answers to each question.

Table 1: The four final questions, and the four answers to each question. Questions and answers emerged from Idea Coach, powered by AI.

tab 1

Step 3: Select Four Answers to Each Question

Once the researcher selects the questions, the BimiLeap program presents each question 2, with a request to provide four answers. Figure 3 shows this third step. The top left panel in Figure 3 shows the layout, presenting the first question for the researcher, and requesting four answers. Often researchers find this step easy. For those who want to use Idea Coach, the question is already selected, but can be edited, and then Idea Coach invoked (Figure 3, Top Row, Right screen). Each request to Idea Coach uses the question as Idea Coach currently finds it. As the researcher learns more about the topic from Idea Coach, the researcher can run many requests to get the four answers, or change the question, and rerun the Idea Coach. The Bottom Row (left panel) shows 7 of the 15 answers.

fig 3

Figure 3: Creating four answers for a single question, showing the contribution of Idea Coach

The Bottom Row (right panel) shows the four answers selected or written in. Once again, the answers can be used as Idea Coach provides them, or edited, or even some answers can be provided by the researcher without using Idea Coach. As the researcher becomes more familiar with the Mind Genomics templated process it becomes easier to skip the Idea Coach steps, at least when providing answers.

Step 4: Create an Orientation Page and a Rating Scale

Respondents in the Mind Genomics study will be presented with vignettes, viz., with combinations of messages. The respondent has to be instructed what to do. In most studies it suffices to instruct the respondent to read the vignette. Figure 4 (Top Left Panel) shows the orientation page, presented at the start of the study. Right below (Figure 4, Bottom Left Panel,) appear the instructions accompanying each test stimulus (vignette, described below), along with the set-up page to define the scale. The five-point scale used here is a simple Likert scale, with the middle scale point reserved for ‘don’t know.’ Respondents find this scale easy to use.

fig 4

Figure 4: Left panels show the orientation to the respondent (Left Panel, Top Row), and the rating scale to be used for each vignette (Left Panel, Bottom Row). Right panel Top Row shows the instructions for the open-end question regarding feelings about PayPal. Right Panel Bottom Row shows the instructions regarding the acquisition of respondents.

There is little guidance given to the respondent, the reason being that it is the elements which must convey the information, not the instruction. Only in situations where it is necessary for the respondent to understand the background facts more deeply, e.g., law cases, does the respondent orientation move beyond the basics of ‘read and rate.

Step 5: Launch the Study

Once the study is created, a process requiring about 30-40 minutes, the final task is to launch the study. In the interests of efficiency, the BimiLeap program provides the researchers with four builds in options, as shown in Figure 4 (Bottom Row, Right Panel). The standard approach is to use a built-in link to the panel provider (Luc.id), for easy-to-find respondents of specific gender, age, income, education, country, etc. This standard approach is made easy. All the research need do it select the top bar in the screen shot. The researcher ends up paying about $4.00/respondent for respondents in most geographies. Below are other options, such as a custom sample of respondents, a third-party provider of respondents (e.g., not Luc.id, Inc.), and finally the ability to source one’s own respondents at the fee of $2.00/respondent processed. In all cases but the first, with BimiLeap providing the respondent, it is the researcher who must assume the responsibility of finding respondents. For this study, the request was for n=100 respondents, males and females, ages 18-54.

The Mind Genomics process works best with respondents who are part of a panel. The panel comprises many hundreds of thousands, perhaps millions of individuals, whose qualifications are known, and who have agreed to participate in these types of studies. The field service (Luc.id Inc., for this study) sends out invitations to respondents who fit the criteria requested by the researcher. The entire mechanism is automated. In the interests of cost and efficiency, it is almost always better to work with standard respondents provided by BimiLeap. The time between launch and completion of Mind Genomics sessions, one per respondent, is generally 50-60 minutes for the respondents specified here.

In the end, Steps 1-5 required about a little less than two hours from start of the study with ‘no knowledge’. The results are returned by email, the detailed analysis along with summarization through AI contained in an Excel report.

Step 6: The Respondent Experience

The respondents receive an email invitation. Those who click on the embedded invitation link are led to the study. The first screens introduce the topic, obtain information about the respondent. The standard information is gender and age. The third self-profiling question was the respondent’s experience-with/opinion-of PayPal.

The actual experience comprises a set of 27 screens.

  1. Welcome.
  2. Self-profiling classification (gender, age, attitude/experience regarding PayPal. The self-profiling classification has room for a total of 10 questions, each question with 10 possible answers.
  3. Introduction to the issue.
  4. Presentation of 24 screens, each screen comprising 2-4 rows of elements, and the rating scale below.

The noteworthy thing to keep in mind about the experience is that each respondent evaluates a set of vignettes which comprise seemingly unconnected elements, as Figure 2 shows. To many respondents and to virtually all professionals who inspect the 24 vignettes, the array of 2-4 elements in vignette after vignette speaks of a ‘blooming, buzzing confusion’ in the words of the revered Harvard psychologist, William James, writing at the end of the 19th century. Nothing, however, could be further from the truth. The 24 vignettes are set up in an specific array, called an experimental design,, with the property that the 16 elements are presented an equal number of times, that they are statistically independent of each other, that the data emerging from any single set of 24 vignettes from one respondent can be analyzed by OLS (ordinary least squares) regression, and finally the coefficients have ratio scale properties. The design is called a permuted design.

Figure 5 shows the content of the three vignettes recorded after the evaluation, and just before deconstruction in to the record-by-record database used in the statistical analysis. The figure shows the respondent number, the order of the vignettes, the text of the vignette as presented to the respondent, followed by the rating scale and the response time. The rating scale is taken from Figure 4 (bottom left panel).

fig 5

Figure 5: Content of three vignettes, as recorded by the BimiLeap program, showing the respondent (participant), the text of the vignette, the rating, and the response time in thousands of a second.

The respondents are oriented with what ends up being very little information, but after the first evaluation the respondent find the evaluation easy to do. Figure 6 shows the average response time by each position of the 24 positions. By the time the third or really fourth vignette is evaluated, the respondent feels comfortable with the process, and settles down to a about 2-2.5 seconds per vignette. One of the unexpected implications of these results is that the initial set of responses may be unstable, at least in terms of the externally measured variable of response time. It may be that the decreasing response time is due to the time taken to develop an automatic point of view, one which may not change during the last 20 or so vignettes. If this is the case, then we might not want to look at the data from the first part of the study simply because the processing of the information has not reached ‘steady’ state.’ The implications call for a rethink of just how to measure attitudes when the ratings for the first few questions are labile as a point of view emerges and solidifies, unbeknownst to the respondent and to the researcher alike. This is an interesting finding, and reinforces the good research practice of randomizing the different test stimuli.

fig 6

Figure 6: How average response time to the vignettes varies with test order

Table 2 presents the final information recorded for the study, including name, number of respondents, etc. This table is presented for archival purposes in every report of the study returned to the researcher.

Table 2: Final specifics of the study, based upon the input for the researcher

tab 2

Step 7: Create the Database in Preparation for Statistical Analysis

All of the set up and research steps become preparations for a database that can be accessed by statistical analysis. The database is ‘flat,’ with all of the relevant information in one file. Thus, beyond the automatic analysis of the data to be done by the BimiLeap program, the raw data are available for further custom analysis by the researcher.

The database comprises one record or row for each vignette. Thus, 100 respondents, each of whom evaluate 24 different vignettes, generate a database of 100 x 24 or 2400 rows. The entries in the database are usually numbers ready for immediately statistical analyses, or easily converted to new variables for additional analysis.

First set of columns – correspond to the study name and the information about the respondent, including a respondent identification number unique for the Mind Genomics system, as well as a sequence number for the particular study. The data in this first set of columns correspond to information which remains the same across all 24 vignettes.

Second set of numbers – change according to the vignette. The first number is the order number, from 01 (first vignette in the set of 24) to 24 (the 24th vignette in the 24). The ‘actual first vignette’ is used as training, data not recorded. The actual first vignette is repeated to become the 24th of 24 vignettes whose data are recorded. The next set of 16 elements, 2nd to 17th, correspond to the 16 elements. For a specific row or vignette, the elements which appear in that vignette are coded ‘1’, the elements absent from that vignette are coded ‘0.

Third set of numbers – vary according to the 5-point rating assigned by the respondent, and then the response time in thousandths of a second elapsing between the time that the vignette appeared on the screen and the time that the respondent assigned a rating using the 5-point scale.

The fourth set of numbers is created by the program or by the researcher working with the raw data. This fourth set of numbers is called the binary transformed data. The objective of the binary transformation is to move from a scale to a yes/no measurement. The reason for doing so is pragmatic, based on the history of consumer research and public opinion polling. Those who use the scales, such as managers in companies find it difficult to understand how to interpret the average value of a scale, such as our 5-point scale. For example, just what does a 4.2 mean on the scale? Or a 2.1? And so forth. The question is not whether two scale values ‘differ’ from each other in a statistical sense, but rather just what does this mean tell the manager? Is it a good score? A bad score? How does on interpret the scale value, the average rating, and communicate its real meaning to others?

The consumer researcher and public opinion pollsters have realized that the ordinary person can easily deal with concepts such as ‘a lot of people were positive’ or the message convinced some of the people to change their attitude from mildly positive to deeply negative. To simplify the interpretation, these researchers and pollsters have transformed the 5-point scale (or other scales like in) into discrete scale, such as ‘positive to an idea’ versus ‘negative to an idea’. The typical transformation on a 5-point scale (5 = agree, 1 = disagree) is that the ratings of 4 and 5 are ‘agree with / positive to an idea, whereas the ratings of 1.2, and 3 agree ‘not agree with / positive to an idea’. Following this train of thought, the binary transformation would be ratings of 5 and 4 are transformed to 100, whereas ratings of 3,2 and 1 are transformed to 0 This transformation produces 100’s and 0’s. The transformation is called, not surprisingly, ‘TOP2’. In other studies, there might be several transformations, such as BOT2 (Ratings 1,2 → 100, Ratings 3,4,5 → 0). A vanishingly small random number (<10-5) is added to each transformed number, to ensure that the binary transformed variables exhibit some variation, a variation that will be necessary for analysis by OLS (ordinary least-squares) regression.

Step 8: Relate the Presence/Absence of Elements to the Binary Transformed Variable, TOP2

The underlying objective of Mind Genomics is to relate subjective feelings (responses) to the underlying messages. The entire thinking, preparation and field execution is devoted to the proper empirical steps needed to discover how the different ideas embodied in the elements drive the response.

The TOP2 variable is the positive response to PayPal selected after reading the vignette (Definitely/Probably use PayPal). How does each of our 16 elements ‘drive’ that feeling. And, what it the pattern across the different genders, ages and PayPal-related attitudes and self-described behaviors?

The analysis uses OLS (ordinary least-squares) regression analysis, colloquially known as curve fitting, although the model here is strictly linear, with no curvature [10]. We express the dependent variable, Binary Transformed Variable, TOP2 as a weight sum of the elements, or more correctly, the weights of ‘positive feeling’ (ratings 4 and 5) contributed by each of the 16 elements. Each element is going to contribute to the positive feeling when that element is present in the vignette, or perhaps take away from the positive feeling.. The real question is ‘how much weight or how big is the contribution’.

OLS uses the regression model to create the simple equation: TOP2 = k0 + k1(A1) + k2(A2)…k16(D4).

We interpret the model as follows:

Additive constant (k0) is the estimated percent of ratings of 5 and 4 (TOP2) in the absence of elements. Of course, the experimental design ensures that each respondent will evaluate vignettes with a minimum of two elements and a maximum of four elements. There is never a vignette actually experienced with no elements. Yet, the OLS regression estimates that value. The additive constant ends up being a ‘baseline’ value, the underlying likelihood of a TOP2 rating. The additive constant is high when most of the vignettes are rated 4 or 5, not 1 or 2 or 3. The additive constant is low when most of the vignettes are rated 1 or 2 or 3.

The coefficients k1-k16 show us the estimated percent of positive ratings (TOP2) when the element is incorporated into the vignette. Statisticians use inferential statistics to study the statistical significance of the coefficients. Typical standard errors of the coefficients are around 4-5 for base sizes of 100 respondents.

Mind Genomics returns with a great deal of data, almost a wall of numbers, such as that shown in Table 3. To allow the patterns to emerge we blank out all coefficients of +1 or lower and highlight through shading coefficients of +7 or higher.

Table 3 shows us high additive constants for all respondents except those who define themselves as having used PayPal once or twice. The 8 respondents generate an additive constant of 21, quite different from the high additive constants for the regular users.

Table 3 further shows a great number of positive coefficients, as well as very strong performing elements. Our goal here is not to describe the underlying rationales of what might be occurring, but rather in the spirit of an applied effort with limit budget and short time frames identify ‘what to do.’ The science exists and can be developed at one’s leisure.

Table 3: Parameters of the models for Total Panel and for panelist who identify themselves by gender, age, and experience/attitude regard PayPal.

tab 3

Step 9: Create Individual Level Models and Use Clustering to Discover Mind-sets

A hallmark analysis of Mind Genomics is to cluster the respondents on the basis of the pattern of their 16 element coefficients, in order to discover new to the world mind-sets, viz., patterns of reactions to the different elements. Underlying this strategy of clustering is the worldview of Mind-Genomics that it is the pattern of responses to the activities of the everyday which teach us a great deal.

A word of explanation is in order here. Researchers accept the fact that people differ from each other, and that the nature of these differences is important to understand, for either basic science of human behavior., or for applications. The conventional methods of dividing people fall into at least three different classes, namely WHO the person is, what the person THINKS/BELIEVES, and finally what the person DOES, viz., how the person behaves. These divisions are not considered to be hard and fast, but rather simple heuristics to divide people into meaningful groups. The studies leading to these groups in, these clusters, are generally large, expensive, and work at the higher level of abstraction. That is, the focus is on how people think in general about a topic. The topic of these ways of understanding people has been written about many times, in popular books, but also in scientific tomes [11-13].

A key problem of conventional division of people into the large groups is how to apply this group information to the world of the specific, granular, every day. Faced with a real-world problem, such as our PayPal issues, can we use these large-scale studies to illuminate the issue with what to do with PayPal. In other words, what are these issues when the topic is the whole world, but rather the quotidian, daily efforts of people in the world of ‘PayPal.

The Mind Genomics approach to the problem of individual differences is to work at the level of the granular, finding groups of respondents who show different patterns of responses to the same test stimuli, with these patterns of responses being both parsimonious (the fewer the better) and interpretable (the patterns must make sense). Generally, as the researcher extracts more groups of smaller size from the population the groups are increasingly interpretable, but at the same time the effort ends up with many groups, often too many to use in any application.

The approach used by Mind Genomics ends up being very simple, but often such simplicity generates powerful, actionable results. The researcher follows these steps:

  • Generate a model, viz., equation, for each individual respondent, following the same form as the equation for the total panel and each subgroup. It will be straightforward to create this model for each respondent because the vignettes, test combinations evaluated by the respondent, were created to follow an experimental deign at the level of the individual respondent. Furthermore, even when the respondent rates every one of the 24 vignettes similarly (e.g.,, ll rated 5 or 4, transformed to 100 for TOP2), the vanishingly small random number added to eh transformed value of TOP2 ends up ensuring sufficient variation I the dependent variable, in turn preventing the regression program from crashing.
  • The regression generates 100 models or equation one for each respondent, with 17 parameters (additive constant, 16 coefficients)
  • Using only the 16 coefficients, compute a correlate coefficient between each pair of respondents. The correlation coefficient measures how ‘linearly related’ are two individuals, based upon the measures of the 16 correlations. This is called the Pearson R, which varies from a high of +1 when the 16 pairs of coefficients line up perfectly, to a low of -1 when the 16 pairs of coefficients are perfectly but inversely related to each other.
  • Create a measure of ‘dissimilarity’ or ‘distance’, defined here as (1-Pearson R). The quantity (1-Pearson R) is one of many distance measures that could be used. (1-Pearson R) varies from of a low of 0 when two set of 16 coefficients correlate perfectly (1-R) becomes 0 because for perfect linear correlation R =1. In contrast, when two sets of 16 coefficients move in precise opposite direction (1-R) becomes 2 because R= -1
  • The k-means regression program [14] attempts to classify the respondent, first into two groups (clusters, mind-sets,) and then into three groups, using strictly mathematical criteria. The solution is approximately. The program does not use the meanings of the elements as an aid.
  • It remains the job of the researcher to choose the number of clusters and then to name the clusters. In keep with the orientation of Mind Genomics, namely, to find out how people think, the clusters emerging from the k-means clustering exercise are named Mind-Sets.
  • Once each respondent has been assigned by the clustering program to only one of two emergent mind-sets, or one of three emergent mind-sets, the researcher ca easily rerun the regression models, two times for the two mind-sets (once per mind-set) or three times for the three mind-sets, respectively.
  • Table 4 shows the data array in the form to which we have become accustomed. The rows are the elements, the columns are the respondents. The top of Table 4 (Table 4A) shows the results from the two mind-set-clustering. The bottom of Table 4 (Table 4B) shows the results from the three mind-set-clustering. As before, only positive coefficients are show. Negative coefficients and coefficients of 0 and 1 are also omitted. The stronger coefficients of 7 or higher are shown in shaded cells.
  • Table 4 shows the elements with positive coefficients and the strong performing elements. The names of the mind-sets are used as a mnemonic. The reality is that the respondents are identified by mind-sets for convenience only. It is the content of the message which is important/.

    Table 4: Parameters of the models for Total Panel and for panelist who identify themselves by gender, age, and experience/attitude regard PayPal.

    tab 4a

    tab 4b

    Step 10: How Well Did We Do, the Index of Divergent Thought (IDT)

    A continuing issue in research is the need to measure how ‘good’ the ideas are. Just because the researcher can quantify the ideas using experimental design and regression, the results can be useless. In consumer research one often hears about the quality of ‘insights’, and that it takes a seasoned professional to know what to do. The effort in consumer research and its sister disciplines such as sensory analysis is to follow a set of procedures, doing so meticulously. Yet, to reiterate, just how good are the results?

    S.S. Stevens, the aforementioned Professor of Psychophysics at Harvard University from the 1940’s to the early 1970’s, would often proclaim the truism that ‘validity is a matter of opinion.’ Stevens was actually ‘on to something.’ How does one know the validity of the data, the quality of insights.

    The notion of IDT, the Index of Divergent Thought, was created with the notion that ‘divergent’ is a qualitative number. Divergent means attractive to different groups, rather than divergent from 0. Low IDT values mean that the ideas are simply weak for people who think differently (viz., the mind-sets) High IDT values mean that the ideas are strong among people who think differently. The term ‘divergent’ refers to the nature of the ideas, the different that ideas can take.

    To answer this question, we present one bookkeeping approach shown in Table 5. The idea is to calculate the weighted sum of positive coefficients (1 or higher), based upon the results from the six clearly defined groups: Total, MS1 of 1, MS2 of 2, MS1 of 3, MS2 of 3, and MS3 of 3, respectively. Each group generates a sum of positive coefficients, emerging from the study. Each group has a defined base size from the study. The data in Table 4 suffice to create a weight sum of positive coefficients. The value of the IDT is 44. The IDT is only an indexed value. Other studies have shown IDT values both above and below. High IDT value corresponds to studies with high or even very high coefficients among a relatively sizeable subgroup in the study. These high coefficients belong to elements that respondents believe to be important, elements which should draw attention.

    Table 5: The Index of Divergent Thought (IDT)

    tab 5

    Step 11: Responses to the Open-ended Question

    Our final empirical section involves the open ends. Respondents were instructed to write about their feelings towards PayPal. Step 11 provides an edited version of the open ends, for those respondents who wrote a ‘reasonable’ answer. The open end response is accompanied by the respondent number, gender, age, Q1 (attitude about PayPal), and membership in one of the three mind-sets. Table 6 presents the open-ended responses. The open-ended questions are presented here as background to the analysis of open-ended questions by artificial intelligence, later on in Step xxxx.

    Table 6: Responses to the open-ended question

    tab 6(1)

    tab 6(2)

    tab 6(3)

    Bringing Generative AI into the World of Mind Genomics and Insights

    During the past year or two the idea of artificial intelligence as a critical aspect of intelligence gathering and insights development t seems to be at the tips of everyone’s tongue. From an esoteric approach wonderful to throw around at cocktail parties and business meetings to create an ‘image’, AI has burst on the scene to become a major player. Unlike some of the other hype technologies, ranging from Big Data to neuromarketing, AI seems to be able to deliver beyond its hype.

    As part of the evolution of Mind Genomics as a science and BimiLeap as a program, we have instituted artificial intelligence in the Idea Coach to provide ideas, questions, and answer. The approach works well, or at least seems to do when the task is to generate disparate questions and disparate answers to reasonably well formulated inputs, such as a specific description of a problem to generate questions, or a specific question to generate answer.

    The next step in the use of AI in Mind Genomics may be the interpretation of the winning element of defined subgroups. The elements tell what ideas rise to the topic, but don’t tell us a pattern. Can AI discern patterns, and report them without human guidance?

    The four final tables are more of a demonstration of the AI enhancements to Mind Genomics and placed in the appendix to this paper. It’s important to note that the BimiLeap software used by Mind Genomics instructs the AI using a defined set of pre-programmed templated prompts to learn about the mind-set segments, Total panel, subgroups, and questions and answers themselves generated by Idea Coach.. The prompts command the AI to write summaries that tell a story and aim for completeness in thinking. For example, the prompts ask for “what’s missing,” alternative points of view, and groups or audiences that might hold opposing views. In other words, the summarizer equips researchers not just with data interpretation but adds different perspectives and counterarguments that may be helpful in assessing their results, anticipating disagreements, or suggesting further research.

    Appendix 1 shows us the use of AI to understand the winning elements of each mind-set.

    Appendix 2 shows the use of AI to understand the open-end questions.

    Appendix 3 shows the use of AI to digest and summarize the output of Idea Coach during the creation of the 30 questions. Each separate query to generate 30 question using Idea Coach will produce its own page, to digest and to summarize that particular set of 30 questions generated by Idea Coach.

    Appendix 4 shows the use of AI to digest and summarize the 15 answers produced by Idea Coach for a single question.

    Appendices 3 and 4 show summaries by artificial intelligence of somewhat disconnected ideas, specifically ideas produced by a previous query to the artificial intelligence engine represent by Idea Coach.

    Discussion and Conclusions

    A Google Scholar® search of the combined terms ‘marketing research’ and ‘artificial intelligence’ generates 982,000 ‘hits’, most hits appearing during the past few years as the interest in artificial intelligence has exploded, and the potential applications have expanded due to the widespread availability of AI tools, such as Chat GPT4. A deeper look at these references shows that the term ‘marketing research’ really devolves down to marketing, not research. Indeed, it is hard to find good reference about the use of AI in marketing research as we know marketing research to be. A parallel can be drawn with the introduction of the ‘web’ into the world of the computer, and the interest, but not really ‘new’ applications for the capabilities of ‘on-line research’. There were issues about the ‘quality’ of data that would be obtained in this new and more rapid fashion, and many issues emerging about validating the interviews, but sadly, few truly new vistas emerging in market research. In both the emergence of the internet and the growth of artificial intelligence marketing research has focused primarily on data acquisition, rather than on vistas of a truly new nature [15-22].

    It is on the vision of ‘new’ to the world of marketing research, the world ‘new’ reserved for a new vision of what could be, not just simply a possibly threat of technology to the ‘best practices’ endorsed by the thought leads and the status quo. The focus of this paper has been on the use of a templated system to enhance insights and solutions about a problem, the specific problem here being the self-declared lack of information about solutions to a marketing problem. As the paper unfolds, however, it becomes increasingly clear that the paper moves away from the traditional approaches, best-practice, and wisdom of the consumer research and other insight-based communities, such as sensory evaluation in the world of food, cosmetics, and other consumer products. Rather, the paper moves towards a systemized approach which requires absolutely no knowledge about a topic, an approach easy to use even by school children as young as eight years old [23,24]. The focus is on a process which requires literally no expertise to master, a process which starts with questions and exports actionable answers. In other words, the vision of democratizing research, and liberating it from the bonds of best practices.

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Fractures after Initiation of a Drug Holiday in a Real- Life Setting

DOI: 10.31038/EDMJ.2023712

Abstract

Purpose: We aimed to assess the fracture rate in patients who were placed on a drug holiday (DH) after minimum adequate therapy versus those who continued therapy (CT) in a real-life setting.

Methods: This is a retrospective cohort study conducted in a tertiary academic center. Inclusion criteria involved osteoporotic adults who received minimum adequate bisphosphonate therapy (≥ 3 years), otherwise, patients were excluded.

Results: Of 1,814 charts randomly selected and reviewed, 272 patients met the inclusion criteria. In our cohort, females were 90.9%, White 50.0%, and African American 40.5%. A DH was initiated in 119 patients (43.8%). In the CT versus DH cohorts, the mean duration of therapy was 6.0 ± 2.6 versus 5.7 ± 2.3 years, total duration of follow-up 6.9 ± 2.9 versus 7.8 ± 2.7 years, and fractures occurred in 11.7% versus 9.2% respectively, not statistically different. The mean duration of follow-up after starting DH was 2.5 ± 1.9 years. Upon risk stratification using FRAX scoring, in the high-risk cohort, fragility fractures occurred in 16.5% (n=22/133) of the CT group versus 13.5% (n=7/52) of the DH cohort (P=0.66). In the lower risk cohort based on FRAX scoring, fragility fractures occurred in 7.1% (n=10/131) of the CT group versus 6.0% (n=4/63) of the DH cohort (P=1.0).

Conclusion: In our cohort, continued drug therapy did not provide additional fracture protective benefits beyond the minimum adequate duration of therapy. A drug holiday after three to five years of treatment may be considered after review of risk factors for future fracture.

Keywords

Osteoporosis, Fracture, Fragility fracture, Drug holiday, Continuous therapy

Introduction

Osteoporosis (OP) is a silent disease that may initially present as a fragility fracture with subsequent high morbidity, mortality, and healthcare financial burden [1]. Screening patients using fracture risk assessment modalities is suggested for case detection and institution of appropriate preventative therapeutics to prevent fragility fractures [2]. Fracture risk assessment modalities include DXA scanning (Dual-Energy X-ray Absorptiometry), online risk assessment tools such as the FRAX algorithm, and determining the presence of prevalent or incident fragility fractures [3]. High-risk patients are candidates for treatment while intermediate-risk patients may be monitored more frequently versus initiating a moderate intensity therapy like zoledronate 5 mg every other year [4].

Several classes of effective OP medications are available that significantly decrease the risk of initial and subsequent fragility fractures [5]. The pharmacological therapies for osteoporosis at the time of this analysis were broadly classified as antiresorptive therapies (bisphosphonates, denosumab, hormonal therapy, selective estrogen receptor modulators [SERM]) and osteoanabolic therapies (teriparatide, abaloparatide). Bisphosphonates (BP) include alendronate, risedronate, ibandronate, and zoledronate (FDA approved 1995, 2000, 2005, and 2007, respectively).

The concept of a drug holiday (DH) was introduced in 2008 after several reports of rare severe side effects including osteonecrosis of the jaw and atypical femur fracture [6]. Accurate fracture risk assessment is critical for appropriate risk stratification in a variety of clinical settings inclusive of whether a patient should be initially started on medical therapy, when to consider a DH, and continued surveillance every 1-2 years while on a DH to determine when reinstitution of pharmacological therapy will be necessary [7]. It is important to note that a DH is presently considered for only bisphosphonate therapy, it should not apply to other classes of therapy due to the rapid loss of bone mineral density (BMD) and increased fracture risk associated with their withdrawal [8].

In this study, we aimed to retrospectively assess incidence rates of fractures between patients on continuous osteoporosis treatment versus patients placed on a DH after minimum adequate therapy in a tertiary academic center.

Methods

This is a retrospective cohort study. Data were collected by chart review of patients who were followed at a tertiary academic center from October 2007 to September 2016 for treatment of osteoporosis.

Definitions

  • Osteoporotic fracture: a fracture caused by an injury that would be insufficient to fracture a normal bone as a result of reduced compressive and/or torsional strength of bone [9]. Typical fractures in patients with osteoporosis include vertebral (spine), proximal femur (hip), distal forearm (wrist), and proximal humerus [9,10]. Osteoporotic fractures may involve any bone except the hand (distal to carpal bones), foot (distal to ankle), face, and skull [11]. Osteoporotic fractures are also termed fragility fractures in this study.
  • DXA scan: Dual-energy X-ray Absorptiometry
  • Drug holiday: A period when treatment is stopped after a patient has been on continuous treatment. However, the term ‘holiday’ implies the temporary withdrawal of treatment that may be restarted in the future [12]
  • FRAX score: An online validated tool for fracture risk assessment (https://www.shef.ac.uk/FRAX/tool.jsp), FRAX web version 4.0 was utilized in this study.

Risk Stratification

Risk Assessment was based on the recommendations of the National Osteoporosis Foundation (NOF) (USA) [13-16]:

  • High risk:
  • FRAX Score: Major Osteoporotic Fracture [MOF] risk ≥20% and/or Hip Fracture [HF] risk ≥3%
  • DXA scan findings: T-score ≤ -2.5 SD at the lumbar spine, femur neck, or total hip
  • Presence of a fragility fracture (sites as previously mentioned in the protocol)
  • Only one positive high risk categorical finding is enough to be classified as “high-risk”.
  • Intermediate risk:
  • FRAX Score: MOF risk 10-19% and/or HF risk 1.5-2.9%
  • Low risk:
  • FRAX Score: MOF risk <10% and/or HF risk <1.5%
  • Lower risk:
  • Patients in the low or intermediate-risk categories to facilitate their combined risk as compared to high-risk patients.

Medications for the treatment of osteoporosis [16]:

Bisphosphonates: Alendronate, ibandronate, risedronate, and zoledronate.

Inclusion and Exclusion Criteria

Inclusion criteria consisted of adults aged ≥18-year-old with a history of receiving continuous bisphosphonate therapy (alendronate, ibandronate, risedronate, zoledronate) for treatment of osteoporosis or patients at high risk of fracture. Continuous therapy was defined as a minimum of three years of continuous bisphosphonates therapy.

Exclusion criteria included adult patients who received a shorter duration of bisphosphonates therapy for treatment of osteoporosis or receiving other osteoporosis treatment medications solely; receiving treatment to manage hypercalcemia or osteolytic lesions related to malignancy or other medical conditions. Institutional Review Board (IRB) approval was obtained.

Aim, Data, and Analysis

We aimed to assess fracture incidence rates between patients on continuous treatment (CT) for osteoporosis versus patients placed on a drug holiday (DH) after minimum adequate therapy.

Descriptive analysis was performed to assess baseline patient demographics, clinical characteristics, duration of therapy, and fracture rates. Categorical variables were presented as frequencies, proportions, and percentages. Continuous variables were presented as means ± standard deviations. The χ2 test was used for the analysis of categorical variables and the t-test was used for continuous variables. Fracture-free survival analysis was performed using the Kaplan-Meier method, comparison and assessment of statistical difference were performed using Mantel-Cox analysis. SPSS (Statistical Package for the Social Sciences) ≥23 was used for all the statistical analyses.

To compare data between different proportions or means, a fixed-effects statistical model for meta-analysis was used [17]. The means, SD, and proportions were weighted based on the sample sizes of the different cohorts in each study. The confidence intervals for the variables were calculated for the p values of 0.05, 0.01, and 0.001. These were compared with corresponding means and proportions in the other cohort to determine the statistical significance [17].

Results

A total of 12,885 patients were identified based on the presence of at least one prescription for an osteoporosis medication in the electronic medical records from 2007 to 2016. The research group reviewed 1,814 randomly selected charts and 272 patients met the inclusion criteria as shown in Figure 1. The mean age of the cohort ( ± standard deviation) was 68.8 ± 10.7 years, females accounted for 90.9%. Most of the patients were Caucasian (50.0%) and African American (40.5%). A Drug holiday was initiated in 119 (43.8) patients. Table 1 summarizes the baseline clinical characteristics of the cohort and the medical specialty of the treating providers. Table 1 discloses the prevalence of comorbidities and risk factors for osteoporosis and fragility fractures in our cohort.

fig 1

Figure 1: Consort Table

Table 1: Clinical characteristics, and comorbidities and risk factors for osteoporosis and fractures

Age (years, mean ± SD)

68.8 ± 10.7

Female gender [n (%)]

248 (91.2%)

Race [n (%)]
Caucasian

135 (49.6%)

African American

111 (40.8%)

Hispanic

22 (8.1%)

Asian

3 (1.1%)

Unknown

1 (0.4%)

Treating Provider
PCP (IM)

132 (48.5%)

Rheumatologist

62 (22.6%)

PCP (FM)

34 (12.5%)

Endocrinology

19 (6.9%)

PCP (Geriatrics)

12 (4.4%)

Others

8 (2.9%)

Oncology

5 (1.8%)

Comorbidities and risk factors for osteoporosis and fractures
Falls

146 (53.7%)

Smoking

90 (33.1%)

Glucocorticoids

59 (21.7%)

Prednisone (or equivalent) ≥7.5mg

32/59 (54.2%)

Diabetes mellitus

53 (19.5%)

Rheumatoid arthritis

28 (10.3%)

Parent fractured hip

10 (3.7%)

Premature menopause

8 (2.9%)

Liver disease

7 (2.6%)

Hyperthyroidism

6 (2.2%)

Hypogonadism

4 (1.5%)

Alcohol abuse

1 (0.4%)

Osteogenesis imperfect

0 (0%)

Abbreviations: SD=Standard Deviation; DH=Drug Holiday; PCP=Primary Care Physician; IM=Internal Medicine; FM=Family Medicine.

The entire cohort received continued therapy beyond the minimum of three years and therefore, all the patients (n=272) were included in the analysis for the continued therapy (CT) group while they were receiving uninterrupted treatment. The mean duration of therapy in the CT group was 6.0 ± 2.6 years. A total of 119 patients were placed on a DH after a mean duration of prior bisphosphonate therapy of 5.7 ± 2.3 years. Any fragility fractures that occurred while receiving therapy and before initiating their DH were analyzed as being in the CT group. Fragility fractures that were present before starting anti-osteoporosis therapy were documented in 82 (29.9%) patients but were not included as occurring during therapy in the CT group. In the CT group, fragility fractures occurring during the initial three years of therapy were noted in 30/272 (11.0%) patients, as observed in Table 2 (fragility fractures within the first 3 years of therapy). These fractures were included in the calculation of each patient’s FRAX fracture risk assessment at the time of institution of their DH but were not considered a failure of therapy since these patients had not completed the predefined minimum adequate therapy of three years. A total of 159 patients received 5 or more years of continuous therapy.

Table 2: Continued therapy and drug holiday

 

Continued Therapy

Drug Holiday

P-Value

Number of patients

272

119

Duration of therapy (y; mean ± SD)

6.0 ± 2.6

5.7 ± 2.3

P>0.05

Follow-up duration (y; mean ± SD)

6.9 ± 2.9

7.8 ± 2.7

P=0.05

Fragility fractures within the first 3 years of therapy (%; n)
Total

11.0% (30/272)

13.4% (16/119)

P>0.05

During 1st year of Rx

3.3% (9/272)

3.4% (4/119)

P>0.05

During 2nd year of Rx

2.2% (6/272)

4.2% (5/119)

P>0.05

During 3rd year of Rx

5.5% (15/272)

5.9% (7/119)

P>0.05

Fragility fractures beyond the first 3 years of therapy (%; n)
Total

11.7% (32)

9.2% (11)

P>0.05

3-4.9y of therapy

6.3% (17/272)

14.0% (8/57)

P>0.05

≥5y of therapy

9.4% (15/159)

4.8% (3/62)

P>0.05

Fragility Fractures in the Continued Therapy versus Drug Holiday Cohorts

In the CT versus DH cohorts, mean duration of therapy was 6.0 ± 2.6 versus 5.7 ± 2.3 years (p>0.05) and total duration of follow-up was 6.9 ± 2.9 in CT group versus 7.8 ± 2.7 years in DH group (P=0.05). The mean duration of follow-up after starting DH was 2.5 ± 1.9 years. The mean duration of follow-up until the occurrence of first fracture (after a minimum of three years of therapy) or last follow-up if there were no fractures was 2.6 ± 2.3 years for the CT group and 2.3 ± 1.8 years for the DH group. As observed in Table 2, the total number of fragility fractures during the entire study period were 32/272 (11.8%) of the CT group versus 11/119 (9.2%) of the DH cohort (P=0.60). A total of 272 patients continued to receive therapy beyond three years and 159 patients received therapy for ≥5 years. Fragility fractures occurred in 17/272 (6.3%) patients on CT for 3-4.9 years and in 15/159 (9.4%) patients on CT for 5 or more years (p>0.05). Fragility fractures after initiation of a DH occurred in 8/57 (14.0%) patients who completed 3-4.9 years of prior bisphosphonate therapy and 3/62 (4.8%) patients who received ≥5 prior treatment, P>0.05. The mean duration for the occurrence of the first fragility fracture was 2.3 ± 2.7 years in the CT cohort versus 1.5 ± 1.2 years in the DH cohort (P<0.01). The fracture-free survival analysis for the whole cohort using Kaplan Meier analysis revealed no significant difference in fracture rates between the CT and DH groups (P = 0.74) as shown in Figure 2.

fig 2(1)

fig 2(2)

fig 2(3)

Figure 2: Fracture-free survival using Kaplan-Meier analysis.
Analysis for the whole cohort using continued therapy after a minimum of 3 years is presented in figure A. Analysis for the whole cohort using continued therapy after a minimum of 5 years is presented in figure B. Analysis based on risk assessment using FRAX scoring are presented in figures C (high risk with continued therapy ≥3 y), D (high risk with continued therapy ≥5 y), E (lower risk with continued therapy ≥3 y), and F (lower risk with continued therapy ≥5 y). High risk patients based on FRAX score were defined as having major osteoporotic fracture risk ≥20% and/or hip fracture risk ≥3. Censored data refers to incomplete data for patients like those who lost follow up or deceased before experiencing the primary outcome (fractures) and who could have otherwise experienced it if continued followed up [2]. Abbreviations: CT=Continued Therapy; DH=Drug Holiday; FRAX=An Online Fracture Risk Assessment Tool.

Fragility Fractures Using Different Risk Assessment Tools

Fragility fractures in the high risk versus lower-risk patients in the CT cohort based on FRAX high risk (HR) were 16.5% versus 7.1% (P=0.01). In the combined FRAX HR plus DXA HR groups, fragility fractures occurred in 13.2% of the high-risk group versus 9.0% in the lower-risk groups (P=0.20). Based on fragility fractures that occurred during the first three years of therapy, 0.0% in the higher risk group versus 13.2% in the lower risk group (P=0.02) respectively. Fragility fractures in the high-risk versus lower-risk patients in the DH cohort based on FRAX HR were 13.5% versus 6.0% (P=0.14). In the combined FRAX HR plus DXA HR group, fragility fractures occurred in 11.3% versus 6.3% in the lower risk group (P=0.28). Based on the presence of fragility fractures during the first three years of therapy, 14.3% were in the high risk versus 8.2% in the lower risk group (P=0.30).

Fragility Fractures in FRAX High Risk versus Lower Risk Patients and Drug Holiday

In the FRAX high-risk group of 133 patients, 87/133 (65.4%) continued therapy whereas 46/133 (34.6%) were placed on a drug holiday. For the high-risk cohort, the mean duration of follow-up until the time of the first fracture or last follow-up if no fractures occurred ( ± standard deviation) was 2.4 ± 2.0 years for the CT group (after 3 years of minimum therapy), and 2.1 ± 1.8 years for the DH group. Among the high-risk patients at initial FRAX risk stratification, fragility fractures occurred in 22/133 (16.5%) of the CT group versus 7/52 (13.5%) of the DH cohort (P=0.66). The mean duration for the occurrence of the first fragility fracture was 2.5 ± 3.1 years in the CT cohort after minimum adequate therapy versus 1.4 ± 1.4 years in the DH cohort.

In the 141 lower-risk patients, 68 (48.2%) continued therapy and 73 (51.8%) were placed on a DH. For the lower risk cohort, the mean duration of follow-up until the time of first fracture or last follow-up if no fractures occurred ( ± standard deviation) was 2.8 ± 2.6 years for the CT group (after 3 years of minimum therapy), and 2.4 ± 1.8 years for the DH group. Among the lower risk patients at initial FRAX risk stratification, fragility fractures occurred in 10/141 (7.1%) of the CT group versus 4/67 (6.0%) of the DH cohort (P=1.0). The mean duration for the occurrence of the first fragility fracture was 2.0 ± 1.9 years in the CT cohort after 3 years of therapy versus 1.6 ± 0.8 years in the DH cohort. The fracture-free survival analysis for the high-risk and low-risk cohorts using Kaplan Meier analysis revealed no significant difference in the fracture rates between the CT and DH groups (P = 0.87 and 0.88 respectively) as shown in Figure 2.

Five Years of Therapy

The rate of fractures was also assessed for patients who continued therapy for a minimum of five years. In FRAX high-risk patients on CT for 3-4.9 years versus ≥5 years of minimum therapy, fractures occurred in 11/63 (17.5%) versus 11/70 (15.7%) patients, respectively, P=0.88. In FRAX lower-risk patients on CT for 3-4.9 years versus ≥5 years of minimum therapy, fractures occurred in 6/52 (11.5%) versus 4/89 (4.5%) patients, respectively, P=0.34. Among the entire cohort, 159/272 patients continued therapy ≥5 years (mean 7.1 ± 2.5y) while the mean duration of therapy for the DH cohort (n=119) was 5.7 ± 2.3 years. Fracture rates were comparable between both groups as shown in Figure 2, P=0.61.

Discussion

The duration of osteoporosis therapy and when institution of a drug holiday should be considered is an under-researched area. There are differences in guidance regarding a DH among the osteoporosis-related societies [7,18-23]. At present, there are few prospective clinical trials or retrospective studies available to assess the risk of fracture while on continuous osteoporosis pharmacological therapy versus a drug holiday. The present consensus states that high-risk patients should continue therapy for no less than 5 years. Our goal was to assess the pattern of osteoporosis pharmacological treatment and fracture rates in a real-life setting in patients on continued therapy (CT) and a drug holiday (DH). Patients in the CT and DH subgroups were further stratified by FRAX scoring into high risk versus lower risk categories.

The first clinical trial to prospectively assess the concept of DH was the FLEX trial comparing continuing alendronate for a total of 10 years versus a DH after 5 years of therapy [24]. DH did not increase the risk of non-vertebral fractures or x-ray-detected vertebral fractures over the 5 years of follow-up, but the risk of clinically diagnosed vertebral fractures was significantly lower among CT 2.4% (16/662) versus DH cohort 5.3% (n=23/437); relative risk 0.45; 95% confidence interval 0.24–0.85). However, post hoc analysis of this data disclosed increased risk of fractures in the DH group was associated with lower baseline BMD and increased number of fractures prior to starting therapy. Significant limitations of the FLEX trial included the lack of assessing shorter duration of therapy (namely three years of treatment) and inability to utilize fracture risk assessment tools such as FRAX scoring (2008) [24]. The second clinical trial to prospectively assess CT versus DH was the Zoledronate HORIZON-Pivotal Fracture Trial, 3 years of therapy (Z3) versus placebo (P3) [25]. Two subsequent extension trials assessed CT versus DH, 6 years of CT (Z6) versus 3 years of therapy followed by a DH (Z3P3), followed by 9 years of CT (Z9) versus 6 years of therapy followed by a DH (Z6P3) [26,27]. In the first extension trial (Z6 versus Z3P3), there was no significant difference in non-vertebral or hip fractures, although patients who continued therapy had a lower rate of new vertebral fractures: 3.0% versus 6.2% (Odds ratio 0.51, 95% confidence interval [0.26, 0.95], P 0.035), and >60% of the patients in each cohort were at high risk of fractures. In the second extension trial (Z9 versus Z6P3), there was no significant difference in fracture rates between CT versus DH. Limitations of the HORIZON extension trials included the lack of risk stratification at either baseline or at the time of starting a drug holiday using a well-validated fracture risk assessment tool. In the study assessing Risedronate in osteoporosis, there was no DH comparator group but rather a comparison of 7 years of CT versus 5 years of placebo followed by 2 years of therapy [28]. DH after the use of denosumab was found to be associated with a rebound rapid increase in bone remodeling rates and a high risk of vertebral fragility fractures [29]. DH after teriparatide therapy is associated with loss of accrued bone mass and loss of the fracture protective effect of the drug and as such the general recommendation has been to follow osteoanabolic therapy with an antiresorptive therapy [19]. It should be noted that all the recommendations regarding drug holidays are primarily based on the alendronate and zoledronate clinical trials with the noted limitations of the trials and reliance on expert opinion.

Based on the previously mentioned two prospective placebo-controlled trials and their extensions, the general recommendation has been to consider a DH after 5 years of oral bisphosphonate therapy (FLEX Trial for alendronate) and 3 years for intravenous zoledronate. In our study, the statistical analysis was performed after a minimum of 3 years of therapy that is comparable to all bisphosphonate registration trials with assessment of fracture rates in both CT and DH groups. We performed our preliminary analysis after collecting data for 272 patients with the aim of re-estimating the power analysis and the number of patients to be reviewed afterward. Unexpectedly, the rate of fractures was not statistically different in CT as compared to DH. The absolute rate of fractures was numerically higher in the CT group, and therefore the study was terminated at that point.

In our study, the rate of fractures was assessed for the entire cohort and FRAX high and lower-risk patients. Comparison of the rate of fractures between CT and DH cohorts in each of these groups was performed at treatment thresholds of ≥3 and ≥5 years. The cut-off of 3 years was suggested to assess the efficacy after the use of 3 years of oral bisphosphonate therapy. The mean duration of therapy in patients who were placed on a drug holiday was 5.7 ± 2.3 years, and as most of the initial therapy was oral bisphosphonates, followed the general recommended guidelines of 5 years of oral therapy. Therefore, analysis of CT ≥5 years was necessary as well to avoid the bias of under-treatment using the cut-off of 3 years and having a falsely higher number of fractures in the CT cohort. Among the 6 comparison studies as shown in Figure 2, there was no significant difference between the patients on CT versus DH, independent of the risk status (high or lower FRAX risk) or duration of therapy (≥3 or ≥5 years).

The position statements of the American Society of Bone and Mineral Research, International Osteoporosis Foundation (IOF), AACE/ACE, FDA commentary, and the Endocrine Society guidelines agree that initial therapy with oral bisphosphonates of 5 years or 3 years of intravenous zoledronate should be considered standard of care [7,18-23]. In high-risk patients, these guidelines suggest the continuation of therapy with some advocating at least 10 years of oral therapy and 6 years of intravenous zoledronate. In low to intermediate-risk patients, it was suggested that clinicians consider a DH with frequent risk assessment every 2-4 years [7,18]. Although it is a reasonable approach to consider continuation of therapy and avoidance of fragility fractures in high fracture risk patients started on a premature DH after less than 5 years of therapy, there is little objective evidence to confirm this position. In our study, the rate of fractures was comparable between the high-risk patients who continued therapy as compared to those who were placed on a DH.

Our study is the first retrospective cohort study to perform an in-depth fracture risk assessment based on calculating FRAX scores and assessing fracture rates in different risk strata. The pre-treatment fracture rate in our cohort was 29.9% consistent with the inclusion of a significant number of high-risk patients comparable to several prospective studies and as such avoiding under-powering of the study. The mean duration of therapy, as well as post-drug holiday follow-up, is reasonable considering the introduction of electronic health records in 2007. There are some limitations of this study including the retrospective nature of the study, small number of patients, and relatively short duration of follow-up. Patients who had their bone density scans or received treatment at outside facilities were not available in the EMR via notes/charts, per verbal discussion with the provider, or through use of cross-EMR observations (Care Everywhere®) reference. Most of our patients received alendronate without sufficient information available in the EMR regarding medication compliance. Bone remodeling markers were rarely checked. Lastly, no significant episodes of ONJ or AFF were observed although outside medical records were not always available, and assessment of these rare complications was limited.

Conclusion

In our cohort study, continued drug therapy beyond 3 years did not provide additional protective benefit as compared to a drug holiday in high-risk patients. Future studies with larger cohorts and a longer duration of follow-up are needed to validate these findings. Although not uniformly performed in all patients of our cohort, annual reassessment of the response to pharmacological therapy and fracture risk assessment should be performed. The present common use of the term “Drug Holiday” in osteoporosis management should be replaced and endorsed by all societies as “Bisphosphonate Drug Holiday”.

References

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Chronic Hepatitis B and Hepatocellular Carcinoma: Novel Therapeutic Concepts

DOI: 10.31038/IDT.2023411

Abstract

Hepatitis B virus (HBV) is a partially double-stranded hepatotropic DNA virus that currently infects about 4% of the population world-wide (ca. 296 million people) with the highest prevalence in Asia and Africa and more than half a million deaths annually. Clinically, HBV infection can be asymptomatic with normal or near normal aminotransferase levels or with elevated alanine aminotransferase levels, significant necroinflammation and eventually progression to advanced liver cirrhosis and hepatocellular carcinoma. Indications for treatment of chronic hepatitis B are HBV DNA levels >2000 IU per milliliter and liver cirrhosis. Different from the now available curative oral therapies of chronic hepatitis C by direct-acting antiviral agents (DAAs), to date there exists no curative therapeutic strategy for chronic hepatitis B. Therefore, multiple new investigational therapeutic antiviral concepts are currently explored.

Globally, HCC is the sixth most diagnosed cancer and the third leading cancer-related death in 2020. The management of HCC is complex and depends on the stage of the disease at the time of diagnosis. HCC is largely chemotherapy-resistant and no systemic treatments improved survival until recently. In the early 2000s HCC treatment was revolutionized by sorafenib, a modestly effective orally available tyrosine kinase inhibitor (TKI). In 2018 levantinib was also approved as first-line treatment, followed by several antiangiogenic agents, including among others regorafinib, ramucirumab, and cabozantinib as second-line treatments. Unfortunately, 5-year overall survival of advanced or metastatic disease is still <10%. Therefore, numerous clinical trials are ongoing, assessing immune checkpoint inhibitors (ICIs) in combination with each other or with targeted agents in the treatment of HCCs. Further, ICI incorporation into the treatment of very early-stage HCC by resection or ablation may lower recurrence rate or even cure these patients.

Abbreviations

CHB: Chronic Hepatitis B, HBV: Hepatitis B Virus, HCC: Hepatocellular Carcinoma, ICI: Immune Checkpoint Inhibitors, TKI: Tyrosine Kinase Inhibitor

Introduction

Hepatitis B is a major global public health problem. Hepatitis B virus (HBV) causes acute and chronic infection. The long-term consequences, i.e. liver cirrhosis and hepatocellular carcinoma (HCC) arising from chronic HBV infection carry a risk of premature death in 25% of individuals. The World Health Assembly adopted in 2016 the WHO Global Health Sector Strategy on Viral Hepatitis (WHO-GHSS) aiming at a 30% reduction of new hepatitis B infections and a 10% reduction of HBV-related deaths by 2020 and a 95% reduction of new HBV infections and a 6% reduction of HBV-related deaths by 2030, compared to the baseline year 2015 [1-4]. Vaccines, virus testing and antiviral therapies already exist to prevent HBV infection as well as HBV-related disease progression. While new cases of hepatitis B have been reduced by vaccination [1]. HBV-related deaths are expected to rise under the current pace of testing and the available treatment interventions. The same holds true for the early detection of advanced hepatocellular carcinoma and the medical treatment of advanced tumor stages.

In the following novel concepts for the medical treatment of chronic hepatitis B and of advanced HCC will be discussed.

Novel Antiviral Strategies against Chronic HBV Infection

HBV infects and replicates in hepatocytes after it binds to the cell surface via the pre-S glycoprotein and interacts with the hepatic bile acid transporter sodium taurocholate cotransporting polypeptide. The relaxed circular DNA genome is transported to the nucleus and converted to covalently closed circular DNA (cccDNA) that is transcribed into pregenomic RNA which serves as template for reverse transcription into HBV RNA and the translational template for the core protein and polymerase. After the partially double-stranded HBV DNA is enveloped, the virion is secreted or recycles back into the nucleus [2-4].

Therapy of chronic hepatitis B at present rests mostly on pegylated interferons alpha and nucleos(t)ide analogues, such as adefovir, entecavir, lamivudine, telbivudine, tenofovir disoproxil fumarate and tenofovir alafenamide. The nucleos(t)ide analogues result in a sustained viral suppression, improvement of ALT levels and ultimately in a decrease of liver cirrhosis and liver cancer [5,6]. However, even with clearance of serum HBV DNA and hepatitis B e antigen, HBsAg and cccDNA can persist, putting the patient at risk for relapse if therapy is stopped with a potentially severe or even fatal clinical course. To reduce the need for lifelong treatment, novel strategies are aimed at a functional or complete cure (Table 1). Numerous new anti-HBV compounds that are expected to fulfill these requirements have been or are presently evaluated in clinical studies [2-4].

Table 1: Therapeutic Antiviral Response

Liver cccDNA

Serum ALT

Serum HBV DNA

Serum HBsAg

Anti-HBs

Virologic + Variable

+

Biochemical + Normal

variable

+

Functional + * Normal

-/+

-/+

-/+

Cure – ** Normal

+

* Time-limited therapy, e.g. 1 yr
** Long-term therapy, yrs.

While none of the antivirals evaluated to date in clinical trials result in a functional or complete cure (Table 2), one can hope that innovative curative therapeutic concepts will be developed in the future. For the time being the major focus will be the worldwide implementation of HBV vaccination, the consequent clinical testing of individuals at risk and the antiviral treatment of those already infected. Given the seminal development of effective drugs against chronic hepatitis C [7], it is hoped that a similar success will eventually eradicate HBV infections and its associated morbidity and mortality.

Table 2: HBV antivirals in clinical studies

Drug

Mode of Action

Myrcludex B Entry inhibitor
Nitazoanide HBx target
CRV-431
GSK 3228836 RNA degradation
JNJ-3989
AB-729 RNAi
ALN-HBV (VIR-2218) RNA degradation
   
Vebicorvir Capsid Assembly Modulator
ABI-H3733
ABI-4334
Morphodiadin
JNJ-6379
EDP-514
RG7907
QL-007
ALGH-000184
AB-836
VNRX-9945
O7049839
RG7336 iRNA agent
JNJ-3989 “ [15]
AB7-29-001
VIR-22198
ALG-125755
Bepirovirsen Antisense oligo [16,17,18]
   
ALG-020572-401
Nivolumab Anti-PD-1
Cemiplimab

Novel Therapeutic Strategies for Advanced Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, the sixth most frequent cancer and the third leading cause of cancer-related death worldwide [8]. HCC is an aggressive tumor that usually occurs in the setting of chronic liver diseases and cirrhosis [9]. A widely accepted treatment algorithm has been proposed by the Barcelona Clinic [10]. Depending on the stage of the HCC, treatment options are divided into surgical (resection, cryoablation, liver transplantation) and liver-directed non-surgical procedures (percutaneous ethanol or acetic acid injection, radiofrequency/microwave ablation, transarterial embolization, external beam radiation) and systemic treatment modalities (chemotherapy, molecularly targeted therapy and immunotherapy with immune checkpoint inhibitors (ICIs) [10].

Systemic treatment approaches for patients with advanced, unresectable HCCs in most cases are inappropriate for surgical or liver-directed non-surgical interventions, due the patient’s limited hepatic reserve. Unfortunately, in clinical practice >20% of HCCs are detected late, at already advanced stages. Further, HCCs are relatively chemotherapy-refractory tumors.

With a better understanding of the pathophysiology of HCCs, its hypervascularity and vascular abnormalities, the role of proangiogenic factors such as VEGF was identified in the early 2000s. With the development of the small molecule sorafenib, blocking the VEGFR, PDGFR, cRAF1, B-Raf, as orally available tyrosine kinase inhibitors (TKIs) or humanized monoclonal antibodies bevacizumab, cetuximab, e.g., VEGF, EGF, into clinical practice [11,12] this strategy gained momentum]. While the single-agent anti-programmed cell death (anti-PD-1) ICIs resulted in a modest response, the combination of atezolizumab (an anti-PFD-L1 ICI) with bevacizumab (an anti-VEGF antibody) was approved as first-line therapy in 2020. It showed a significant improvement in response rate, progression free survival and overall survival compared to sorafenib, the previous standard of care. This study established the combination of the antibody anti-PD-L1 atezolizumab with the VEGF-Inhibitor bevacizumab as first-line therapy for the advanced HCC [S]. While pembrolizumab and nivolumab were conditionally approved, a decision whether to keep or withdraw the approval is still pending [13, 14].

Despite these promising results of the combination of atezolizumab and bevacizumab for advanced HCC, several issues need to be carefully considered, especially the hepatic reserve and possibly the cause of liver disease. Further, a word of caution is in order, regarding the efficacy of multiple combination therapies. A recent study evaluating the combination of siRNA (JNJ-3989) with or without a CpAM (JNJ-6379) had the lowest rate of response compared with the 2 siRNA plus NA for comparison. This raises the possibility of an interaction between CpAM and siRNA and suggests that not all combinations will result in synergy.

Discussion and Conclusion

Chronic HBV infection results in chronic hepatitis with a life-time risk for progression to cirrhosis and HCC. Consequently, life-long monitoring is required to detect disease progression and surveillance is recommended to identify individuals at increased risk for HCC development. Current therapeutic options against chronic hepatitis B improve clinical outcome, but are not curative because they have no effect on cccDNA and integrated HBV DNA. While to date, none of the numerous therapeutic options (Table 2) have resulted in a functional or curative response. Given the global burden of disease there is an urgent need for more effective therapies, increased efforts to identify the patients already infected and to expand the vaccination programs with the aim to eliminate HBV infection worldwide.

With respect to the dismal prognosis of patients with advanced HCC at the time of diagnosis numerous clinical trials are assessing ICIs in combination with each other and with targeted agents. At the same time, major efforts are directed at the earlier detection of HCCs that are amenable to surgical and non-surgical liver-directed therapeutic strategies.

Conflict of Interest

No financial interest or conflict of interest exists.

References

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The Decision-Making Process for Percutaneous Endoscopic Gastrostomy in People with Amyotrophic Lateral Sclerosis

DOI: 10.31038/ASMHS.2023714

Abstract

Percutaneous endoscopic gastrostomy (PEG) insertion is recommended for people with amyotrophic lateral sclerosis (PALS) who are experiencing dysphagia resulting in diminished food and oral intake. Unintended weight loss and malnutrition are negative prognostic factors in PALS. Insertion of a PEG tube provides reliable access for nutrition, hydration, and medication, and can diminish the risk for aspiration pneumonia, choking, weight loss and fatigue. PEG use can significantly increase survival time for PALS; however, less than half of PALS who meet the criteria for PEG tube placement undergo the procedure. The factors influencing PALS in making this decision have not been extensively explored. This qualitative case study investigated the decision-making process in accepting a PEG as an alternative means of feeding. A purposive sample of 5 participants utilizing a PEG tube was recruited. Data was collected using in-depth semi structured interviews consisting of open-ended questions. Interviews were completed face to face through Zoom, a virtual platform. A thematic analysis was conducted to understand the unified subjective experiences of the participants. The analysis revealed four themes: (1) Survival; (2) Scary and Anxiety Provoking Process; (3) Wanted to Live Longer; and (4) Not Alone in My Decision. Conclusions: The decision-making process for PALs is highly emotive and challenging. Lack of appropriate education and comprehensive discussions with health care providers were negative factors that influenced the decision making process. Social supports and the will to live were positive factors that facilitated autonomous decision-making and eased the angst in PALS during this very difficult process.

Keywords

Amyotrophic lateral sclerosis, Percutaneous endoscopic gastrostomy, Decision-Making, Feeding tube, Dysphagia

Introduction

Amyotrophic lateral sclerosis (ALS) is an uncurable, progressive, fatal neurodegenerative disorder that destroys motor neurons in the nervous system. Motor neurons are key in the transmission of impulses from the spinal cord to skeletal muscles, as they enable individuals to have direct control of all muscle movements. As such, this neurodegenerative disease ultimately leads to progressive muscle weakness and loss of voluntary muscle control [1]. ALS has an incidence of 5.2 per 100,000 people in the United States [2]. The average life expectancy for patients with this disease is 2-5 years post diagnosis, with most deaths resulting from respiratory failure, often precipitated by pneumonia [3]. Several subtypes of ALS exist, with the most common resulting in limb onset (70%) and bulbar onset (25%) [4]. Both subtypes are characterized by upper motor neuron (UMN) symptoms, including hyperreflexia, spasticity, and bradykinesia, and lower motor neuron (LMN) symptoms, including fasciculations, muscle weakness, and atrophy. Despite the decreased mobility of people with ALS (PALS), the metabolic demands increase secondary to continuous muscle spasms and fasciculations [5]. The decreased nutritional intake due to difficulty swallowing results in negative caloric balance, further exacerbating muscle wasting, reducing body mass index (BMI), and worsening functional status. Furthermore, weakness and atrophy of the tongue and muscles of mastication contribute to fatigue in chewing and increase the time required for feeding. Other serious complications such as aspiration pneumonia and acute episodes of choking, either of which can be life threatening, have also been observed. Similarly, progressive dysphagia diminishes patients’ respiratory reserve, which will become a crucial factor in recommending and evaluating for further intervention [4].

Although the progression of symptoms and the areas of the body affected vary by subtype, approximately 85% of all PALS develop dysphagia [6], or difficulty swallowing, over the course of the disease. PALS with bulbar onset have a greater incidence of dysphagia early in the progression of the disease, whereas those with spinal onset develop dysphagia in the late stages [7]. Statistics reflect that dysphagia in those with bulbar onset increased from an initial incidence of 95% to 98% and those with spinal onset from 35% to 73% over a 2-year period [8]. Depending on the severity and onset of dysphagia, many PALS need additional support for receiving proper nourishment. Negative prognostic factors in PALS includes weight loss and malnutrition. Guidelines for intervention for dysphagia include placement of an enteral gastrostomy tube [1]. There are different enteral tube procedures utilized for tube placement, with the two most common procedures being the percutaneous endoscopic gastrostomy (PEG) and the Radiologically Inserted Gastrostomy (RIG). The RIG is less desirable than PEG because it has been associated with increased rates of dislodgement, tube blockages, and infections [9,10]. Further, a meta-analysis [11], studied the technical success rates, complication rates, and mortality rates between PEG and RIG resulting in the PEG having an increased success rate, with complications and mortality comparable after placement. Similarly, a prospective study [12], found mortality and complication rates comparable involving 50 patients with ALS who underwent a PEG or RIG procedure. Another meta-analysis evaluated postoperative complications, procedural success rate, and survival outcomes. In contrast, the PEG procedure was associated with less post-operative pain, but again had a lower success rate without any differences in survival [13]. An important advantage of the RIG procedure is that it does not require general anesthesia which lessens the possibility of respiratory complications, especially in patients with a reduced forced vital capacity (FVC) less than 50%, as measured by spirometry [12-14].

Furthermore, feeding tubes require specialized care and maintenance which oftentimes causes considerable burden on PALS and their caregivers leading to significant emotional impact and decreased quality of life. Although medically necessary, there is a limited amount of qualitative literature on the factors that influence the patients’ decision-making process to undergo such a procedure. Current studies [15,16], have shown that PEG tube acceptance in patients with ALS varies across countries and that patients are often reluctant to undergo this procedure [17].

In a review, Bradly [18], evaluated changes (in terms of ALS management) established in the 1999 American Academy of Neurology ALS Practice Parameters publication and reported that only 46% of patients were recommended for a PEG tube and of those only 43% received one. This amounts to an overall 20% PEG insertion rate. The timely implementation of a PEG tube is important, as there is a limited window of opportunity to receive this type of treatment. Without a PEG tube, PALs nutritional intake is compromised and thus negatively impacts health.

Initial studies have failed to demonstrate the benefit of enteral feeding in survival duration in the ALS population. However, more recent research has shown a trend toward a positive effect, especially in studies following the most recent guidelines and larger sample sizes [7]. A retrospective study combined with a meta-analysis demonstrated that enteral feeding increased survival duration irrespective of ALS subtype and stabilized BMI. Furthermore, analysis determined that enteral tube placement in patients with an FVC greater than 50% had a better survival duration than those with a FVC less than 50%. This trend was magnified in patients with a FVC greater than 60% [19]. Lastly, the American Academy of Neurology (AAN) recommends a FVC of below 50% as a threshold where complication rates are increased [20]. However, other studies have proposed higher FVC, such as 60% to 70%, to improve outcomes [19,21,22].

Currently, there are no established criteria to determine the optimal timing of a tube placement. This may result in a delay in recommendations and patients missing a window of opportunity for the most beneficial outcomes and maximal risk reduction. To assist a patient in the decision-making process, it is important to understand the factors that influence and motivate an individual in choosing a course of action. Thus, the purpose of this study was to explore the decision-making process that contributed to the placement of a PEG feeding tube in people with ALS. The study also sought to highlight the influences and experiences, while obtaining such an invasive alternate feeding device.

Methods and Materials

Study Design

This research used qualitative case study methodology based on thematic analysis to conduct an in-depth exploration of the phenomena of the decision-making process among PALS who opted for PEG tube insertion. The question “how do PALS describe their decision-making process in obtaining a PEG “feeding tube”?” guided this study. Secondary questions included “how do people with ALS describe the experience of obtaining a PEG feeding tube?” and “what were the influences that impacted the decision to accept a feeding tube?”.

The number of participants recruited for this study was based on previous qualitative studies. The literature suggests a small sample size, which enables a more in-depth perspective on the phenomena (decision-making process). Specifically, a purposive sample of 5 participants would offer a more in-depth perspective on the decision-making process of these individuals [23].

Approval from Hofstra University’s Institutional Review Board (IRB) was obtained (HUIRB Approval Ref#: 20220727-OT-HPHS-CIA-1) prior to recruitment of participants.

Participants

A purposive sampling was used to recruit PALS who use PEG tube feedings. Recruitment occurred via ALS care teams in multidisciplinary clinics located in various areas of the northeast USA. Members of care teams were asked to inform PALS with PEGs of our study. Those PALS who were interested were contacted by the first author. Participation was voluntary and informed consent was obtained from all participants. Confidentiality and anonymity were assured as well as the right to withdraw from the study at any time.

Five participants, 3 male and 2 female, were interviewed for this study. The mean age of the participants was 55.4 (range=36-75 years old). The mean time from diagnosis to PEG insertion was 5.6 years (range=2-11 years). All participants had a diagnosis of ALS, and all were using PEG tube for nutrition and hydration. All participants attend specialized multi-disciplinary clinics for ALS located in the northeast of the United States. None of the participants held any form of paid or volunteer employment, all resided with family, and all utilized a power wheelchair to meet their mobility needs (Table 1).

Table 1: Study participants demographics and related information

Participant

Information

1 Diagnosed with ALS in 2014; PEG inserted in 2016; ventilator dependent, uses assistive technology devices for augmentative and alternative communication. Lives with spouse and dependent child.
2 Diagnosed with ALS in 2017; PEG inserted in 2021. Lives with spouse and adult children.
3 Diagnosed with ALS in 2008; PEG inserted in 2017; ventilator dependent. Lives with adult child.
4 Diagnosed with ALS in 2010; PEG inserted in 2021. Lives with spouse and adult children.
5 Diagnosed with ALS in 2019; a PEG inserted in 2022; uses assistive technology devices for augmentative and alternative communication. Lives with parents.

Data Collection

Participants were interviewed between August and September of 2023 by one of the researchers (GC) using a semi-structured interview format. Semi-structured interviews addressed the aims of this research and facilitated a deep understanding of the decision-making process, which was further appreciated by encouraging a bidirectional dialogue between researcher and participant. This is an inherent strength of interviews over questionnaires [24].

All interviews were conducted face-to-face using the online platform Zoom. Interviews were video and audio recorded. Participants reaffirmed consent verbally prior to the interviews. All interviews were scheduled at a time of the participant’s choosing. Interview duration ranged from 55 to 89 minutes, as is typical for a semi-structured interview [25]. Interview time was longer for participants who used augmentative and alternative communication. A personal zoom account through the University was used to allow for great control of privacy and security. A unique private meeting ID and passcode was created for each interview. Unique identifiers were applied to each participant for referencing purposes and to protect confidentiality.

Data collection consisted of participants responding to demographic questions and semi-structured questions developed prior to the interviews which focused on the decision-making experience of having a PEG placement. The use of open-ended questions in a semi-structured interview format permitted the participants to expound upon their experience while allowing the interviewer to obtain relevant data across the participant sample. Data collected provided researchers with descriptive and personal findings from each participant.

Data Analysis

All interviews were audio recorded, transcribed, and anonymized. Demographic information was recorded and included age, sex, social support, living arrangements, date of ALS diagnosis and the date of the PEG procedure. Additional informational data recorded was the time, date, and location of each interview. Two researchers (GG and IS) independently analyzed and coded the five transcripts for description and themes. [26] This process assisted in isolating common responses between participants to assist in theme development. Using this method built textural and structural description of the participants’ experiences [27]. Due to the qualitative design of this study, the information obtained is highly subjective. Participants had different reasons for agreeing to a PEG tube insertion and experiences surrounding the process. Several steps were implemented to enhance trustworthiness and increase the rigor within the study design. Trustworthiness was established through formulating structured questions in advance to minimize bias and increase consistency between questions asked. This uniformity prevented the use of “lead in” questions, which also tends to bias responses from research participants [28]. Data collection was completed through a consistent interview technique involving open-ended non-leading questions. All interviews were voice recorded with typed verbatim transcriptions for researchers to verify for accuracy.

To confirm the accuracy of the researchers’ interpretations, inductive thematic analysis was utilized to evaluate the data [29]. Researchers familiarized themselves with the data collected and initial construction of data was created, and hierarchies developed. This was then analyzed and aggregated to develop themes. Themes were further reviewed, defined, named, and refined by returning to the raw data for confirmation of an accurate representation of the participants’ experiences. A written summary on each theme was completed with participants’ responses linked to the themes that shared the essence of that theme. Results of their analysis were compared, and discrepancies discussed to enhance the credibility of the results and to minimize interpretation bias.

Results

Results of the thematic analysis illuminated the many challenges that impact the decision-making process of undergoing an invasive procedure as a PEG insertion. Analysis produced 4 themes and included the following: (1) survival; (2) scary and anxiety provoking process; (3) wanting to live longer; and (4) not alone in my decision.

Theme 1: Survival

As the disease progressed, participants described the ability to swallow becoming more difficult with various type of food consistencies. Participants reported starting with solid foods, then moving to solids cut into very small pieces and eventually progressing to puree. Besides the physiology intricacies of swallowing, the psychological fear of choking became apparent (Table 2).

Table 2: Participant responses related to survival

Participant

Exemplar Responses

1 I was having trouble swallowing solid foods; ALS, and that the expected progression, the next step would be that I wouldn’t be able to feed myself; Went from cut up food very small then to puree then to straining the food. You know we’d rather do it earlier then wait till it’s too late … I choked a couple of times, scared the hell out of me…It was an awful submission to come to.
2 “I knew it had to be done, do it now before it is too late”
4 I was having trouble swallowing solid foods so I thought that with ALS that was the expected progression, the next step I wouldn’t be able to feed myself.
5 I was losing weight and my ability for chewing and swallowing…and muscles in my mouth got weaker… I was having trouble swallowing solids- food…concerned I wouldn’t be able to feed myself to stay alive”

Theme 2: Scary and Anxiety Provoking Process

Participants experienced a range of emotions from being scared to having anxiety in the decision-making process to obtain a PEG. These feeling stemmed from the lack of education and misinformation from the medical team who conveyed the urgency for a PEG, although not necessarily needed at the time. Participants expressed that medical teams were overly assertive and too comfortable in recommending such an invasive procedure that would have a lasting impact in their lives. The fear of the procedure was only heightened when participants were told they would not be able to feed orally post PEG placement. These factors and inconsistencies contributed to the theme of scary and anxiety provoking process, which is reflected in the following statements (Table 3).

Table 3: Participant responses related to scary and anxiety provoking process

Participant

Exemplar Responses

1 It was awful!! It was scary. I saw the doctor and he wanted to PEG me right away even though my vital capacity was near 70……not the type of bedside manner I could handle ….Awful submission to come to.
2 “They wanted to do it preemptively….I did not want to stop eating. I was concerned that I could no longer eat my favorite food. I mean…you know…my gosh!”
3 The doctor decided, I went to the hospital because of pneumonia and ending up with a tracheostomy and a PEG inserted…It was very terrifying… I was misinformed by the doctors as I was still able to eat by mouth. At that time, no one believe in those guys (doctors)… I didn’t need this as I never had a swallowing issue…I don’t recall them (doctors) sorry about the issue. Not well informed.” “I was misinformed by the MDs, as they told me I need it (PEG) as I would not be able to eat…. I still eat by mouth. Either I get the tracheotomy and the feeding tube, or they (doctors) unhook me. It was terrible… It was terrible.
4 I had a bad experience… the anesthesiologist, she scared the heck out of me; she had no experience. We were told that it was going to be a very simple procedure, it’s a common procedure, it happens all the time, it’s not a big deal. And we had a very different experience. When the anesthesiologist wasn’t experienced with ALS patients, apparently, and said in a nutshell that I was going to have to be intubated in order to do this procedure, and there was a very strong possibility that I would have a tracheostomy for the rest of his life, after that procedure. There was discussion when we went to the ALS Clinic, that you should get a feeding tube before you need it, but at that time I was eating food just fine; Overall it was scary…I fear procedures.
5  “The ALS clinic very persistent (in PEG placement). ”I was told that there was a possibility that if I didn’t come out of anesthesia I would be put on a vent and they weren’t sure if it could be reversed”

Theme 3: Wanted to Live Longer

Although the participants ranged in age, the need to live longer was an overarching theme for different reasons. The decision to have a PEG insertion weighed heavily as participants wanted to spend time with their children and see them through the stages of their lives. Besides their own children, the thought of not meeting future grandchildren seemed apparent. Other participants wanted to be able to live longer to spend time with family (Table 4).

Table 4: Participant responses related to wanting to live longer

Participant

Exemplar Responses

1 I did it (PEG insertion) for my daughter, I wanted to be here longer for her. The practical reasons were lost on me. I just needed more time with my daughter.
2 “It was all steppingstones, I walked with the walker, then a scooter. I couldn’t drive my car anymore. So it’s like each step…O.k., this is real, I’m not getting better.
3 They (doctors)could do anything they want; I want to live…all I want was to live a few more years. That’s what I’m thinking about. The day I can’t eat or swallow is the day that I’ll lose all hope.
4 I want to be here to see my kids grow up, see my grandchildren…so it’s my driving force.
5 “losing weight, not being able to chew or swallow, not having enough nutrition, I want to go on”

Theme 4: Not Alone in My Decision

The decision-making process to have a PEG placement can be influenced by an individual’s immediate or extended family to a health care provider, having the knowledge of the outcomes of prior patients. In this study, participants cited that both family and healthcare team members were instrumental in the decision-making process. These influences in decision-making were reflected in participants’ statements (Table 5).

Table 5: Participant responses related to not alone in my decision

Participant

Exemplar Responses

1 I was bombarded by them, my family. My mom, spouse, siblings, mother-in-law who wanted me to live longer…There were just a lot of them… you should do it sooner, don’t wait too long… The person that helped me agree to it was my nurse practitioner working for my neurologist.. She had a nice bedside manner and explained the process.
2 My spouse and I…We talked about it; it was scary… My spouse, together we made the decision… We knew it had to be done. I started to cry…just another step further into the disease…We knew it was time…do it earlier than wait till it’s too late…My RN played a huge role in my decision. She knows everything, she’s really smart
4 My spouse and I made the decision… didn’t want to get it until I needed it…when I had difficulty that would be the time to get it.
5 ” Family, brothers, sister- in- laws, parents, aunts, uncles, cousins, all encouraged and supported that this would be the best thing before my lungs got worse”

Discussion and Conclusion

Participants of this study went through a myriad of feelings and emotions including fear and anxiety when faced with the decision to have a PEG tube inserted. The decision-making process was described as very difficult and layered, filled with an array of varying opinions, facts, and influences from family members, friends, and health care professionals. Participants described the support from family and friends and the need to survive and live on as greatly contributing to the decision-making process. Two participants identified the nurse or nurse practitioner at multidisciplinary clinics as being instrumental in the decision-making process because they took the time to explain and educate on what the procedure and what living with a PEG might be like.

Some participants expressed frustration and resentment over feeling pushed by into the decision even though they were not quite ready. These feelings were exacerbated by receiving differing or no information from healthcare professionals about the PEG procedure, the need for the PEG, and what to expect after the procedure. Additionally, participants described some healthcare professionals as being cavalier in their discussions with them, which left them feeling disrespected and not heard. The experience in decision making of our participants was found to be congruent with research reviewed. Shaghayegh (2016) found that inconsistent or poor patient involvement between the medical team and patients led to patients’ loss of autonomy and responsibility for their own care [30]. Similarly, Covvey et al. 2019, identified themes for barriers to shared decision making were uncertainty in the treatment decision, concern regarding adverse effects, and poor physician communication [31].

Research reflects that patients are often fearful to engage health professionals in discussions regarding medical issues beyond their understanding, placing patients in a negotiating position from fear and confusion, rather than knowledge and shared discussion, Berry (2017) refers to as “hostage bargaining syndrome” (HBS) [32]. This idea of HBS, where an imbalance of knowledge exist, will only further breakdown shared decision-making and lead to a sense of frustration, anger, or helplessness on part of the patient. The outcome of this study reflects some participants who were offered little options or medical justification for the PEG insertion, rather “since you’re here in the clinic already, you will need a PEG eventually”. This mindset left participants with increased anxiety and a loss of autonomy over their own care. Although participants were able to cope with these challenges, it was not without exerting a toll on their emotional well-being. The data also suggests collaborative decision-making can provide benefits in terms of a reduction of conflict between families and healthcare members to improve the overall decision-making process. Effective communication is a medical necessity for the delivery of quality professional care to PALS and their families, as vital decisions cannot be made lightly.

The study further shows that the decision-making process is multifaceted, from participants’ healthcare team, spouses, children, to extended family and friends. Participants highlighted that family played an integral role supporting them in the process. Although family may have not understood the process, through their eyes, it was an extension of life. Besides family, participants discussed their healthcare teams in both a positive and negative context. Some PALS found the nurses and nurse practitioners in the multidisciplinary ALS clinics that they attend, to be helpful. Some PALS perceived that some members of their health care team showed little compassion or that they treated the situation as “another day on the job”. All participants wished that they had received better education on PEG tubes from their health care teams. Patients with ALS face a difficult and multifaceted decision when it comes to accepting or refusing the placement of a permanent feeding tube. Interviewing these participants who decided to obtain a PEG tube allowed us to obtain first-hand information on the factors that went into their decision-making process. Beside researchers, healthcare teams may be better equipped in meeting patients’ needs in preplacement stages to reduce overall stress and anxiety. Ultimately, the study shed light on the reasons that participants choose to receive a feeding tube despite the procedure’s implications. Given that patient participation results in improved health outcomes, increased quality of life, and provision of more client-centered interventions, patients need to be involved in the shared decision making process [33-35]. Based on the information widely available through current technology, patients need to be regarded as equal partners in the discussion of their own health care process, to better make more informed decisions.

Finally, we note that the results of this study cannot be generalized to the rest of the ALS population. Findings are not intended to speak for the experiences of other PALS who made the decision to receive a feeding tube. However, the study collected meaningful, individualized data, allowing participants the opportunity to share their personal experiences and tell their stories. This will contribute to the knowledge base regarding PEG feeding and have the potential to help other PALS, caregivers, and healthcare professionals. There are several limitations to this study. There are a small number of participants, all attending multidisciplinary ALS clinics, and all living in the same region of the United States. As such, these factors may limit the generalizability to PALS living in other geographical locations.

Acknowledgments

The authors thank Erin Callahan, OTS, Sara Long, OTS, and Samantha Sewell, OTS, (Hofstra University) for assisting with this research study.

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The Use of a Novel Graphitic Carbon Nitride/Cerium Dioxide (g-C3N4/CeO2) Nanocomposites for the Ofloxacin Removal by Photocatalytic Degradation in Pharmaceutical Industry Wastewaters and the Evaluation of Microtox (Aliivibrio fischeri) and Daphnia magna Acute Toxicity Assays

DOI: 10.31038/NAMS.2023621

Abstract

In this study, a novel graphitic carbon nitride/cobalt molybdate (g-C3N4/CeO2) nanocomposites (NCs) as a photocatalys was examined during photocatalytic degradation process in the efficient removal of Ofloxacin (OFX) from pharmaceutical industry wastewater plant, İzmir, Turkey. Different pH values (3.0, 4.0, 6.0, 7.0, 9.0 and 11.0), increasing OFX concentrations (5 mg/l, 10 mg/l, 20 mg/l and 40 mg/l), increasing g-C3N4/CeO2 NCs concentrations (1 mg/l, 2 mg/l, 4 mg/l, 6 mg/l, 8 mg/l and 10 mg/l), different g-C3N4/CeO2 NCs mass ratios (5/5, 6/4, 7/3, 8/2, 9/1, 1/9, 2/8, 3/7 and 4/6), increasing recycle times (1., 2., 3., 4., 5., 6. and 7.) was operated during photocatalytic degradation process in the efficient removal of OFX in pharmaceutical industry wastewater. The characteristics of the synthesized nanoparticles (NPs) were assessed using X-Ray Difraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), Energy-Dispersive X-Ray (EDX), Fourier Transform Infrared Spectroscopy (FTIR), Transmission Electron Microscopy (TEM), and Diffuse reflectance UV-Vis spectra (DRS) analyses, respectively. The acute toxicity assays were operated with Microtox (Aliivibrio fischeri also called Vibrio fischeri) and Daphnia magna acute toxicity tests. The photocatalytic degradation mechanisms of g-C3N4/CeO2 NCs and the reaction kinetics of OFX were evaluated in pharmaceutical industry wastewater during photocatalytic degradation process. ANOVA statistical analysis was used for all experimental samples. The maximum 99% OFX removal efficiency was obtained during photocatalytic degradation process in pharmaceutical industry wastewater, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively. The maximum 99% OFX removal efficieny was found with photocatalytic degradation process in pharmaceutical industry wastewater, at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min, at pH=6.0 and at 25°C, respectively. The maximum 99% OFX removal efficieny was measured to 8 mg/l g-C3N4/CeO2 NCs with photocatalytic degradation process in pharmaceutical industry wastewater, at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min, at pH=6.0 and at 25°C, respectively. The maximum 99% OFX removal efficiency was measured at 2/8wt g-C3N4/CeO2 NCs mass ratios at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min, at pH=6.0 and at 25°C, respectively. The maximum 99% OFX removal efficiency was measured in pharmaceutical industry wastewater during photocatalytic degradation process, after 1. recycle time, at 20 mg/l OFX, 8 mg/l g-C3N4/CeO2 NCs, at 2/8wt g-C3N4/CeO2 NCs mass ratio, after 180 min, at pH=6.0 and at 25°C, respectively. 96.41% maximum Microtox (Aliivibrio fischeri) acute toxicity removal yield was found in OFX=20 mg/l after 180 min photocatalytic degradation time and at 60°C. It was observed an inhibition effect of OFX=40 mg/l to Microtox with Vibrio fischeri after 180 min and at 60°C. 92.38% maximum Daphnia magna acute toxicity removal was obtained in OFX=20 mg/l after 180 min photocatalytic degradation time and at 60°C, respectively. It was observed an inhibition effect of OFX=40 mg/l to Daphnia magna after 180 min and at 60°C. OFX concentrations > 20 mg/l decreased the acute toxicity removals by hindering the photocatalytic degradation process. Similarly, a significant contribution of increasing OFX concentrations to acute toxicity removal at 60°C after 180 min, was not observed. It can be concluded that the toxicity originating from the OFX is not significant and the real acute toxicity throughout photocatalytic degradation process was attributed to the pharmaceutical industry wastewater, to their metabolites and to the photocatalytic degradation process by-products. As a result, the a novel g-C3N4/CeO2 NCs photocatalyst during photocatalytic degradation process in pharmaceutical industry wastewater was stable in harsh environments such as acidic, alkaline, saline, and then was still effective process. When the amount of contaminant was increased, the a novel g-C3N4/CeO2 NCs photocatalys during photocatalytic degradation process performance was still considerable. The synthesis and optimization of g-C3N4/CeO2 heterostructure photocatalyst provides insights into the effects of preparation conditions on the material’s characteristics and performance, as well as the application of the effectively designed photocatalyst in the removal of antibiotics, which can potentially be deployed for purifying wastewater, especially pharmaceutical wastewater. Finally, the combination of a simple, easy operation preparation process, excellent performance and cost effective, makes this a novel g-C3N4/CeO2 NCs a promising option during photocatalytic degradation process in pharmaceutical industry wastewater treatment.

Keywords

ANOVA statistical analysis, Antibiotics, Coronavirus Disease-2019 (COVID-19), Cost analysis, Diffuse reflectance UV-Vis spectra (DRS), Electrochemical filtration process, Energy-dispersive X-ray (EDX), Field emission scanning electron microscopy (FESEM), Fourier transform infrared spectroscopy (FTIR), Hydrothermal-calcination method, Hydroxly (OH●) radicals, Microtox (Aliivibrio fischeri or Vibrio fischeri) and Daphnia magna acute toxicity tests, Nanoparticles (NPs), Novel graphitic carbon nitride/cerium dioxide nanocomposites (g-C3N4/CeO2 NCs), Ofloxacin (OFX), Pharmaceutical industry wastewater, Photocatalytic degradation mechanisms, Reaction kinetics, Sol–gel method, Transmission Electron Microscopy (TEM), Ultraviolet (UV), X-ray difraction (XRD)

Introductıon

Emerging contaminants (ECs), sometimes known as contaminants of emerging concern (CECs) can refer to a wide variety of artificial or naturally occurring chemicals or materials that are harmful to human health after long-term disclosure. ECs can be classified into several classes, including agricultural contaminants (pesticides and fertilizers), medicines and antidote drugs, industrial and consumer waste products, and personal care and household cleaning products [1,2]. Antibiotics are one of the ECs that have raised concerns in the previous two decades because they have been routinely and widely used in human and animal health care, resulting in widespread antibiotic residues discharged in surface, groundwater, and wastewater.

Antibiotics, which are widely utilized in medicine, poultry farming and food processing, have attracted considerable attention due to their abuse and their harmful effects on human health and the ecological environment. The misuse of antibiotics induces Deoxyribonucleic Acid (DNA) contamination and accelerates the generation of drug-resistant bacteria and super-bacteria thus, some diseases are more difficult to cure . A number of studies have revealed that the level of antibiotics in the soil, air and surface water, and even in potable water, is excessive in many areas, which will ultimately accumulate in the human body via drinking water and then damage the body’s nervous system, kidneys and blood system. Therefore, it is necessary to develop an efficient method to remove antibiotics present in pharmaceutical industry wastewater [3-13].

The uncontrolled, ever-growing accumulation of antibiotics and their residues in the environment is an acute modern problem. Their presence in water and soil is a potential hazard to the environment, humans, and other living beings. Many therapeutic agents are not completely metabolized, which leads to the penetration of active drug molecules into the biological environment, the emergence of new contamination sources, the wide spread of bacteria and microorganisms with multidrug resistance. Modern pharmaceutical wastewater facilities do not allow efficient removal of antibiotic residues from the environment, which leads to their accumulation in ecological systems . Global studies of river pollution with antibiotics have shown that 65% of surveyed rivers in 72 countries on 6 continents are contaminated with antibiotics. According to the World Health Organization (WHO), surface and groundwater, as well as partially treated water, containing antibiotics residue and other pharmaceuticals, typically at < 100 ng/l concentrations, whereas treated water has < 50 ng/l concentrations, respectively . However, the discovery of ECs in numerous natural freshwater sources worldwide is growing yearly. Several antibiotic residues have been reported to have been traced at concentrations greater than their ecotoxicity endpoints in the marine environment, specifically in Europe and Africa . Thus, the European Union’s Water Framework Directive enumerated certain antibiotics as priority contaminants. In some rivers, the concentrations were so high that they posed a real danger to both the ecosystem and human health. This matter, the development of effective approaches to the removal of antibiotics from the aquatic environment is of great importance [14-26].

The removal of antibiotics and their residues from water and wastewater prior to their final release into the environment is of particular concern. Modern purification methods can be roughly divided into the following three categories depending on the purification mechanism: biological treatment, chemical degradation, and physical removal. Each of these methods has its own advantages and disadvantages. For example, biological purification can remove most antibiotic residues, but the introduction of active organisms into the aquatic environment can upset the ecological balance. Various chemical approaches (ozonation, chlorination, and Fenton oxidation) cannot provide complete purification and, in some cases, lead to the death of beneficial microorganisms due to low selectivity. Photocatalysis is widely used in new environmental control strategies. However, this method has a number of key disadvantages, such as insufficient use of visible light, rapid annihilation of photogenerated carriers, and incomplete mineralization, which greatly limits its application [27-33].

Ofloxacin (OFX) is a quinolone antibiotic useful for the treatment of a number of bacterial infections . A quinolone antibiotic is a member of a large group of broad-spectrum bacteriocidals that share a bicyclic core structure related to the substance 4-quinolone . They are used in human and veterinary medicine to treat bacterial infections, as well as in animal husbandry, specifically poultry production . OFX is well-known for their antimicrobial and anti-inflammatory capabilities . OFX is used to treat pneumonia, skin and urinary tract infections . Severe acute respiratory syndrome (SARS)-CoV-2 (COVID-19) pandemic, which has killed and infected people in 216 countries/territories, has become the most significant pandemic of the century . OFX combined with other drugs, has been widely used to minimise COVID-19-induced inflammation in 2020.OFX is a typical fluoroquinolone antibiotic administered to both humans and animals, and after administration, approximately 78% of OFX is excreted. OFX pharmaceutical compounds enter water resources in various ways, such as human and animal excretions and inefficient industrial wastewater treatment. In the class of antibiotics, OFX is also recognised as highly refractory and persistent in aquatic water systems. As the biodegradation of OFX is difficult, sewage treatment plants (STPs) have a low removal rate, and the OFX concentrations in the STP effluents of Beijing, Hangzhou, and Vancouver have been determined to be between 6×10-7 and 1.405×10-3 mg/l [34-41].

Generally, the advanced oxidation processes (AOPs), such as the Fenton or Fenton-like reaction, ozonation or catalytic ozonation, photocatalytic oxidation, electrochemical oxidation, and ionizing radiation, have been widely used for antibiotics degradation in recent years . One of the most promising techniques applied for efficient degradation of antibiotics are Advanced Oxidation Processes (AOPs). Nowadays, particular attention is paid to photocatalytic reactions, in which highly oxidizing species responsible for mineralization of organic pollutants are formed in-situ in the reaction media by means of light and a photocatalyst . The photocatalytic activity is closely related to the physicochemical properties but also to the morphology and texture of the materials studied, for this reason the synthesis techniques are often of great importance. Photocatalysis, which occurs under exposure to UV light, is also a common method for the environmental pollutant elimination . The conventional photocatalysis utilizes mostly UV from sunlight, which accounts for only 4% of the solar energy. Therefore, through the introduction of catalysts, the utilization rate of sunlight can be effectively improved. To overcome the low-efficiency problem of the photocatalysis, the development of a more efficient catalyst system that would effectively improve the catalytic oxidation efficiency and overcome the existing limitations is important. The catalytic activity of the catalyst can be effectively improved by modulating its surface area, preparation method, and changing its properties and structures [42-57].

Numerous materials have been reported to have the potential and capacity to treat water or wastewater polluted with these antibiotics residue by applying the processes of adsorption and catalytic oxidation during the last few decades. The reported materials include mesoporous carbon beads, clay minerals, activated carbon, cellulose, and chitosan. As a result of engineering and science evolution, and in complement to the urgent need to increase the adsorption capability of antibiotic contaminants, more advanced materials such as carbon nanotube (CnT), nano-zero valent iron (nZVI), nanoporous carbons, porous graphene and graphene oxide (GO), to date have been analyzed and improved in their ability to remove these ECs from water [58-85].

Nanomaterials with a high specific surface area are a promising platform for the development and production of low-cost and highly efficient sorbents for various pollution molecules. For example, graphene-based nanomaterials were utilized to remove antibiotics, which are adsorbed on the material surfaces due to π-π-, electrostatic or hydrophobic interactions, as well as the formation of hydrogen bonds. Highly efficient antibiotic sorption was also observed when using highly porous, surface-active, and structurally stable silica-based materials, metal oxide NPs, and metal-organic frameworks. The photocatalysts, which mainly rely on the production of highly oxidizing species such as hydroxyl radical (OH) and superoxide anion radical (O2− ●), have been considered an effective approach for the degradation of antibiotics in water [86-103].

The two-dimensional (2D) g-C3N4 semiconductor has a wide range of applications in the environmental and energy fields because of its visible-light activity, unique physicochemical properties, excellent chemical stability and low-cost. Some important limitations of the photocatalytic activity of g-C3N4 are its low specific surface area, fast recombination of electrons and holes and poor visible light absorption. To improve the above problems, the construction of a heterojunction with a suitable band gap semiconductor (co-catalyst) has been shown to be a good strategy to improve the photocatalytic performance of g-C3N4, such as g-C3N4-based conventional type II heterostructures, g-C3N4-based Z-scheme heterostructures, and g-C3N4-based p–n heterostructures, etc. The unique “Z” shape as the transport pathway of photogenerated charge carriers in Z-scheme photocatalytic systems is the most similar system to mimic natural photosynthesis in the many g-C3N4-based heterojunction photocatalysts. The construction of Z-scheme photocatalytic systems can promote visible light utilization and carrier separation, and maintain the strong reducibility and oxidizability of semiconductors. There are many studies on g-C3N4-based Z-scheme heterojunction photocatalysts, such as ZnO/g-C3N4, WO3/g-C3N4, g-C3N4/ZnS,, g-C3N4/NiFe2O4, g-C3N4/graphene/NiFe2O4, NiCo/ZnO/g-C3N4 and Bi2Zr2O7/g-C3N4/Ag3PO4, respectively. g-C3N4-based Z-scheme heterojunction photocatalysts have been made to improve the photocatalytic activity by combining with other semiconductor materials. Therefore, there are some problems with the single photocatalytic method, such as low adsorption ability, limited active sites and low removal efficiency. The integration of the adsorption and photocatalytic degradation of various organic pollutants is considered as a suitable and promising technology. On the other hand, it is still essential to fabricate photocatalysts with superior adsorption and degradation efficiencies [104-121].

g-C3N4 has been gaining great attention as a potential photocatalyst due to its stability and safety characteristics, as well as the fact that it can be facilely synthesized from low-cost raw materials. The low bandgap (~2.7 eV) can drive photo-oxidation reactions even under visible light. However, the pure g-C3N4 has some drawbacks such as its low redox potential and high rate of recombination between photo-induced electrons and holes, which dramatically limits its photocatalytic efficiency. Several strategies have been investigated, including modification of the material’s size and structure, nonmetal and metal doping, and coupling with other photocatalysts. For example, Liu et al. improved bulk g-C3N4’s performance in terms of Rhodamine B degradation from 30% to 100% by synthesizing mesoporous g-C3N4 nanorods through the nano-confined thermal condensation method. Dai et al. doped g-C3N4 with Cu through a thermal polymerization route and acquired a degradation rate of 90.5% with norfloxacin antibiotic. Nithya and Ayyappan, synthesized hybridized g-C3N4/ZnBi2O4 for reduction of 4-nitrophenol and reached an optimal removal efficiency of 79%. Among all, the construction of heterostructure photocatalysts by coupling g-C3N4 with other semiconductors seems to be an effective strategy to prevent electron and hole recombination, hence improving photocatalytic efficiency for contaminant treatment [122-131].

CeO2 (Ceria or Cerium(IV) oxide) is a versatile, inert, and physically and chemically stable material with multiple and diverse applications. Due to its hardness (Mohs scale 7), it was initially used as an abrasive material, but today it is used (alone or in binary or complex mixtures) in the field of heterogeneous catalysis (oxidation of hydrocarbons) or in the field of sensors, energy, and fuels such as solid oxide fuel cells, but also in water-splitting processes or photocatalysis. CeO2 applications in the dermato-cosmetics industry and in the biomedical field (antibacterial effect) should also be mentioned here. CeO2 is also possible to combine two or more properties, for example, the infrared filtering properties with the photocatalytic ones, to optimize practical applications. CeO2 is semiconductor photocatalyst with various applications and similar properties to TiO2. However, its band gap is in the wide range of 2.6 to 3.4 eV, depending on the preparation method. Furthermore, CeO2 exhibits promising photocatalytic activity. Nonetheless, the position of CB and VB limits its application as an efficient photocatalyst utilizing solar energy, even though CeO2 can absorb a larger fraction of the solar spectrum than TiO2. The photocatalytic and photoelectrocatalytic activity of CeO2 in wastewater treatment can be improved by various modification techniques, including changes in morphology, doping with metal cation dopants and non-metal dopants, coupling with other semiconductors, combining it with carbon supporting materials, etc.. The main properties that make CeO2 significant as a photocatalyst and photoelectrode material applied in the degradation of various pollutants result from its high band gap energy, high refractive index, high optical transparency in the visible region, high oxygen storage capacity, and chemical reactivity. The other properties of CeO2 which should be mentioned include its high thermal stability, high hardness, oxygen ion conductivity, special redox features, and easy conversion between Ce+3 and Ce+4 oxidation states [132-156].

The conduction band (CB) of g-C3N4 is more negative than that of CeO2 (-1.24 eV and -0.44, respectively), while CeO2 possesses a relatively positive valance band (VB) (2.56 eV) compared to the conduction band of g-C3N4, would theoretically facilitate the electron transition within the coupled photocatalyst to prolong the electron-hole separation [157]. Particularly, under the illumination of visible light, g-C3N4 can be excited to generate electron-hole pairs. Cerium (Ce) has exciting catalytic characteristics because 4d and 5p electrons sufficiently defend the 4f orbitals. The photogenerated electrons in the conduction band of CeO2 tend to transfer and recombine with the photogenerated holes in the valence band of g-C3N4. Like this, the larger number of photogenerated electrons accumulated in the conduction band of g-C3N4 can reduce the adsorbed O2 to form more O2– ●. At the same time, the photogenerated holes left behind in the valence band of CeO2 can oxidize the adsorbed H2O to give OH. But, the photocatalytic activity of the g-C3N4/CeO2 system would be significantly increased, leading to the decomposition of organic compounds by O2– ● and OHreactive species.

In this study, a novel g-C3N4/CeO2 NCs as a photocatalys was examined during photocatalytic degradation process in the efficient removal of OFX from pharmaceutical industry wastewater plant, İzmir, Turkey. Different pH values (3.0, 4.0, 6.0, 7.0, 9.0 and 11.0), increasing OFX concentrations (5 mg/l, 10 mg/l, 20 mg/l and 40 mg/l), increasing g-C3N4/CeCO2 NCs concentrations (1 mg/l, 2 mg/l, 4 mg/l, 6 mg/l, 8 mg/l and 10 mg/l), different g-C3N4/CeO2 NCs mass ratios (5/5, 6/4, 7/3, 8/2, 9/1, 1/9, 2/8, 3/7 and 4/6), increasing recycle times (1., 2., 3., 4., 5., 6. and 7.) was operated during photocatalytic degradation process in the efficient removal of OFX in pharmaceutical industry wastewater. The characteristics of the synthesized NPs were XRD, FESEM, EDX, FTIR, TEM and DRS analyses, respectively. The acute toxicity assays were operated with Microtox (Aliivibrio fischeri also called Vibrio fischeri) and Daphnia magna acute toxicity tests. The photocatalytic degradation mechanisms of g-C3N4/CeO2 NCs and the reaction kinetics of OFX were evaluated in pharmaceutical industry wastewater during photocatalytic degradation process. ANOVA statistical analysis was used for all experimental samples.

Materıals and Methods

Characterization of Pharmaceutical Industry Wastewater

Characterization of the biological aerobic activated sludge proses from a pharmaceutical industry wastewater plant, İzmir, Turkey was performed. The results are given as the mean value of triplicate samplings (Table 1).

Table 1: Characterization of Pharmaceutical Industry Wastewater

Parameters

Unit

Concentrations

Chemical oxygen demand-total (CODtotal) (mg/l)

4000

Chemical oxygen demand-dissolved (CODdissolved) (mg/l)

3200

Biological oxygen demand-5 days (BOD5) (mg/l)

1500

BOD5/CODdissolved

0.5

Total organic carbons (TOC) (mg/l)

1800

Dissolved organic carbons (DOC) (mg/l)

1100

pH

8.3

Salinity as Electrical conductivity (EC) (mS/cm)

1552

Total alkalinity as CaCO3 (mg/l)

750

Total volatile acids (TVA) (mg/l)

380

Turbidity (Nephelometric Turbidity unit, NTU) NTU

7.2

Color 1/m

50

Total suspended solids (TSS) (mg/l)

250

Volatile suspended solids (VSS) (mg/l)

187

Total dissolved solids (TDS) (mg/l)

825

Nitride (NO2) (mg/l)

1.7

Nitrate (NO3) (mg/l)

1.91

Ammonium (NH4+) (mg/l)

2.3

Total Nitrogen (Total-N) (mg/l)

3.2

SO3-2 (mg/l)

21.4

SO4-2 (mg/l)

29.3

Chloride (Cl) (mg/l)

37.4

Bicarbonate (HCO3) (mg/l)

161

Phosphate (PO4-3) (mg/l)

16

Total Phosphorus (Total-P) (mg/l)

40

Total Phenols (mg/l)

70

Oil & Grease (mg/l)

220

Cobalt (Co+3) (mg/l)

0.2

Lead (Pb+2) (mg/l)

0.4

Potassium (K+) (mg/l)

17

Iron (Fe+2) (mg/l)

0.42

Chromium (Cr+2) (mg/l)

0.44

Mercury (Hg+2) (mg/l)

0.35

Zinc (Zn+2) (mg/l)

0.11

Preparation of Graphitic Carbon Nitride (g-C3N4) Nanoparticles

g-C3N4 was prepared by calcination of melamine (C3H6N6) in a crucible with a lid at 550°C for 4 h. The obtained yellow powder was ground in an agate mortar after being cooled down to 25°C room temperature.

Preparation of Cerium Dioxide (CeO2) Nanoparticles

CeO2 NPs were prepared by sol–gel method. Nano-sized CeO2 was also prepared by the sol–gel procedure using Cerium nitrate hexahydrate [Ce(NO3)3.6H2O] and 20 ml of Triethanolamine (C6H15NO3). Then, they were mixed together by a magnetic stirrer on a hot plate to insure that the cerium salt was dissolved in C6H15NO3. After that the solution was heated up to 90°C until the clear dark brown homogenous solution, sol, was observed. To prepare black colloidal solution (gel), it was kept in a digital furnace at 270°C for 2 h. As gel was produced, it was cooled to 25°C room temperature. In order to form the expected precipitate, the volume of the gel solution was adjusted to 100 ml by adding ethanol (C₂H₆O). Then, synthesized precipitate was separated by centrifugation and washed by deionized water and C₂H₆O. Finally, the produced CeO2 NPs was dried at 90°C and calcinated.

Preparation of A Novel Graphitic Carbon Nitride/Cerium Dioxide (g-C3N4/CeO2) Nanocomposites

The g-C3N4/CeO2 NCs was synthesized by the hydrothermal-calcination method. Firstly, 1 gram g-C3N4 NPs was added into distilled water and magnetically stirred for 30 min. Then, the portions of prepared g-C3N4 NPs were added to the mixtures to obtain the mass ratios of g-C3N4 to CeO2 of 5/5, 6/4, 7/3, 8/2, 9/1, 1/9, 2/8, 3/7 and 4/6, respectively, and kept being stirred for another 1 h. The final mixtures were transferred into a 100 ml autoclave and reacted at 180°C for different hydrothermal (HT) times of 2 h, 4 h and 6 h. The final samples were centrifuged and washed with distilled water and C₂H₆O for 2 times. Then, the samples were dried, and finally, the dried products were heated in a Muffle furnace at different calcination temperatures of 300°C, 400°C and 500°C for 4 h to get the target composites. The synthesis conditions and the corresponding sample names were summarized at Table 2.

Table 2: The optimization parameters of g-C3N4/CeO2 NCs samples

 

Sample Name

Mass Ratios of g-C3N4/CeO2 NCs

Calcination Temperature (°C)

in 240 min

Hydrothermal Time (min) at 180°C

HT-2h-Cal300

8/2

300°C

120

HT-2h-Cal400

8/2

400°C

120

HT-2h-Cal500

8/2

500°C

120

HT-4h-Cal300

8/2

300°C

240

HT-4h-Cal400

8/2

400°C

240

HT-4h-Cal500

8/2

500°C

240

HT-6h-Cal300

8/2

300°C

360

HT-6h-Cal400

8/2

400°C

360

HT-6h-Cal500

8/2

500°C

360

5/5 wt, g-C3N4/CeO2

5/5

500°C

360

6/4 wt, g-C3N4/CeO2

6/4

500°C

360

7/3 wt, g-C3N4/CeO2

7/3

500°C

360

8/2 wt, g-C3N4/CeO2

8/2

500°C

360

9/1 wt, g-C3N4/CeO2

9/1

500°C

360

1/9 wt, g-C3N4/CeO2

1/9

500°C

360

2/8 wt, g-C3N4/CeO2

2/8

500°C

360

3/7 wt, g-C3N4/CeO2

3/7

500°C

360

4/6 wt, g-C3N4/CeO2

4/6

500°C

360

Photocatalytic Degradation Reactor

A 2 liter cylinder quartz glass reactor was used for the photodegradation experiments in the pharmaceutical industry wastewater at different operational conditions. 1000 ml pharmaceutical industry wastewater was filled for experimental studies and the photocatalyst were added to the cylinder quartz glass reactors. The UV-A lamps were placed to the outside of the photo-reactor with a distance of 3 mm. The photocatalytic reactor was operated with constant stirring (1.5 rpm) during the photocatalytic degradation process. 10 ml of the reacting solution were sampled and centrifugated (at 10000 rpm) at different time intervals. The UV irradiation treatments were created using one or three UV-A lamp emitting in the 350–400 nm range (λmax = 368 nm; FWHM = 17 nm; Actinic BL TL-D 18W, Philips). Six 50 W UV-A lamps (Total: 300 W UV-A lamps) were used during experimental conditions for this study.

Characterization

X-Ray Diffraction Analysis

Powder XRD patterns were recorded on a Shimadzu XRD-7000, Japan diffractometer using Cu Kα radiation (λ = 1.5418 Å, 40 kV, 40 mA) at a scanning speed of 1°/min in the 10-80° 2θ range. Raman spectrum was collected with a Horiba Jobin Yvon-Labram HR UV-Visible NIR (200-1600 nm) Raman microscope spectrometer, using a laser with the wavelength of 512 nm. The spectrum was collected from 10 scans at a resolution of 2 /cm. The zeta potential was measured with a SurPASS Electrokinetic Analyzer (Austria) with a clamping cell at 300 mbar.

Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-Ray (EDX) Spectroscopy Analysis

The morphological features and structure of the synthesized catalyst were investigated by FESEM (FESEM, Hitachi S-4700), equipped with an EDX spectrometry device (TESCAN Co., Model III MIRA) to investigate the composition of the elements present in the synthesized catalyst.

Fourier Transform Infrared Spectroscopy (FTIR) Analysis

The FTIR spectra of samples was recorded using the FT-NIR spectroscope (RAYLEIGH, WQF-510).

Transmission Electron Microscopy (TEM) Analysis

The structure of the samples were analysed TEM analysis. TEM analysis was recorded in a JEOL JEM 2100F, Japan under 200 kV accelerating voltage. Samples were prepared by applying one drop of the suspended material in ethanol onto a carbon-coated copper TEM grid, and allowing them to dry at 25°C room temperature.

Diffuse Reflectance UV-Vis Spectra (DRS) Analysis

DRS Analysis in the range of 200–800 nm were recorded on a Cary 5000 UV-Vis Spectrophotometer from Varian. DRS was used to monitor the OFX antibiotic concentration in experimental samples.

Analytical Procedures

Chemical oxygen demand-total (CODtotal), chemical oxygen demand-dissolved (CODdissolved), total phosphorus (Total-P), phosphate phosphorus (PO4-3-P), total nitrogen (Total-N), ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3-N), nitrite nitrogen (NO2-N), biological oxygen demand 5-days (BOD5), pH, Temperature [(°C)], total suspended solids (TSS), total volatile suspended solids (TVSS), total organic carbon (TOC), Oil, Chloride (Cl), total phenol, total volatile acids (TVA), disolved organic carbon (DOC), total alkalinity, turbidity, total dissolved solid (TDS), color, sulfide (SO3-2), sulfate (SO4-2), bicarbonate (HCO3), salinity, cobalt (Co+3), lead (Pb+2), potassium (K+), iron (Fe+2), chromium (Cr+2), Mercury (Hg+2) and zinc (Zn+2) were measured according to the Standard Methods (2017) 5220B, 5220D, 4500-P, 4500-PO4-3, 4500-N, 4500-NH4+, 4500-NO3, 4500-NO2, 5210B, 4500-H+, 2320, 2540D, 2540E, 5310, 5520, 4500-Cl, 5530, 5560B, 5310B, 2320, 2130, 2540E, 2120, 4500-SO3-2, 4500-SO4-2, 5320, 2520, 3500-Co+3, 3500-Pb+2, 3500-K+, 3500-Fe+2, 3500-Cr+2, 3500-Hg+2, 3500-Zn+2, respectively [158].

Total-N, NH4+-N, NO3-N, NO2-N, Total-P, PO4-3-P, total phenol, Co+3, Pb+2, K+, Fe+2, Cr+2, Hg+2, Zn+2, SO3-2, and SO4-2 were measured with cell test spectroquant kits (Merck, Germany) at a spectroquant NOVA 60 (Merck, Germany) spectrophotometer (2003).

The measurement of color was carried out following the methods described by Olthof and Eckenfelder [159] and Eckenfelder [160]. According these methods, the color content was determined by measuring the absorbance at three wavelengths (445 nm, 540 nm and 660 nm), and taking the sum of the absorbances at these wavelengths. In order to identify the color in pharmaceutical industry wastewater (25 ml) was acidified at pH=2.0 with a few drops of 6 N HCl and extracted three times with 25 ml of ethyl acetate. The pooled organic phases were dehydrated on sodium sulphate, filtered and dried under vacuum. The residue was sylilated with bis(trimethylsylil)trifluoroacetamide (BSTFA) in dimethylformamide and analyzed by gas chromatography–mass spectrometry (GC-MS) and gas chromatograph (GC) (Agilent Technology model 6890N) equipped with a mass selective detector (Agilent 5973 inert MSD). Mass spectra were recorded using a VGTS 250 spectrometer equipped with a capillary SE 52 column (HP5-MS 30 m, 0.25 mm ID, 0.25 μm) at 220°C with an isothermal program for 10 min. The initial oven temperature was kept at 50°C for 1 min, then raised to 220°C at 25°C/min and from 200 to 300°C at 8°C/min, and was then maintained for 5.5 min. High purity He (g) was used as the carrier gas at constant flow mode (1.5 ml/min, 45 cm/s linear velocity).

The total phenol was monitored as follows: 40 ml of pharmaceutical industry wastewater was acidified to pH=2.0 by the addition of concentrated HCl. Total phenol was then extracted with ethyl acetate. The organic phase was concentrated at 40°C to about 1 ml and silylized by the addition of N,O-bis(trimethylsilyl) acetamide (BSA). The resulting trimethylsilyl derivatives were analysed by GC-MS (Hewlett-Packard 6980/HP5973MSD).

Methyl tertiary butyl ether (MTBE) was used to extract oil from the water and NPs. GC-MS analysis was performed on an Agilent gas chromatography (GC) system. Oil concentration was measured using a UV–vis spectroscopy fluorescence spectroscopy and a GC–MS (Hewlett-Packard 6980/HP5973MSD). UV–vis absorbance was measured on a UV–vis spectrophotometer and oil concentration was calculated using a calibration plot which was obtained with known oil concentration samples.

Acute Toxicity Assays

Microtox Acute Toxicity Test

Toxicity to the bioluminescent organism Aliivibrio fischeri (also called Vibrio fischeri or V. fischeri) was assayed using the Microtox measuring system according to DIN 38412L34, L341, (EPS 1/ RM/24 1992). Microtox testing was performed according to the standard procedure recommended by the manufacturer [161]. A specific strain of the marine bacterium, V. fischeri-Microtox LCK 491 kit was used for the Microtox acute toxicity assay. Dr. LANGE LUMIX-mini type luminometer was used for the microtox toxicity assay [162].

Daphnia magna Acute Toxicity Test

To test toxicity, 24-h born Daphnia magna were used as described in Standard Methods sections 8711A, 8711B, 8711C, 8711D and 8711E, respectively [163]. After preparing the test solution, experiments were carried out using 5 or 10 Daphnia magna introduced into the test vessels. These vessels had 100 ml of effective volume at 7.0– 8.0 pH, providing a minimum dissolved oxygen (DO) concentration of 6 mg/l at an ambient temperature of 20–25°C. Young Daphnia magna were used in the test (≤24 h old); 24–48 h exposure is generally accepted as standard for a Daphnia magna acute toxicity test. The results were expressed as mortality percentage of the Daphnia magna. Immobile animals were reported as dead Daphnia magna.

Statistical Analysis

ANOVA analysis of variance between experimental data was performed to detect F and P values. The ANOVA test was used to test the differences between dependent and independent groups, [164]. Comparison between the actual variation of the experimental data averages and standard deviation is expressed in terms of F ratio. F is equal (found variation of the date averages/expected variation of the date averages). P reports the significance level, and d.f indicates the number of degrees of freedom. Regression analysis was applied to the experimental data in order to determine the regression coefficient R2, [165]. The aforementioned test was performed using Microsoft Excel Program.

All experiments were carried out three times and the results are given as the means of triplicate samplings. The data relevant to the individual pollutant parameters are given as the mean with standard deviation (SD) values.

Results and Dıscussıons

A Novel g-C3N4/CeO2 NCs Characteristics

The Results of X-Ray Diffraction (XRD) Analysis

The results of XRD analysis was observed to pure g-C3N4 NPs, pure CeO2 NPs and g-C3N4/CeO2 NCs, respectively, in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal (Figure 1). The characterization peaks were observed at 2θ values of 14.21°, 20.12° and 28.24°, respectively, implying pure g-C3N4 NPs in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal (Figure 1a). The characterization peaks were obtained at 2θ values of 29.41°, 34.22°, 48.45°, 57.62°, 59.27°, 70.18°, 77.17° and 79.31°, respectively, implying pure CeO2 NPs in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal (Figure 1b). The characterization peaks were found at 2θ values of 13.20°, 28.72°, 33.67°, 48.15°, 58.39°, 60.16°, 71.17°, 75.35° and 79.53°, respectively, and which can also be indexed as (100), (002), (111), (200), (220), (311), (222), (400), (331) and (420), respectively, implying g-C3N4/CeO2 NCs in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal (Figure 1c).

fig 1

Figure 1: The XRD patterns of (a) pure g-C3N4 NPs (black pattern), (b) pure CeO2 NPs (blue pattern) and (c) g-C3N4/CeO2 NCs (red pattern), respectively, in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal.

The Results of Diffuse Reflectance UV-Vis Spectra (DRS) Analysis

The absorption spectra of OFX was observed in DRS Analysis (Figure 2). First, the absorption spectra of OFX were obtained at a maximum concentration of 40 mg/l in the wavelength range from 250 nm to 800 nm using diffuse reflectance UV-Vis spectra (Figure 2). Absorption peaks were observed at wavelengths of 400 nm for pure g-C3N4 NPs (black pattern) (Figure 2a), 310 nm for pure CeO2 NPs (green pattern) (Figure 2b), and 340 nm for g-C3N4/CoMoO4 NCs (blue pattern) (Figure 2c), respectively, in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal.

fig 2

Figure 2: The DRS patterns of (a) pure g-C3N4 NPs (black pattern) (b) pure CeO2 NPs (green pattern) and (c) g-C3N4/CeO2 NCs (blue pattern), respectively, in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal.

The Results of Field Emission Scanning Electron Microscopy (FESEM) Analysis

The morphological features of pure g-C3N4 NPs, pure CeO2 NPs and g-C3N4/CeO2 NCs were characterized through FE-SEM images (Figure 3). The FESEM images of pure g-C3N4 NPs were obtained in in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal (Figure 3a). The FESEM images of pure CeO2 NPs were observed in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal (Figure 3b). The FESEM images of g-C3N4/CeO2 NCs were characterized in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal (Figure 3c).

fig 3

Figure 3: FESEM images of (a) pure g-C3N4 NPs, (b) pure CeO2 NPs and (c) g-C3N4/CeO2 NCs, respectively, in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal.

The Results of Energy Dispersive X-Ray (EDX) Spectroscopy Analysis

The EDX analysis was also performed to investigate the composition of g-C3N4/CeO2 NCs (Figure 4), respectively, in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal.

fig 4

Figure 4: EDX spectrum of g-C3N4/CeO2 NCs, respectively, in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal.

The Results of Fourier Transform Infrared Spectroscopy (FTIR) Analysis

The FTIR spectrum of pure g-C3N4 NPs (black spectrum), pure CeO2 NPs (blue spectrum) and g-C3N4/CeO2 NCs (red spectrum), respectively, in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal (Figure 5). The main peaks of FTIR spectrum for pure g-C3N4 NPs (black spectrum) was observed at 1645 1/cm, 1564 1/cm, 1411 1/cm, 1321 1/cm, 1240 1/cm and 807 1/cm wavenumber, respectively (Figure 5a). The main peaks of FTIR spectrum for pure CeO2 NPs (blue spectrum) was obtained at 462 1/cm wavenumber, respectively (Figure 5b). The main peaks of FTIR spectrum for g-C3N4/CeO2 NCs (red spectrum) was determined at 462 1/cm wavenumber, respectively (Figure 5c).

fig 5

Figure 5: FTIR spectrum of (a) pure g-C3N4 NPs (black spectrum), (b) pure CeO2 NPs (blue spectrum) and (c) g-C3N4/CeO2 NCs (red spectrum), respectively, in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal.

The Results of Transmission Electron Microscopy (TEM) Analysis

The TEM images of g-C3N4/CeO2 NCs was observed in micromorphological structure level in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal (Figure 6).

fig 6

Figure 6: TEM images of g-C3N4/CeO2 NCs in micromorphological structure level in pharmaceutical industry wastewater with photocatalytic degradation process for OFX antibiotic removal.

The Reaction Kinetics of OFX Antibiotic

The reaction kinetics OFX were investigated using the Langmuir–Hinshelwood first-order kinetic model, expressed by Eddy et al. [166], as following Equation (1):

for 1

where; ro: denotes the initial photocatalytic degradation reaction rate (mg/l.min), and k: denotes the rate constant of a first-order reaction. At the beginning of the reaction, t = 0, Ct = C0, the equation can be obtained after integration as following Equation (2):

for 2

where; C0 and C : are the initial and final concentration (mg/l) of OFX; the solution at t (min) and k (1/min) are the rate constant.

The correlation coefficients had R2 values greater than 0.9, as a result, the first-order kinetic model fit the experimental data well. The first-order rate constants (k) were determined from the slope of the linear plots.

Photocatalytic Degradation Mechanisms

The photocatalytic performance of the catalyst in the degradation of OFX is determined by photons. The degradation mechanism of OFX by hydroxyl radicals (OH) radicals concerning g-C3N4/CeO2 NCs as following equations (Equation 3, Equation 4, Equation 5, Equation 6, Equation 7, Equation 8, Equation 9 and Equation 10):

for 3-10

g-C3N4/CeO2 NCs absorbs photons with energies greater than the photocatalyst bandgap. As a result, the electron in the valence band (VB) jumps to the conduction band (CB), leaving a hole in the CB. The electrons present in the CB and VB will react with oxygen (O2) and water (H2O) molecules which are absorbed by the photocatalyst and lead to the formation of OH radicals which react with OFX. OH radicals are produced when the photocatalyst surface is illuminated with photons, and OH radicals are strong oxidising species, with an oxidation potential of approximately 2.8 V [as opposed to Normal hydrogen electrode (NHE)], which may increase total pollutant mineralisation. Normally, the higher the rate of formation of OH radicals, the greater the separation efficiency of electron-hole pairs. In this way, there is a correlation between the increased photocatalytic activity and the rate of formation of OHradicals. The OH radicals generation of g-C3N4/CeO2 NCs was extremely high, indicating that the sample has a high electron and hole separation rate.

CeO2 composites with g-C3N4 are also promising photocatalytic materials with a lower band gap energy [167-169] and significantly higher photocatalytic efficiency in degradation processes [170,171]. Considering the position of CB and VB in CeO2 and g-C3N4, the higher photocatalytic efficiency can be attributed to the transfer of photoexcited electrons and holes between CeO2 and g-C3N4, which suppresses the recombination of photogenerated h+/e pairs. During irradiation, photogenerated electrons on CB in g-C3N4 are transferred to CB in CeO2 and react with O2, while photogenerated holes on VB in CeO2 are transferred to VB in g-C3N4 and react with H2O according to the following reactions [172]:

The superoxide and hydroxyl radicals formed in the above-presented reactions take part in the degradation of pollutants. In the case of CeO2 composites with g-C3N4, two problems have still not been resolved. The first one is related to the lower rates of TOC or COD decrease in wastewater in comparison with the degradation rate of pollutants [173]. The second one is attributed to the immobilization of a composite photocatalyst, which could eliminate the post-treatment process of photocatalyst removal from the wastewater.

Effect of Increasing pH values for OFX Removal in Pharmaceutical Industry Wastewater during Photocatalytic Degradation Process

Increasing pH values (pH=3.0, pH=4.0, pH=6.0, pH=7.0, pH=9.0 and pH=11.0, respectively) was examined during photocatalytic degradation process in pharmaceutical industry wastewater for OFX removal (Figure 7). 67.2%, 85.7%, 96.4%, 56.5% and 44.8% OFX removal efficiencies was measured at pH=3.0, pH=4.0, pH=6.0, pH=7.0 and pH=11.0, respectively, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at 25°C (Figure 7). The maximum 99% OFX removal efficiency was obtained during photocatalytic degradation process in pharmaceutical industry wastewater, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively (Figure 7).

fig 7

Figure 7: Effect of increasing pH values for OFX removal in pharmaceutical industry wastewater during photocatalytic degradation process, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively.

Effect of Increasing OFX Concentrations for OFX Removal in Pharmaceutical Industry Wastewater during Photocatalytic Degradation Process

Increasing OFX concentrations (5 mg/l, 10 mg/l, 20 mg/l and 40 mg/l) were operated at 300 W UV-vis irradiation power, after 180 min photocatalytic degradation time, at pH=6.0, at 25°C, respectively (Figure 8). 85.3%, 94.1% and 77.2% OFX removal efficiencies were obtained to 5 mg/l, 10 mg/l and 40 mg/l OFX concentrations, respectively, at pH=6.0 and at 25°C (Figure 8). The maximum 99% OFX removal efficieny was found with photocatalytic degradation process in pharmaceutical industry wastewater, at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively (Figure 8).

The percentage decrease (8%) in the concentration of OFX during the studies under the dark conditions was due to the contaminant adsorption onto the catalyst surface [174]. The formation of contaminant monolayer on the surface of the catalyst may have occupied all its active sites, and therefore no more adsorption was observed.

fig 8

Figure 8: Effect of increasing OFX concentrations for OFX removal in pharmaceutical industry wastewater during photocatalytic degradation process, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively.

Effect of Increasing g-C3N4/CeO2 NCs Concentrations for OFX Removals in Pharmaceutical Industry Wastewater during Photocatalytic Degradation Process

Increasing g-C3N4/CeO2 NCs concentrations (1 mg/l, 2 mg/l, 4 mg/l, 6 mg/l, 8 mg/l and 10 mg/l) were operated at 20 mg/l OFX, at 150 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0, at 25°C, respectively (Figure 9). 54.5%, 68.1%, 75.8%, 87.3% and 92.1% OFX removal efficiencies were obtained to 1 mg/l, 2 mg/l, 4 mg/l, 6 mg/l and 10 mg/l g-C3N4/CeO2 NCs concentrations, respectively, at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0, at 25°C, respectively (Figure 9). The maximum 99% OFX removal efficieny was measured to 8 mg/l g-C3N4/CeO2 NCs with photocatalytic degradation process in pharmaceutical industry wastewater, at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively (Figure 9).

fig 9

Figure 9: Effect of increasing g-C3N4/CeO2 NCs concentrations for OFX removal in pharmaceutical industry wastewater during photocatalytic degradation process, at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively.

Effect of Different g-C3N4/CeO2 NCs Mass Ratios for OFX Removals in Pharmaceutical Industry Wastewater during Photocatalytic Degradation Process

Different g-C3N4/CeO2 mass ratios (5/5wt, 6/4wt, 7/3wt, 8/2wt, 9/1wt, 1/9wt, 2/8wt, 3/7wt and 4/6wt, respectively) were examined for OFX removal in pharmaceutical industry wastewater during photocatalytic degradation process, at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively (Figure 10). 80.3%, 84.6%, 77.9%, 62.1%, 48.4%, 55.2%, 64.0% and 79.7% OFX removal efficiencies were measured at 5/5wt, 6/4 wt, 7/3wt, 8/2wt, 9/1wt, 1/9wt, 3/7wt and 4/6wt g-C3N4/CeO2 NCs mass ratios, respectively, at 20 mg/l OFX after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively (Figure 10). The maximum 99% OFX removal efficiency was measured at 2/8wt g-C3N4/CeO2 NCs mass ratios at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively (Figure 10).

fig 10

Figure 10: Effect of different g-C3N4/CeO2 NCs mass ratios for OFX removal in pharmaceutical industry wastewater during photocatalytic degradation process, at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively.

Effect of Different Recycle Times for OFX Removals in Pharmaceutical Industry Wastewater during Photocatalytic Degradation Process

Different recycle times (1., 2., 3., 4., 5., 6. and 7.) were operated for OFX removals in pharmaceutical industry wastewater during photocatalytic degradation process, at 20 mg/l OFX, 8 mg/l g-C3N4/CeO2 NCs, at 2/8wt g-C3N4/CeO2 NCs mass ratio, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively (Figure 11). 97.5%, 96.2%, 94%, 93.8%, 89.2%, 86.2% and 80.1% OFX removal efficiencies were measured after 2. recycle time, 3. recycle time, 4. recycle time, 5. recycle time, 6. recycle time and 7. recycle time, respectively, at 20 mg/l OFX, 8 mg/l g-C3N4/CeO2 NCs, at 2/8wt g-C3N4/CeO2 NCs mass ratio, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively (Figure 11). The maximum 99% OFX removal efficiency was measured in pharmaceutical industry wastewater during photocatalytic degradation process, after 1. recycle time, at 20 mg/l OFX, 8 mg/l g-C3N4/CeO2 NCs, at 2/8wt g-C3N4/CeO2 NCs mass ratio, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively (Figure 11).

fig 11

Figure 11: Effect of recycle times for OFX removal in pharmaceutical industry wastewater during photocatalytic degradation process, at 20 mg/l OFX, 8 mg/l g-C3N4/CeO2 NCs, at 2/8wt g-C3N4/CeO2 NCs mass ratio, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively.

Acute Toxicity Assays

Effect of Increasing OFX Concentrations on the Microtox (Aliivibrio fischeri or Vibrio fischeri) Acute Toxicity Removal Efficiencies in Pharmaceutical Industry Wastewater at Increasing Photocatalytic Degradation Time and Temperature

In Microtox with Aliivibrio fischeri (also called Vibrio fischeri) acute toxicity test, the initial EC90 values at pH=7.0 was found as 825 mg/l at 25°C (Table 3: SET 1). After 60 min, 120 min and 180 min photocatalytic degradation time, the EC90 values decreased to EC57=414 mg/l to EC22=236 mg/l and to EC12=165 mg/l in OFX=20 mg/l at 30°C (Table 3: SET 3). The Microtox (Aliivibrio fischeri) acute toxicity removal efficiencies were 40.86%, 79.75% and 90.86% after 60 min, 120 min and 180 min, respectively, in OFX=20 mg/l and at 30°C (Table 3: SET 3).

The EC90 values decreased to EC51, to EC16 and to EC6 after 60 min, 120 min and 180 min, respectively, in OFX=20 mg/l, at 60°C (Table 3: SET 3). The EC51, the EC11 and the EC7 values were measured as 550 mg/l, 540 mg/l and 500 mg/l, respectively, in OFX=20 mg/l at 60°C. The toxicity removal efficiencies were 46.41%, 85.30% and 96.41% after 60 min, 120 min and 180 min, respectively, in OFX=20 mg/l, at 60°C (Table 3: SET 3). 96.41% maximum Microtox (Aliivibrio fischeri) acute toxicity removal yield was found in OFX=20 mg/l after 180 min and at 60°C (Table 3: SET 3).

The EC90 values decreased to EC62=422 mg/l to EC21=241 mg/l and to EC17=168 mg/l after 60 min, 120 min and 180 min, respectively, in OFX=5 mg/l at 30°C (Table 3: SET 3). The EC90 values decreased to EC62=421 mg/l to EC27=239 mg/l and to EC11=167 mg/l after 60 min, 120 min and 180 min, respectively, in OFX=10 mg/l at 30°C. The EC90 values decreased to EC67=408 mg/l to EC32=230 mg/l and to EC22=162 mg/l after 60 min, 120 min and 180 min, respectively, in OFX=40 mg/l at 30°C. The Microtox (Aliivibrio fischeri or Vibrio fischeri) acute toxicity removals were 85.30%, 85.28% and 79.75% in 5 mg/l, 10 mg/l and 40 mg/l OFX, respectively, after 180 min, at 30°C. It was obtained an inhibition effect of OFX=40 mg/l to Vibrio fischeri after 180 min and at 30°C (Table 3: SET 3).

The EC90 values decreased to EC57=419 mg/l to EC22=266 mg/l and to EC12=150 mg/l after 60 min, 120 min and 180 min, respectively, in OFX=5 mg/l at 60°C (Table 3: SET 3). The EC90 values decreased to EC57=414 mg/l to EC22=232 mg/l and to EC12=161 mg/l after 60 min, 120 and 180 min, respectively, in OFX=10 mg/l at 60°C. The EC90 values decreased to EC62=403 mg/l to EC27=218 mg/l and to EC17=148 mg/l after 60 min, 120 and 180 min, respectively, in OFX=40 mg/l at 60°C. The Microtox (Aliivibrio fischeri or Vibrio fischeri) acute toxicity removals were 90.86%, 90.83% and 85.30% in 5 mg/l, 10 mg/l and 40 mg/l OFX, respectively, after 180 min, at 60°C. It was observed an inhibition effect of OFX=40 mg/l to Microtox with Vibrio fischeri after 180 min, and at 60°C (Table 3: SET 3).

Table 3: Effect of increasing OFX concentrations on Microtox (Aliivibrio fischeri) acute toxicity in pharmaceutical industry wastewater after photocatalytic degradation process, at 30°C and at 60°C, respectively.

No

Parameters

Microtox (Aliivibrio fischeri) Acute Toxicity Values, * EC (mg/l)

25°C

0 min

60 min

120 min

180 min

*EC90

*EC

*EC

*EC

1 Raw ww, Control

825

EC70=510

EC60=650

EC49=638

30°C

60°C

0. min

60 min

120. min

180. min

0 min

60 min

120 min

180 min

*EC90

*EC

*EC

*EC

*EC90

*EC

*EC

*EC

2 Raw ww, control

825

EC70=580

EC50=580

EC39=548

825

EC55=550

EC40=590

EC29=688

3 OFX=5 mg/l

825

EC62=422

EC27=242

EC17=168

825

EC57=419

EC22=266

EC12=150

OFX=10 mg/l

825

EC62=421

EC27=239

EC17=167

825

EC57=414

EC22=232

EC12=161

OFX=20 mg/l

825

EC57=414

EC22=236

EC12=165

825

EC52=550

EC17=540

EC7=500

OFX=40 mg/l

825

EC67=408

EC32=230

EC22=162

825

EC62=403

EC27=218

EC17=148

* EC values were calculated based on CODdis (mg/l).

Effect of Increasing OFX Concentrations on the Daphnia magna Acute Toxicity Removal Efficiencies in Pharmaceutical Industry Wastewater at Increasing Photocatalytic Degradation Time and Temperature

The initial EC50 values were observed as 850 mg/l at 25°C (Table 4: SET 1). After 60 min, 120 and 180 min photocatalytic degradation time, the EC50 values decreased to EC31=350 mg/l to EC17=240 mg/l and to EC12=90 mg/l in OFX=20 mg/l, at 30°C (Table 4: SET 3). The toxicity removal efficiencies were 42.96%, 72.87% and 82.65% after 60 min, 120 min and 180 min, respectively, in OFX=20 mg/l at 30°C (Table 4: SET 3).

The EC50 values decreased to EC27 to EC12 and to EC7 after 60 min, 120 min and 180 min, respectively, in OFX=20 mg/l at 60°C (Table 4: SET 3). The EC27, the EC12 and the EC7 values were measured as 150 mg/l, 60 mg/l and 375 mg/l, respectively, in OFX=20 mg/l at 60°C. The toxicity removal efficiencies were 52.94%, 82.62% and 92.36% after 60 min, 120 min and 180 min, respectively, in OFX=20 mg/l at 60°C (Table 4: SET 3). 92.38% maximum Daphnia magna acute toxicity removal was obtained in OFX=20 mg/l after 180 min and at 60°C, respectively (Table 4: SET 3).

The EC50 values decreased to EC37=450 mg/l to EC22=145 mg/l and to EC17=260 mg/l after 60 min, 120 min and 180 min, respectively, in OFX=5 mg/l at 30°C (Table 4: SET 3). The EC50 values decreased to EC37=450 mg/l to EC22=175 mg/l and to EC17=100 mg/l after 60 min, 120 min and 180 min, respectively, in OFX=10 mg/l and at 30°C. The EC50 values decreased to EC42=300 mg/l to EC27=170 mg/l and to EC22=52 mg/l after 60 min, 120 min and 180 min, respectively, in OFX=40 mg/l and at 30°C. The Daphnia magna acute toxicity removals were 72.22%, 72.56% and 63.21% in 5 mg/l, 10 mg/l and 40 mg/l OFX, respectively, after 180 min and at 30°C. It was observed an inhibition effect of OFX=40 mg/l to Daphnia magna after 180 min and at 30°C (Table 4: SET 3).

The EC50 values decreased to EC32=130 mg/l to EC17=425 mg/l and to EC12=340 mg/l after 60 min, 120 min and 180 min, respectively, in OFX=5 mg/l and at 60°C (Table 4: SET 3). The EC50 values decreased to EC32=425 mg/l to EC17=140 mg/l and to EC7=90 mg/l after 60 min, 120 min and 180 min, respectively, in OFX=10 mg/l and at 60°C. The EC50 values decreased to EC37=250 mg/l to EC22=110 mg/l and to EC17=10 mg/l after 60 min, 120 min and 180 min, respectively, in OFX=40 mg/l and at 60°C. The Daphnia magna acute toxicity removals were 83.06%, 92.65% and 73.11% in 5 mg/l, 10 mg/l and 40 mg/l OFX, respectively, after 180 min and at 60°C. It was observed an inhibition effect of OFX=40 mg/l to Daphnia magna after 180 min and at 60°C (Table 4: SET 3).

Increasing the OFX concentrations from 5 mg/l to 40 mg/l did not have a positive effect on the decrease of EC50 values as shown in Table 4 at SET 3. OFX concentrations > 20 mg/l decreased the acute toxicity removals by hindering the photocatalytic degradation process. Similarly, a significant contribution of increasing OFX concentration to acute toxicity removal at 60°C after 180 min of photocatalytic degradation time was not observed. Low toxicity removals found at high OFX concentrations could be attributed to their detrimental effect on the Daphnia magna (Table 4: SET 3).

Table 4: Effect of increasing OFX concentrations on Daphnia magna acute toxicity in pharmaceutical industry wastewater after photocatalytic degradation process, at 30°C and at 60°C.

 

No

 

Parameters

Daphnia magna Acute Toxicity Values, * EC (mg/l)

25°C

0. min

60. min

120. min

180. min

*EC50

*EC

*EC

*EC

1 Raw ww, control

850

EC45=625

EC40=370

EC29=153

30°C

60°C

0 min

60 min

120 min

180. min

0. min

60. min

120. min

180. min

*EC50

*EC

*EC

*EC

*EC50

*EC

*EC

*EC

2 Raw ww, control

850

EC39=468

EC34=228

EC23=111

850

EC34=373

EC29=210

EC18=71

3 OFX=5 mg/l

850

EC32=450

EC22=145

EC17=260

850

EC32=130

EC17=425

EC12=340

OFX=10 mg/l

850

EC37=450

EC22=175

EC17=100

850

EC32=425

EC17=140

EC7=90

OFX=20 mg/l

850

EC32=350

EC17=240

EC12=90

850

EC27=150

EC12=60

EC7=375

OFX=40 mg/l

850

EC42=300

EC27=170

EC22=52

850

EC37=250

EC22=110

EC17=11

* EC values were calculated based on CODdis (mg/l).

Direct Effects of OFX Concentrations on the Acute Toxicity of Microtox (Aliivibrio fischeri or Vibrio fischeri) and Daphnia magna without Pharmaceutical Industry Wastewater after Photocatalytic Degradation Process

The acute toxicity test was performed in the samples containing 5 mg/l, 10 mg/l, 20 mg/l and 40 mg/l OFX concentrations, at 25°C room temperature. In order to detect the direct responses of Microtox (Aliivibrio fischeri or Vibrio fischeri) and Daphnia magna to the increasing OFX concentrations the toxicity test were performed without pharmaceutical industry wastewater after photocatalytic degradation process, at 25°C room temperature. The initial EC values and the the EC50 values were measured in the samples containing increasing OFX concentrations after 180 min photocatalytic degradation time. Table 5 showed the responses of Microtox (Aliivibrio fischeri or Vibrio fischeri) and Daphnia magna to increasing OFX concentrations.

The acute toxicity originating only from 5 mg/l, 10 mg/l, 20 mg/l and 40 mg/l OFX were found to be low (Table 5). 5 mg/l OFX did not exhibited toxicity to Aliivibrio fischeri (or Vibrio fischeri) and Daphnia magna before and after 180 min photocatalytic degradation time. The toxicity atributed to the 10 mg/l, 20 mg/l and 40 mg/l OFX were found to be low in the samples without pharmaceutical industry wastewater after photocatalytic degradation process for the test organisms mentioned above. The acute toxicity originated from the OFX decreased significantly to EC2, EC4 and EC6 after 180 min photocatalytic degradation time. Therefore, it can be concluded that the toxicity originating from the OFX is not significant and the real acute toxicity throughout photocatalytic degradation process was attributed to the pharmaceutical industry wastewater, to their metabolites and to the photocatalytic degradation by-products (Table 5).

Table 5: The responses of Microtox (Aliivibrio fischeri or Vibrio fischeri) and Daphnia magna acute toxicity tests in addition of increasing OFX concentrations without phamaceutical industry wastewater during photocatalytic degradation process after 180 min photocatalytic degradation time, at 25°C room temperature.

 

 

OFX Conc. (mg/l)

Microtox (Aliivibrio fischeri or Vibrio fischeri)

Acute Toxicity Test

Daphnia magna

Acute Toxicity Test

Initial Acute Toxicity EC50 Value (mg/l)

Inhibitions after 180 min photocatalytic degradation time

EC Values (mg/l)

Initial Acute Toxicity EC50 Value (mg/l)

Inhibitions after 180 min photocatalytic degradation time

EC Values (mg/l)

5

EC10=24

EC10=39

10

EC15=79

3

EC2=3

EC20=99

5

EC3=5

20

EC20=149

5

EC4=6

EC30=199

6

EC6=11

40

EC25=219

7

EC6=9

EC40=299

9

EC8=15

The Comparison with Other Scientific Studies in the Literature

Comparison of our study “The use of a novel graphitic carbon nitride/cerium dioxide (g-C3N4/CeO2) nanocomposites for the ofloxacin removal by photocatalytic degradation in pharmaceutical industry wastewaters and the evaluation of microtox (Aliivibrio fischeri) and Daphnia magna acute toxicity assays” with other scientific studies in the literature is summaried at Table 6 [175-182].

Table 6: The Comparison with other Scientific Studies in the Literature

Photocatalyst

Experimental Conditions (for maximum removal efficiencies)

Experimental Results

References

a-Bi2O3/g-C3N4 [DOX]=10 mg/l, [Material]=500 mg/l, [H2O2]=10 mM, Unadjusted pH, Xe lamp (150 W). 79.1% DOX (30 min) (Liu et al., 2021a)
Ag/AgCl@ZIF-8/g-C3N4 150 W, Xe, λ > 420 nm, 50 mg/l, [LVFX]=10 mg/l, V=50 ml, 87.3% LVFX (60 min) (Zhou et al., 2019)
Ag@ZIF-8/g-C3N4 300 W, Xe, λ > 420 nm, [Ten antibiotics] =10 mg/l, V=50 ml 90% (60 min) (Guo et al., 2022)
Peroxymonosulfate/ZnFe2O4 Waters e2695 HPLC instrument (Milford, USA), UV-Vis detector λ=294 nm [OFX]=1000 mg/l, pH=6.0 80.9% OFX (30 min), pH 6.0 (Sun et al., 2021b)
Bi2WO6 and g-C3N4 nanosheets [CRO]=16.5-66 µM, KrCl excilamp, λ=222 nm, 23 W, incident irradiance 0.74 mW/cm2, 60 min 91% Ceftriaxone (60 min) (Sizykh et al., 2023)
Bi2WO6/g-C3N4 [CRO]=100 mg/l 300 W Xe lamp, 94.5% Ceftriaxone (120 min) (Zhao et al., 2018)
CeO2-ZnO hetero photocatalyst [TCN]=100 mg/l, 300 W Xenon lamp, 87.25% Tetracycline (60 min) (Ye et al., 2016)
g-C3N4/CeO2 core-shell structure Hydrothermal method, [DOX]=1000 mg/l, HCl=10 mg/l, H2O2=100 µl, 150 W Xe lamp (λ > 400 nm), g-C3N4=2.82 eV, CeO2=2.76 eV 66.7%g-C3N4, 71.7%CeO2,

84% g-C3N4/CeO2 (60 min)

(Liu et al., 2019)
CeO2/ATP/g-C3N4 ATP—attapulgite Electrostatic-induced self-assembly method, Dibenzothiophene (DBT), m(catal)/m(DBT)=1/10, SO₂=200 mg/l, 30% H2O2, 300 W Xe lamp (λ > 420 nm) Desulfurization 42% g-C3N4,

83% CeO2/g-C3N4, 98% CeO2/ATP/g-C3N4 (180 min)

(Li et al., 2017b)
g-C3N4/CeO2 NCs g-C3N4/CeO2 NCs was prepared to hydrothermal calcination method, CeO2 was prepared sol-gel method, g-C3N4 was prepared to calcination method, pH=6.0,

[OFX]=20 mg/l,

[g-C3N4/CeO2 NCs]=8 mg/l, g-C3N4/CeO2 mass ratio=2/8,

300 W UV-vis A lamp λ=350-400 nm range (λmax=368 nm; FWHM=17 nm; Actinic BL TL-D 18W, Philips)

Recycle time=7, at 25°C Microtox (Aliivibrio fischeri) and Daphnia magna acute toxicity assays

99% OFX (180 min, at 25°C)

96.41% maximum Microtox (Aliivibrio fischeri) acute toxicity removal (180 min, at 60°C)

92.38% maximum Daphnia magna acute toxicity removal (180 min, at 60°C),

99% OFX after 1. recycle time

This study
DOX: doxycycline; LVFX: Levofloxacin; CRO: ceftriaxone; TCN: Tetracycline; OFX: ofloxacin

Conclusıons

The maximum 99% OFX removal efficiency was obtained during photocatalytic degradation process in pharmaceutical industry wastewater, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively.

The maximum 99% OFX removal efficieny was found with photocatalytic degradation process in pharmaceutical industry wastewater, at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively.

The maximum 99% OFX removal efficieny was measured to 8 mg/l g-C3N4/CeO2 NCs with photocatalytic degradation process in pharmaceutical industry wastewater, at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively.

The maximum 99% OFX removal efficiency was measured at 2/8wt g-C3N4/CeO2 NCs mass ratios at 20 mg/l OFX, at 300 W UV-vis light irradiation power, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively.

The maximum 99% OFX removal efficiency was measured in pharmaceutical industry wastewater during photocatalytic degradation process, after 1. recycle time, at 20 mg/l OFX, 8 mg/l g-C3N4/CeO2 NCs, at 2/8wt g-C3N4/CeO2 NCs mass ratio, after 180 min photocatalytic degradation time, at pH=6.0 and at 25°C, respectively.

96.41% maximum Microtox (Aliivibrio fischeri) acute toxicity removal yield was found in OFX=20 mg/l after 180 min and at 60°C. It was observed an inhibition effect of OFX=40 mg/l to Microtox with Vibrio fischeri after 180 min photocatalytic degradation time and at 60°C. 92.38% maximum Daphnia magna acute toxicity removal was obtained in OFX=20 mg/l after 180 min photocatalytic degradation time and at 60°C, respectively. It was observed an inhibition effect of OFX=40 mg/l to Daphnia magna after 180 min photocatalytic degradation time and at 60°C. OFX concentrations > 20 mg/l decreased the acute toxicity removals by hindering the photocatalytic degradation process. Similarly, a significant contribution of increasing OFX concentrations to acute toxicity removal at 60°C after 180 min photocatalytic degradation time was not observed. Finally, it can be concluded that the toxicity originating from the OFX is not significant and the real acute toxicity throughout photocatalytic degradation process was attributed to the pharmaceutical industry wastewater, to their metabolites and to the photocatalytic degradation process by-products.

As a result, the a novel g-C3N4/CeO2 NCs photocatalyst during photocatalytic degradation process in pharmaceutical industry wastewater was stable in harsh environments such as acidic, alkaline, saline, and then was still effective process. When the amount of contaminant was increased, the a novel g-C3N4/CeO2 NCs photocatalys during photocatalytic degradation process performance was still considerable. The synthesis and optimization of g-C3N4/CeO2 heterostructure photocatalyst provides insights into the effects of preparation conditions on the material’s characteristics and performance, as well as the application of the effectively designed photocatalyst in the removal of antibiotics, which can potentially be deployed for purifying wastewater, especially pharmaceutical wastewater. Finally, the combination of a simple, easy operation preparation process, excellent performance and cost effective, makes this a novel g-C3N4/CeO2 NCs a promising option during photocatalytic degradation process in pharmaceutical industry wastewater treatment.

Acknowledgement

This research study was undertaken in the Environmental Microbiology Laboratories at Dokuz Eylül University Engineering Faculty Environmental Engineering Department, Izmir, Turkey. The authors would like to thank this body for providing financial support.

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Stock Effect of Bio-Economic Indicators in an Over- exploited Fishery of the Gulf of Mexico

DOI: 10.31038/AFS.2023513

Abstract

The main bio-economic indicators of a pelagic fishery of the Northern Gulf of México (the Gulf Menhaden Brevoortia patronus Goode), was examined to understand their 8 performance as a result of simulated trials of the age of first catch and the fishing mortality. First, the validity of simulation was tested rebuilding numerically the performance of biological data and catch over time. A simple approach was made assigning economic value to the catch per-kg and the cost of fishing, so the output of biological variables could be linked to their corresponding economic performance, under the dynamics of the exploited stock, before adding value to the catch. As a part of results, the historical trend of a declining yield, suggests that the fishery has been over exploiting the juveniles, and even tough this condition has been sustained for more than forty years, this process produces nearly 400 thousand t, while it could yield more than one million t if it exploits only adult fish. On testing the economic indicators of the stock response as effect of the age of first catch, it is evident that the current yield is well below the Maximum Sustainable Yield, which might be higher if only adult fish are the targets of the fishery. The same occurs with the Maximum Economic Yield. The Benefit/Cost displays an inverse relationship with the Cost per t. It was found that the fishery could profit more than three hundred million USD if the age of first catch is re-addressed to get only adults as fisheries target. It is clear that this approach could be adopted as useful tool for decision-making and fisheries management.

Keywords

Stock assessment, Maximum sustainable yield, Maximum economic yield, Overexploited fishery, Gulf menhaden

Introduction

One of the problems of fisheries management deals with use of formal procedures of evaluation of exploited stocks in order to derive quantitative regulations able to foresee accurate consequences after the application of certain management actions [1-5]. However, a few years ago, it was stated that fisheries overexploitation and unsustainability are still not widely understood [6], despite cascade effects have been reported [7,8]. Fortunately, after twenty years, with much scientific research work done on this problem, it is much better understood. Therefore, there are multiple examples of mismanagement of fisheries resources, with regrettable consequences leading to the over exploitation of many fish resources and their consequences in their environment [9-12]. In the present paper, populations are evaluated by reconstructing the age structure of each of the years analyzed. The potential catch, benefits, direct jobs, and earnings per fisher can be estimated in several scenarios by changing the fishing mortality F, and the age of first catch, tc. In this way it is possible to test the response of the biologic and socio-economic variables of each fishery with reference to the maximum sustainable yield MSY, and the maximum economic yield MEY.

Many Fisheries at a worldwide level, have been declared chronically overexploited [13]. However, stock assessments and management regulations are usually addressed towards limitations of access, reduction of fishing seasons, reduction of fishing effort, establishment of closed areas, etc., but fisheries scientists as advisers and managers do not usually pay attention in the effects of mesh openings as means to control the age of first catch (tc), allowing to catch only adults of the fish stock and giving to juveniles usually caught, the opportunity to survive to the adult age and having the chance to breed at least only once in their lifetime. An examination of the population parameter values of several fisheries shows that tc value is at least one year lower than the age of first maturity ™. This is a circumstance that unavoidably leads these fisheries towards a condition of overexploitation, which in the case of the Gulf Menhaden and other cases, becomes a chronical condition that often leads to a biological and economic crisis. It is remarkable to find out that management regulations usually ignore the need to increase mesh openings to allow juvenile fish being released from the nets and capturing only adults [14].

Methods

The assessment of the Menhaden stock was made by using a simulation model [15,16]; is based on the general principles of the assessment of exploited fish stocks and is conducted with usually fifteen years of catch data. Thus, with the purpose of formulating better management options, a meta-analysis of data was conducted  to evaluate the performance of the fisheries with reference to the output of this model. In each of these options, catch data and the values of the population parameters are used, from the references    or estimated directly [17-20] and are indicated in Table 1. The associated costs and economic benefits of the fishery are taken as a reference for the bio-economic analysis. The model proposed allows testing of as many exploitation possibilities as fishing data allow, in a dynamic programming exercise that can provide answers to logical questions such as: What will happen to the biomass of the stock and the economic yield if the size of first capture is increased? What will be the biological and economic consequences if fishing effort is doubled? What is the maximum effort that the fishery can sustain and fail to deliver benefits of at least 10 percent above costs? And what are the economic expectations for the next season if the cost of fuel increases in a certain proportion? Population parameter values used as input of the simulation and the corresponding equations are in Table 1.

Table 1: Population parameter values, units, equations and source or comments used for the evaluation of the Gulf Menhaden fishery are indicated.

tab 1

Among the results obtained with the use of this model, the evaluations carried out indicate that for a combination of tc and F values, the estimated performance describes a dome-shaped response surface; if a single value of tc is taken and the response of the stock is observed, the yield is shown as a curve that at certain F level attains a maximum value and declines after this point. The output also describes the number of jobs as a function of F as a line with the same trend as that of potential capture; the benefit/cost ratio is a curve that declines as F increases. In general, the MSY level is at a higher value of F than in the case of the economic yield (MEY). In high-value fisheries, such as lobster, this value coincides with MSY at the same F. In addition, it is remarkable to find out that the cost of fishing increases with higher F intensity, making the activity unprofitable with higher values.

For the economic analysis of the resource, it is necessary to feed the model with data such as the number of fishing days that each season lasts on average, the number of boats and the number of fishers per boat. The total costs are obtained by multiplying the costs/ship/ day by the total number of ships in operation. Ideally, estimates of economic data are made after examining the fishing log from a trading trip [21]. The maximum social value can be determined in two ways, the first is the level of maximum employment (the maximum number of fishers). The second is the maximum profit per fisher. The economic and social values as input data were the value per kilogram landed and the number of fishers during the last fishing season. It is desirable to use a long series of economic data, but these variables though exist, they are not easily available nor collected in a systematic way like those of the catch and effort and for now the estimate that is made by the model roughly reconstructs the economic history of the fishery, with the risk of incurring in certain errors. This problem will cease when a diagnosis of the current situation be made as a basis for the rationale and future management of the resource.

Benefits are determined by subtracting total costs from the total value of the catch. Costs and value are linked to the catch and the other variables in the model. The populations are evaluated by reconstructing the age structure of each one during the series of years of the analyzed data. The potential catch, benefits, direct jobs, and profits per fisher are estimated under the scenarios sought, changing the F and the tc. In this way, it is possible to test the response of the socio-economic variables of the fishery with reference to MSY and MEY. In this context, benefits are obtained by subtracting total costs from the total value of the catch; costs and value are linked to the catch and the other variables in the model.

It is amazing to find out that more than six decades ago Beverton & Holt [22] stated the principle that yield tends to increase with higher values of the age of first catch and displayed this in the well-known figure of yield per recruit. In addition, with the advent of computers and profuse modelling, it is not understood why this problem has not been tackled by fisheries scientists in the following years after that paper. Therefore, this essay was written with the purpose of showing evidence that for any exploited stock, there is a MSY value which is the maximum catch that can be extracted from an exploited stock in the long term, as one of the many equilibrium values that any fishery can have. It is pertinent to mention that in some cases there are huge differences between the MSY and the optimum yield (OY), which is the maximum harvest producing the highest benefit indefinitely. OY is a particular case of the equilibrium MSY values, corresponding to the highest yield that an exploited stock can produce. In addition, when economic values are explicitly considered, it is possible to talk about the MEY, which is closely equivalent to the MSY, but values  of these variables do not coincide at the same F value. Fishing effort was not explicitly considered in this paper, based on the amount of noise usually implicit in it; instead, the spread sheet allowed that catch equation was fitted backwards and most of the significant amount of uncertainty disappeared in the stock assessment process.

The Gulf Menhaden

Despite the distribution range of the Gulf menhaden  spreads over the Gulf of Mexico, and beyond, the fishery takes place in the brackish-waters of the Mississippi river delta, where the coastal areas contain high Cla values, in contrast with the low Cla content along the southern Gulf, where there is much lower productivity (an order of magnitude lower than along the northern Gulf) [23], which does not allow the high stock biomass of this fish as along the vicinity of the Mississippi river delta.

It is pertinent to mention that the catch trend shows an even decline since 1987 (data after Gedar 03 2021) [24], with 640 thousand t in 1987 to 414 thousand t in 2020, as shown in Figure 1, which was drawn to display the fitting process of catch and reconstructed data as a part of the model calibration. No fishing effort data were used to avoid the noise implicit in its use. Population structure was rebuilt in the simulation by applying backwards the stock assessment equations.

fig 1

Figure 1: Model fitting of the catch and assessment of the biomass of the Gulf menhaden for the years 1977-2020

Once the population structure was rebuilt with the current parameters of the fishery, successive trials were applied to each age of first catch of the simulated stock, and this way the stock response could be measured. With the purpose of having an economic output, explicit consideration of the catch value before landing, the number of boats, the catch per trip, and the cost per trip were taken into consideration. The analysis presented in this paper deals within the scope of the so- called stock effect [25,26], it refers to the idea that unit operating costs are sensitive to the size of the exploited fish stocks; in other words, the analysis is referred to the performance of bio-economic indicators inside the fishery, before landing the catch.

With this information in the model, and by knowing the stock response as consequence of different values of the F, it was possible to determine the potential catch, the profits, the benefit/cost ratio,  the MSY, the MEY, the best tc, and other bio-economic variables useful for fisheries management,  produced  as  model  outputs.  As it was stated before, population parameter values were  obtained from FishBase, and Gedar 03 2021. Estimation of some population parameter values were obtained with the aid of Froese 2006; Froese & Binohlan 2000 [27].

Results

Profits and Benefit/Cost Ratio

Despite its declining trend, the Gulf menhaden is a very productive economic activity, displaying profits above 160 M USD in 1987 to around 70 M USD in the year 2020 (Figure 2). The same statement is valid for the Benefit/Cost, whose values (times the cost of fishing) range from 86 in 1986 to 47 in 2011. During the last five years of the series, the economic activity displayed a significant increase up to 116 in 2020 (Figure 2).

fig 2

Figure 2: Trend of profits, in USD and B/C ratio of the Gulf menhaden fishery since 1977

Optimum Yield

The MSY use to be the target of many fisheries; however, it is not usually mentioned that it is not a fixed parameter, it is a variable which depends on the age  of  first  catch  and  therefore  there  are as many MSY values as age groups are in a given stock, being the optimum that one which is at or near the oldest age class in the fishery; in this case it would be the catch of 3.3 M t at the age of eight years profiting 527 M USD at the same age. For obvious reasons, it would be no practical the application of tc = 8 years and the most convenient option could be choosing seven years instead. Any other values are sustainable and are maximum for each age class before the last one. Under the MSY (Figure 3A) and the MEY (Figure 3B), the stock responds the same way and both variables display in an analogous way as a function of tc.

fig 3

Figure 3: Maximum sustainable yield (MSY), 3A, and Maximum economic yield (MEY), 3B, of the Gulf menhaden fishery as a function of tc. In the first case the units are metric tons (t) and in the second case the units are Million USD.

Economic Variables

As a result of the analysis, it was found that there is an inverse relationship between the costs of exploitation and the B/C ratio (Figure 4). This is an evident condition, because it is logical to expect that the exploitation of the fishery is subject to higher costs when the stock is less abundant and vice versa.

fig 4

Figure 4: Relationship between the Benefit/Cost ratio (B/C) and the Costs of exploitation per t (C/t, USD) in the Gulf menhaden fishery. Each dot represents a tc value, being the first two overlapped on the right end of line trend, corresponding to tc ages of 1 and 2 years. The horizontal axis indicates the cost per t.

Under-Exploited or Over-Exploited?

The main reason why the consideration that the Gulf menhaden is overfished, as stated in the title of the present paper, is because from the viewpoint of the author, based on this and previous analysis, the stock is exploited as overexploited of recruits in a condition such that is shared by many fisheries around the world [28-32] and there are countless examples evidencing this problem. The analysis of this and other fisheries lead to the conclusion that the main reason for the over exploitation of recruits may be economic, because often occurs in pelagic stocks which are very productive and display high turnover rate. They are often linked to high economic value, product of high catch volumes, as it is the case of the Gulf menhaden; this fishery is very productive for its high landings and for its high profits. Then, it has been exploited for long time and the yield shows a declining trend to the point of capturing near 400 thousand t per season in the last few years, as compared to the landings of near one million t per season recorded in the middle eighties. It is amazing to realize that nobody has pointed this situation before, despite that the historical decline of catch is an evident fact and the teams in charge of evaluations refuse to accept this condition of the fishery, stating that “the Gulf of Mexico menhaden stock is not experiencing overfishing and is not overfished” (Gedar 03 2021). The authors of the present paper believe that the main reason why nobody has called the attention on the condition   of an overexploited fishery is because the menhaden has been very profitable for many decades, confirming what was stated above. Then if nobody pays attention on this problem, the condition will continue until the turnover rate of recruits becomes critical, the stock biomass to be not enough to replace the stock and the fishery to become unprofitable [33]. This is evidence confirming that the Gulf menhaden is under a condition of overexploitation of recruits [34], a problem common to many other fisheries [35-39].

As result of the analysis, it was found that a nearly four times higher catch could be obtained by applying F = 0.4, as shown in Figure 5, where the exploitation rate (E) and the F estimated for the years 1977-2020 are displayed. It is pertinent to mention that it is  not desirable to increase the F in an overexploited stock because the number of recruits would get exhausted in a few more fishing seasons and the whole fishery would fall into a collapse in brief time.

fig 5

Figure 5: Historic trend of the F (bars) and the E (dotted line) in the menhaden fishery for the period 1977 – 2020.

Numerical analysis and simulation of fisheries systems allow estimating potential yields, amongst other options; one of them deals with the possibility of doing a long-term forecast of the expected performance of the stock under different exploitation policies, with a very reasonable accuracy. In this case, after rebuilding the structure of the population, it is possible to estimate the expected potential yield by application as many feasible management options of F and the tc in a modern and flexible approach of the Beverton- Holt (1957) yield per recruit method. In this study case, three age classes, one, three- and five-years old fish were used as an example to demonstrate in first place, that the current fishery is overexploited of recruitment by applying a tc = 1, as shown in Figure 6. The trend line of each one of the three age classes selected here for demonstration display the outputs of the expected yield as a function of F. In the last few years of catch records, the F value estimated is F = 0.11 and the yield is Y = 414,730 t; then by looking at the expected potential yield by exploiting only adults (tc = 3 and tc = 5), would allow a much higher stock biomass to catch, and the F could be three times the current one being able to yield more than 1.1 M t, without the risk of depleting the fishery.

Horizontal lines showing the FMSY and the EMSY values are indicated as reference, showing that the fishery is exploited below the limit reference points since 1988. This is an apparent situation, because by increasing the F above the current values, the yield would decrease instead of increasing (Figure 6).

fig 6

Figure 6: A. Expected yield of the Gulf menhaden fishery under three scenarios, as a function of fishing mortality. The lowermost line corresponds to the current condition with tc=1, whose maximum yield could hardly produce 670 thousand t at the maximum at F=0.4. By contrast, after applying the same F, with tc=3, the fishery would yield 904 thousand t; with tc=5, the fishery would harvest 1.3 M t. In the last two cases, only adult fish would be exploited. B. Economic performance of the fishery by expressing the profits in Million USD as a dependent variable. In the current condition (tc=1) the maximum profit is $104 M. By applying the same F, with tc=3 the fishery would produce $142 M; with tc=5, the fishery would profit $218 M. Other variables are the same as in Figure 6A.

Discussion

By examining the causes of over exploitation of the most productive world fisheries, it is generally aknowledged that the most common problems of overexploitation of a fishery may occur after the application of excessive fishing effort, by overexploitation of recruits or both, leading to an excess of fishing capacity [40-43], despite clear recommendations and reference points are defined [44,45]. This is the case of the Gulf menhaden, exploited in the northern Gulf of Mexico, and whose huge biomass makes it one of the most productive fish stocks at world level (Myers & Worm 2003).

Production of the Gulf menhaden fishery was briefly examined because of the Deep-Sea Horizon oil spill in 2010 awakened the interest on it by the authors, but an impact of the oil spill on its stock biomass immediately after that event was not evident. A ban was temporarily imposed to the fishery during this disaster, but the fishery continued shortly afterwards. However, no evidence of depletion could be observed in the stock biomass if there was any, as it is not shown in Figure 1.

Overexploitation of fish stocks is a major concern in many world fisheries. It is the case for not just the Gulf menhaden, but it occurs in many others and it has been pointed as one of the reasons why   the world fisheries production display decreasing trends since more than fifty years ago [40,41], claiming for urgent rebuilding of stocks, Despite reference points and statements on the management have been provided (Caddy 1999; Caddy & Mahon 1995).

It has been stated that close to 90% of the world’s marine fish stocks are fully exploited, overexploited or depleted, threatening the chance of renewal of stock biomass, because the gradual reduction  of production capacity of the stocks to the point that a stock may be exhausted becoming incapable to restore its biomass as consequence of the lack of enough reproducers, compromising the sustainability of a fishery [42-44]. There are several causes leading fisheries to    an overexploited condition, like illegal fishing, subsidies, fishing overcapacity and degradation of environment, as the more common ones. The effects of overexploitation are often expressed as social and economic crises on the harbours and ports where the reduced catches are landed, and where much infrastructure and services stop being  in use leaving many people out of jobs. Contrary to what has been expressed in most of stock assessments [45], in this paper the use of fishing effort data was deliberately ignored, but once the model was fitted, it was possible to do an estimation of the number of fishing days, without the noise that is usually implicit in the current stock assessment procedures.

An undesirable perspective of the current condition of the Gulf menhaden, is maybe the worst case of a more general problem, biomass overexploitation not necessarily expresses the more critical consequence of this fishery, which has been gradually overexploiting its juveniles for decades. In this activity, the age groups caught by the fishery include since the age class of one year of age, but the stock reaches the age of sexual maturity at the age of two years; this implies that the fishing gears are catching all age classes. By consequence, the portion of the stock caught by the fishing gears include juveniles that otherwise would have the chance of reaching the adulthood   and contributing to replace the stock with the products of their reproduction. In the Gulf menhaden fishery, catch trend over time displays a slight but consistent decline, evidencing the effect of a gradual reduction of the population turnover rate, which as far as it persists without change, eventually would lead the fishing activity into a crisis, becoming unprofitable, because the cost of fishing would make the fishery unviable. A reduction of the stock biomass would lead to an increase of fishing because it will make the fishery more expensive, to the point of reaching the economic equilibrium limit, this is when the fishing stops being profitable.

In order to conclude this paper, it is considered that despite the Gulf menhaden still is a productive fishery, with profits near to one hundred million USD, it could profit more than three hundred million USD if the owners of the fishing fleet decide to open the meshes of fishing gears, and the age of first catch is re-addressed to get only adults as target of the fishery. Evidently, the adoption of this fishing strategy would imply some previous trials of selectivity using several mesh openings, and results could allow choosing the most suitable one [46-52]. However, in order to achieve the expected goals in the desired size-frequency of the new catch, the adoption of the new mesh size should be applied to the whole fishing fleet, so the new selectivity can have impact on the whole exploited stock; otherwise it would not have the expected effect. It is considered that the use of the new mesh sizes, may take a couple of years to achieve the expected results.

Author Credit Statement

EC and ACH developed the paper concept; EC created the draft and structure of the paper; both authors contributed to writing and editing.

Declaration of Competing Interest

The authors declare that they have not known competing financial interests or personal relationships that could have appeared to to influence the work reported in this paper.

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Prevalance and Risk Factors of Hypogonadism in Male Patients with Type 2 Diabetes in El Minya, Egypt

DOI: 10.31038/EDMJ.2023711

Abstract

Background: Hypogonadism in adult men is a clinical and biochemical syndrome associated with low level of testosterone, which may adversely affect multiple organ functions and quality of life. It is closely related to the development of diabetes. This study was designed to determine the incidence of hypogonadism and related risk factors among men with type 2 diabetes (T2D).

Patients and Methods: A total of 300 male patients diagnosed with T2D age from 30-70 years were enrolled in the study. Arabic version of the Androgen Deficiency in Aging Male (ADAM) questionnaire was employed to assess the androgen insufficiency in men. Hemoglobin A1c, FSH, LH, total and free testosterone, levels were measured by enzyme immunoassay.

Results: T2D patients were divided into two groups: 48 (16%) patients with hypogonadism and 252 (84%) patients without hypogonadism. Multiple logistic regression analysis for factors affecting Hypogonadism among patients according to (total testosterone + ADAM +ve) versus those without hypogonadism it was found that age, random blood sugar, body mass index (BMI), Hb A1c are independent risk factors for the development of hypogonadism with odds ratio (0.95, 1, 1.1, 1.37) with p value (0.02, 0.03, 0. 03, 0.008) respectively. The ROC analysis of the accuracy of indices and cut off values for the studied total testosterone for predicting the hypogonadism according to total testosterone + ADAM score positive: The AUC was 0.98 “p-value <0.0001” with sensitivity 100% and specificity of 96.4% at Cut off value ≤ 12.

Conclusion: Several risk factors of diabetes are associated closely with hypogonadism. Age, BMI, blood sugar, and Hb A1c are independent risk factors for the development of hypogonadism in male patients with T2D.

Introduction

Diabetes mellitus is a metabolic disorder characterized by the presence of hyperglycemia due to defective insulin secretion, defective insulin action or both. The chronic hyperglycemia of diabetes is associated with relatively specific Long-term complications from high blood sugar can include macrovascular complications as coronary artery disease, cerebrovascular strokes and chronic limb ischemia and there is microvascular complication as diabetic retinopathy, chronic kidney disease which may require regular dialysis, and diabetic neuropathy [1].

Type 2 diabetes (T2D) is ranging from predominantly insulin resistance with relative insulin deficiency to predominantly an insulin secretory defect with insulin resistance. This form of diabetes, which accounts for 90-95% of those with diabetes, previously referred as non-insulin dependent diabetes, type 2 diabetes, or adult onset diabetes, encompasses individuals who have insulin resistance and usually have relative (rather than absolute) insulin deficiency. This is probably many different causes of this form of diabetes although the specific etiologies are not known. Most patients with this form of diabetes are obese, and obesity itself causes some degree of insulin resistance [2].

Hypogonadism (testosterone deficiency) in adult men is a clinical and biochemical syndrome associated with low level of testosterone, which may adversely affect multiple organ functions and quality of life [3]. Overt hypogonadism was defined as the presence of clinical symptoms of hypogonadism and low testosterone level (total testosterone <8 nmol/l and/or bioavailable testosterone <2.5 nmol/l). Borderline hypogonadism was defined as the presence of symptoms and total testosterone of 8-12 nmol/l or bioavailable testosterone of 2.5-4 nmol/l [4].

From the cross-sectional studies done, it is clear that between 20% and 64% of men with diabetes have hypogonadism; generally, there is a slow and continuous decrease in testosterone production among older population. Furthermore, the prevalence of hypogonadism varies between racial, ethnic groups [5]. A high incidence of hypogonadism in men with (T2D) has been globally reported. This study was designed to determine the incidence of hypogonadism and related risk factors among men with T2D in Minia governorate, Egypt.

Patients and Methods

This prospective cross sectional study included a total of 300 T2D patients; all patients gave verbal consent to participate in the study. Patients were selected from those coming for follow up at different Minia University and Ministry of Health hospitals in the period between January 2018 and June 2019. Patients are known to have type 2 diabetes mellitus according to criteria of American diabetic association [6].

The criteria of diagnosis of hypogonadism in our work is low level of testosterone (12 nmol/L total testosterone 3.5 ng/mL) represents a reliable threshold to diagnose late onset hypogonadism (LOH) or free testosterone <5.7 pg/ml) and positive result of screening of ADAM questionnaire [3,7]. Hypogonadism was classified as primary hypogonadism (total testosterone >12 nmol/L and LH<10 IU\L) and secondary hypogonadism ( total testosterone >12 nmol/L and LH >10 IU/L).

Inclusion Criteria

Male patients, diagnosed with diabetes mellitus type 2, age from 30-70 years and on oral therapy or insulin or both.

Exclusion Criteria

Any patient with any of the following criteria: a history of hypopituitarism, type 1 diabetes mellitus, chronic debilitating diseases, chronic inflammatory diseases, or connective tissue disorders, take any medications affecting glucose metabolism as (steroids, anti-psychotic medications), malignancy, autonomic neuropathy, or patients on testosterone replacement therapy.

All subjects were subjected to full history taking and thorough clinical examination. All the patients were required to complete an Arabic version of the Androgen Deficiency in Aging Male (ADAM) questionnaire designed by the Saint Louis University, MO, USA, 2007. This 10-item screening questionnaire was employed to assess the androgen insufficiency in aging men, including morning erection to exclude psychogenic erectile dysfunction. A positive response denoted the presence of clinical hypogonadism based on a decrease in libido, strength of erections, or any three nonspecific questions that may include a decrease in muscle strength, fatigability, mood changes, and loss of height.

The following laboratory investigations were performed: blood glucose level, renal and liver function tests, complete lipogram using fully automated clinical chemistry auto-analyzer system Konelab 20i (Thermo-Electron Incorporation, Finland). Hemoglobin A1c (glycated hemoglobin), FSH, LH, total and free testosterone, levels were measured by enzyme immunoassay.

Study sample size: the number of study participants was calculated using EPI – Info (statistical; software for epidemiology) depend on population number (diabetic patients) and percentage of disease (prevalence of hypogonadism).

Statistical Analysis

Normality of data distribution was done by using Shapiro-wilk test. Descriptive statistics, such as percentages, frequencies, mean, and standard deviations, were used to measure the demographic variables, clinical and laboratory data. Analytical statistics were applied to investigate the association of the demographic variables, clinical and laboratory data and hypogonadism. Quantitative data were presented by mean (standard deviation), while qualitative data were presented by frequency distribution. The independent sample t-test used for comparison of means and the Chi-square test was used to compare between proportions.

Logestic regression analyses were performed to identify the significant predictors (independent risk factors) for hypogonadism (target dependent factor) The probability of less than 0.05 was used as a cut off point for all significant tests and all statistical tests were 2 tailed. The receiver operating characteristic (ROC) curve is the plot that displays the full picture of trade-off between the sensitivity (true positive rate) and (1-specificity) (false positive rate) across a series of cut-off points. Total area under ROC curve is a single index for measuring the performance a test. The larger the AUC, the better is overall performance of the medical test to correctly identify diseased and non-diseased subjects. All analyses were done using the statistical Package of social Science (SPSS, version 22).

Results

This study included a total of 300 men with T2D. Their socio-demographic characteristics are shown in Table 1. T2D patients were divided into two groups: 48 (16%) patients with hypogonadism and 252 (84%) patients without hypogonadism. 68.8% of the hypogonadal group were in age group 41-60 years with p value 0.009 , 43.3% of hypogonadal group were smokers with p value 0.05, and 25% sere Ex-smoker, 68.8% of the hypogonadal group were on oral treatment of diabetes mellitus (p=0.05), 18.8% were on insulin, 56.3% of hypogonadal group were diabetic 6-10 years with (p=0.0001), 81.3% of hypogonadal group were obese with (p=0.001). The group of hypogonadism show higher BMI, waist circumference grades, waist/hip ratio and waist/height ratio with (p=0.001, =0.008, >0.0001, =0.03) respectively (Table 2).

Table 1: Sociodemographic data of whole patient with type 2 diabetes mellitus

Socio-demographic characteristics

Mean ± SD or N (%)

Age (years)

54.46 ± 9.46 (31-70)

Age groups

30-40 (years)

41-50 (years)

51-60 (years)

61-70 (years)

 

33 (11%)

60 (20%)

126 (42%)

81 (27%)

Smoking

Non smoker

Smoker

Ex-smoker

 

141 (47%)

96 (32%)

63 (21%)

Type of treatment of DM

Lifestyle

Insulin

Oral antidiabetic

Mixed (Insulin + oral)

 

21 (7%)

60 (20%)

180 (60%)

39 (13%)

Complication of DM

No

Yes

 

198 (66%)

102 (34%)

Classification of complication :

Neuropathy

Stroke

IHD

Retinopathy

Nephropathy

 

39 (13% )

12 (4%)

27 (9%)

21 (7%)

3 (1%)

Hypertension

No

Yes

 

204 (68%)

96 (32%)

Duration of diabetes (years)

7.73 ± 6.76 (0.1-27)

Duration of diabetes ranges

≤ 5 (years)

6-10 (years)

11-15 (years)

< 15(years)

 

156 (52%)

66 (22%)

51 (17%)

27 (9%)

BMI (KGm/M2)

31.22 ± 6.56 (21.1-40.8)

BMI grades (KGm/M2)

≤ 24.9 (KGm/M2) average

25-29.9 (KGm/M2) overweight

≥ 30 (KGm/M2) obesity

 

27 (9%)

102 (34%)

171 (57%)

Waist circumference (Cm)

101.62 ± 13.67 (69-135)

Waist circumference grades (Cm)

< 102 (Cm)

≥ 102 (Cm)

 

84 (28%)

216 (72%)

Waist/hip ratio

0.97 ± 0.06 (0.81-1.15)

Waist/height ratio

60.53 ± 7.71 (43-79)

BMI: Body mass index, KGm: Kilogram, M: Meter, CM: Centimeter

Table 2: Sociodemographic characteristics in type 2 DM patients with hypogonadism according to (Total Testosterone + ADAM +ve) versus those without hypogonadism.

Socio-demographic characteristics

Hypogonadism
N=48

No hypogonadism
N=252

p-value

Age (years)

52.87 ± 9.03

54.76 ± 9.52

0.2

Age groups

31-40 (years)

 41-50 (years)

 51-60 (years)

 61-70 (years)

 

3 (6.3%)

18 (37.5%)

15 (31.3%)

12 (25%)

 

30 (11.9%)

42 (16.7%)

111 (44%)

69 (27.4%)

 

0.009*

Smoking

Non smoker

Smoker

Ex-smoker

 

15 (31.3%)

21 (43.8%)

12 (25%)

 

126 (50%)

75 (29.8%)

51 (20.2%)

 

0.05

Type of treatment of DM

Lifestyle

Insulin

Oral

Mixed

 

0 (0%)

6 (12.5%)

33 (68.8%)

9 (18.8%)

 

21 (8.3%)

54 (21.4%)

147 (58.3%)

30 (11.9%)

 

0.05

Complications of DM

No

Yes

 

33 (68.75%)

15 (31.25%)

 

165 (65.5%)

87 (34.5%)

 

0.01*

Hypertension

No

Yes

 

33 (68.8%)

15 (31.2%)

 

171 (67.9%)

81 (32.1%)

 

0.9

Duration of diabetes (years)

7.15± 3.73

7.84± 7.20

0.5

Duration of diabetes ranges

≤ 5 (years)

6-10 (years)

11-15 (years)

< 15(years)

 

15 (31.2%)

27 (56.3%)

6 (12.5%)

0 (0%)

 

141 (56%)

39 (15.5%)

45 (17.9%)

27 (10.6%)

 

 

>0.0001*

BMI (KGm/M2)

32.97 ± 7.88

30.89 ± 6.24

0.04*

BMI grades (KGm/M2)

≤ 24.9 Average

25-29.9 Overweight

≥ 30 Obesity

 

0 (0%)

9 (18.8%)

39 (81.3%)

 

27 (10.7%)

93 (36.9%)

132 (52.4%)

 

0.001*

Waist circumference grades (cm)

< 102

≥ 102

 

6 (12.5%)

42 (87.5%)

 

78 (31%)

174 (69%)

 

 

0.008*

Waist/hip ratio

1.01 ± 0.07

0.96 ± 0.05

<0.0001*

Waist/height ratio

62.70 ± 4.68

60.11± 8.10

0.03*

*Significant level of p-value is < 0.05.
* p value of frequency was calculated by using chi-square test.
* p value of means was calculated by using independent sample t-test.

In term of complications of diabetes mellitus, our results demonstrated no significant differences between the two studied groups (Table 3). Table 4 shows the comparison between routine investigations and hypogonadism of hypogonadism according to (Total Testosterone + ADAM +ve) versus those without hypogonadism: It was significant in urea, creatinine, eGFR, SGPT with p value (0.01, >0.0001, 0.02, >0.0001). As regard glycemic control non of our hypogonadal patients have HbA1c >7%, but in the non hypogonadal group 33.3% have HbA1c >7 and 66.7% have HbA1c <7%, with p value 0.0001. Regarding the relation between specific investigations and hypogonadism according to total testosterone it was significance in free testosterone, total testosterone, FSH, ADAM score with p value of 0.001, > 0.0001, 0.003, > 0.0001 respectively (Table 5).

Table 3: The classification of complications between the hypogonadism according to (Total Testosterone + ADAM +ve) versus those without hypogonadism.

Diabetic complications

Hypogonadism
N=48

No hypogonadism
N=252

p-value

All number of patients complaint of complications

15(31.25%)

87 (34.5%)

Neuropathy

6 (12.5%)

33 (13.09%)

0.9

Stroke

3 (6.25%)

9 (3.57%)

0.4

Ischemic heart disease

3 (6.25%)

24 (9.52%)

0.6

Retinopathy

3 (6.25%)

18 (7.14%)

0.8

Nephropathy

0 (0%)

3(1.19%)

0.4

Table 4: Comparison of Routine investigations in hypogonadism group according to (Total Testosterone + ADAM +ve) versus those without hypogonadism.

Routine investigations

Hypogonadism
N=48

No hypogonadism
N=252

p-value

Urea (mg/dL)

36.87 ± 9.71

32.42 ± 11.28

0.01*

Creatinine(mg/dL)

1.08 ± 0.19

0.95 ± 0.22

<0.0001*

eGFR(ml/min/1.73 M2)

82.96 ± 19.95

89.79 ± 18.57

0.02*

SGOT(Iu/L)

23.56 ± 11.95

24.20 ± 15.73

0.7

SGPT (Iu/L)

27.68 ± 13.24

20.01 ± 8

<0.0001*

HbA1C (%)

10.01 ± 1.60

8.39 ± 2.17

<0.0001*

HbA1C ranges (%)

< 7

≥ 7

 

0 (0%)

48 (100%)

 

84 (33.3%)

168 (66.7%)

 

<0.0001*

HDL (mg/dL)

35.37 ± 6.40

37.34 ± 9.57

0.2

LDL (mg/dL)

149.43 ± 41.47

142.27 ± 42.69

0.3

TGS (mg/dL)

180.93 ± 51.75

202.05 ± 85.64

0.1

Cholesterol (mg/dL)

223.43 ± 41.01

224.96 ± 45.70

0.8

*Significant level of p- value is < 0.05.
*p value of means was calculated by using independent sample t-test.

Table 5: Relation between specific investigations and hypogonadism according to Total testosterone of the studied group.

Specific investigations

Hypogonadism
N=48

No hypogonadism
N=252

p-value

Free testosterone (pg/mL)

5.58 ± 3.25

7.42 ± 3.62

0.001*

Total testosterone (nmol/L)

9.09 ± 2.87

26.68 ± 12.63

<0.0001*

FSH(IU/L)

8.42 ± 4.26

9.25 ± 4.68

0.003*

LH(IU/L)

11 ± 5.45

12.72 ± 6.06

0.06

ADAM score

Positive

Negative

 

48 (100%)

0(0%)

 

150 (59.5%)

102 (40.5%)

 

<0.0001*

Total Testosterone level

Normal

Low

 

0 (0%)

48 (100%)

 

243 (96.4%)

9 (3.6%)

<0.0001*

*Significant level of p- value is < 0.05.
*p value of frequency was calculated by using chi-square test.
*p value of means was calculated by using independent sample t-test.

Table 6 shows multiple logistic regression analysis for factors affecting Hypogonadism among patients according to (Total Testosterone + ADAM +ve) versus those without hypogonadism it was found that age, random blood sugar, body mass index, HbA1c are independent risk factors for the development of hypogonadism with odds ratio (0.95, 1, 1.1, 1.37) with p value (0.02, 0.03, 0. 03, 0.008) respectively.

Table 6: Multiple logistic regression analysis for factors affecting Hypogonadism among patients according to (Total Testosterone + ADAM +ve) versus those without hypogonadism.

Independent variables

Adjusted odds for multivariate (95% CI)

P-value

Age (years)

0.95 (0.91-0.99)

0.02*

Random blood sugar (mg/dL)

1 (1-1.01)

0.03*

BMI(KGm/M2)

1.1 (1-1.19)

0.03*

Waist circumference (Cm)

1 (0.96-1.05)

0.8

HbA1c (%)

1.37 (1.08-1.74)

0.008*

HDL(mg/dL)

0.95 (0.91-1)

0.06

TGS (mg/dL)

0.99 (0.99-1)

0.2

FSH (IU/L)

0.94 (0.88-1)

0.08

LH (IU/L)

0.99 (0.91-1.08)

0.8

Figure 1 shows the receiver operating characteristic (ROC) analysis of demonstration of the accuracy of indices and cut off values for the studied total testosterone for predicting the hypogonadism according to total testosterone + ADAM score positive: The area under the curve (AUC) was 0.98 ” p-value <0.0001″ with sensitivity 100% and specificity of 96.4% at Cut off value ≤ 12. Figure 2 shows the ROC analysis of demonstration of the accuracy of indices and cut off values for the ADAM score for predicting the hypogonadism according to total testosterone + ADAM score positive: The AUC was 0.70 ” p-value <0.0001″ with sensitivity 100% and specificity of 40.5% at Cut off value<0.

fig 1

Figure 1: Roc curve analysis of total testosterone level in hypogonadism according to Total testosterone

fig 2

Figure 2: Roc curve analysis of ADAM score in hypogonadism according to Total testosterone

Figure 3 shows the ROC analysis of demonstration of the accuracy of indices and cut off values for the studied HbA1c for predicting the hypogonadism according to total testosterone + ADAM score positive: The AUC was 0.72 ” p-value <0.0001″ with sensitivity 87.5% and specificity of 58.3% at Cut off value < 8.7. Figure 4 shows the ROC analysis of demonstration of the accuracy of indices and cut off values for the studied FSH for predicting the hypogonadism according to total testosterone + ADAM score positive: The AUC was 0.60 ” p-value=0.02″ with sensitivity 75% and specificity of 48.8% at Cut off value < 17.

fig 3

Figure 3: Roc curve analysis of HbA1c level in hypogonadism according to Total testosterone

fig 4

Figure 4: Roc curve analysis of FSH level in hypogonadism according to Total Testosterone

Figure 5 shows the ROC analysis of demonstration of the accuracy of indices and cut off values for the studied eGFR for predicting the hypogonadism according to free testosterone + ADAM score positive: The AUC was 0.39 ” p-value 0.003″ with sensitivity 53.6% and specificity of 37% at Cut off value < 82.5.

fig 5

Figure 5: Roc curve analysis of e GFR in hypogonadism according to Free Testosterone

Discussion

Male hypogonadism is a common disease characterized by certain clinical features and low levels of serum testosterone. Its typical clinical manifestations include physical decline, memory loss, difficulty paying attention, depression, loss of libido, and erectile dysfunction. It significantly impacts patients’ quality of life [8]. Recently, studies have shown that hypogonadism is closely related to the development of diabetes [9]. It has been confirmed that male patients with T2D are significantly more likely to develop hypogonadism: the proportions of diabetes patients with low total testosterone levels are 36.5% [10]. Male hypogonadism seriously affects the quality of life in patients with diabetes [11,12]. So far, it is unclear which correlates of diabetes are associated with hypogonadism. Therefore, it is especially important to explore the risk factors for hypogonadism to facilitate prevention, diagnosis, and early treatment.

The current study was designed to evaluate the prevalence and risk factors o hypogonadism in patients with T2D among the egyptian population by using an ADAM questionnaire with the use both total and free testosterone level (≤ 12nmol, ≤5.7). We studied 300 male diabetic, according total testosterone level it showed that the prevalence of hypogonadism was 24.2% with of whom 56.3% was secondary hypogonadism and 43.7% was primary hypogonadism, according to free testosterone level it was found that the prevalence of hypogonadism was 42.42%, and secondary hypogonadism and 42.9% was primary hypogonadism. Similar to our results, the study of Dhindsa et al. [13] which was conducted in 103 patients by hypogonadism (33% in patients aged 28e80 years) and they reported that their high prevalence might be attributed to a higher mean BMI, Multicentre study was done in India reported a hypogonadism prevalence of 20.7% among patients with diabetes mellitus [4].

In our study we found that the prevalence of hypogonadism was higher with free testosterone cut of level (≤5.7 pg/mL) in comparison to total testosterone (≤12 nmol/L), similar to our findings the study of Rhoden et al. [14] which was cross-sectional study from Brazil who reported that that free and total testosterone levels were subnormal in 46% and 34% of diabetics respectively. In the present study hypogonadotropic hypogonadism is the predominant type of hypogonadism in our diabetic subjects. As, 43.7% had primary hypogonadism (LH > 10 MIU/ML) and 56.3% had secondary hypogonadism (LH < 10 MIU/ML); Similar to our finding, the study of Chandel et al. [15] found that LH and FSH concentrations in type 2 diabetic patients with low free testosterone concentrations were in the normal range. Tenover et al. [16] found that the majority of hypogonadal men over the age of 60 had low, or inappropriately normal LH levels. In contrary to our results the study of Ali et al. [17] who found high serum and urinary FSH and LH among diabetics with low serum total and serum free testosterone levels and Kapoor et al. [18] who found that 7% had hypogonadotropic hypogonadism.

Regarding to risk factors for development of hypogonadism, the present study should that age is important risk for development of hypogonadism. A higher prevalence of low total testosterone (69%) was seen in men aged between 60 and 70 years. This finding in agreement Grossmann et al. [19] who reported that 43% of men of the same age had low total testosterone. Many studies reported that the fraction of diabetic men with a subnormal level of total testosterone increased with age [20-22]. Although we found that lower testosterone level was found in older age groups but univariant relationship between total tesosterone and age is not there in similar studies conducted in South Africa and New York [13,23]. In contrast to this study, a study conducted in Jordan reported a significant positive correlation of age with TT [24]. Studies in England and Nigeria reported the presence of a significant negative correlation between age and TT level [25,26]. The most possible explanation for these inconsistencies is that serum hormone binding globulin (SHBG), which accounts for 60-80% of testosterone binding, increases with age. Yet low levels of SHBG may occur in the presence of insulin resistance, thus resulting in a decrease in TT levels. Therefore, in the absence of the assessment of bioavailable testosterone levels, the degree to which this confounder (SHBG) affected our results if at all is difficult to speculate on [27]. In the present study, there was significant association between the serum testosterone level and HbA1c concentration. This finding is consistent with the results obtained by other study Kapoor et al. [25], Our findings also contradict the finding of the study undertaken by Fukui et al. [22], who found that total testosterone concentrations correlated positively with HbA1c concentrations while opposing what was found by Grossmann et al. [21], and Dandona et al. [28].

In the present study, we observed negative association of serum testosterone levels with blood glucose markers including HbA1c values, which is in consistent with the studies of Fukui et al. [22], Rabia et al. [29], and Laaksonen et al. [30] where serum testosterone levels were shown to have negative association with glucose markers, Not only HbA1c levels but Insulin resistance indicators as BMI, waist/hip ratio and waist/height ratio among males have also been found to be related with lower levels of testosterone levels. Many such studies have confirmed that insulin resistance is found to be associated with low serum testosterone levels. The reason would be that the testosterone regulates GLUT-4 gene expression and other genes important for insulin signaling. Lower testosterone levels leads to decrease in the expression of GLUT-4 levels in muscles so reduction in the glycolytic enzyme activity in muscle, liver and abdominal adipose tissues [31,32]. Testosterone reduction also causes dysregulation of lipid metabolism which also increases the risk of developing diabetes [33,34].

Conclusion

Several risk factors of diabetes are associated closely with hypogonadism. Age, BMI, blood sugar, and Hb A1c are independent risk factors for the development of hypogonadism in male patients with T2D.

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Frames of Identity for Young People: A Mind Genomics Exploration

DOI: 10.31038/PSYJ.2023534

Abstract

This research explored the social frames through which young people form their “selves.” Young respondents (age 17-21) from either Alabama or New York each evaluated unique sets of vignettes, combinations of metaphors, descriptions of people or actions with which they could either identify or not identify. The vignettes were created using experimental design, with each of the 127 respondents evaluating a unique set of 24 such vignettes. Deconstruction of the vignettes into the contribution of the different elements revealed how each element drove the responses of either identification (‘like me/like others’) or differentiation (like me/not like others; not like me/like others). Clustering the respondents revealed three clear mind-sets, but only when the metric was ‘differentiation’. These young respondents fell into three clear groups based upon how they saw themselves as different from others. The three groups (mind-sets) are: (MS1=Feelings about people surrounding me; MS2=Feelings about gender; MS3=Feelings about my country).

Introduction

It appears to anyone viewing the news that United States is fracturing, fault lines appear everywhere, whether political, social, educational, religious, and so forth. An ancient Chinese curse is appropriate for this world: ‘may you live in interesting times.’ We are living in the interesting times. The issue is how we can understand the mind of people within these times when people are so conscious of the world around them through the public media, the internet, and the veritable flood of information threatening to drown us every day with its biases and hysterics.

Faced with the opportunity to study people in ‘interesting times’ the authors used the emerging science of Mind Genomics to explore the mind of young people ages 17-21, living in either New York or Alabama, two states deemed to be dramatically different from each other when one reads the tomes of statistics which attempt to quantify today’s life.

Mind Genomics is an emerging science which explores and systematizes the way people make decisions about ordinary daily issues, topics one might explore but usually does not because of the absolutely quotidian nature. For example, in previous Mind Genomics studies the topics explored ranged from what the third-grade mathematics class might be in ten years to what one should say about food to make it interesting. Studied or not, these are the kinds of everyday themes which shape lives, their ordinariness ensuring somehow that they escape the eye of science, although certainly they rarely escape the mouth of opinion [1,2].

Our science, Mind Genomics, particularly suited to study the mind of today, emerged from three interrelated strands of science and research.

a.  Experimental Psychology, More Specifically the Discipline of Psychophysics

Psychophysics attempts to link together psychological magnitude of perception (viz., the sweetness of a beverage) with physical stimuli (viz., the concentration of sweetener in the beverage). The underlying notion is the measurement of a percept. The traditional approach has been to have respondent evaluate stimuli of different physical magnitudes such solutions of sucrose (cane sugar) of different concentrations, or the perceived seriousness of crimes (REF), and the traditional effort of linking the magnitude of that percept to the measured magnitude of a physical stimulus associated with the stimuli. For the first, sugar in water, the physical stimulus is the concentration of the solution. For the second, seriousness of crimes, the physical stimulus is the punishment imposed by the court. This effort is what S.S. Stevens, late Professor of Psychophysics at Harvard, called ‘outer psychophysics’ [3]. Mind Genomics attempts to create what Stevens called the ‘inner psychophysics,’ measuring the strength of ideas.

b.  Statistics, Specifically the Discipline of Experimental Design

Experimental design enables the researcher to mix independent variables (elements or phrases about the topic) into combinations called vignettes, present these vignettes to people, elicit and record ratings of these vignettes, and then deconstruct the ratings of vignettes into the part-worth contribution of each of element. Experimental design ensures the proper set of combinations, created with the prospect of submitting the array of vignettes and responses to statistical analysis (regression modeling). Rather than working with single elements, rated one at a time, Mind Genomics works with combinations of verbal messages, the combination made according to an underlying system. Those mixtures simulate the compound and complex nature of our reality. Although one might think that simply asking a person to rate each idea one at a time would do just as well, the reality is that this ‘one at a time’ approach enables the respondent to adjust the criterion of judgment for each idea. When some ideas are emotion-laden the researcher might use a different judgment criterion than those cases were the ideas to be taken from a less emotional topic. Presenting the respondent with combinations of ideas, vignettes in the language of Mind Genomics, forestalls this tendency to subconsciously shift the criterion of judgment to be more appropriate for the nature of the phase or topic being rated. Creating combinations when it might be easier to evaluate single elements seems to be a great deal of effort, but the reality is that the evaluation of vignettes ends up producing more solid data, resulting from making it impossible for the respondent to ‘game’ the system (Craven & Islam, 2011; Easterling, 2015) [4,5].

c.  Consumer Science

The third source for Mind Genomics is the discipline of consumer science, the study of what consumers want, what they do and why they do it [6,7]. Consumer science is best exemplified by the nature of the work they do, which is to study the mind of the consumer, as that mind interacts with needs, information, and opportunities. Rather than working with artificial situations set up to demonstrate some principle, as is the practice of many psychology researchers, consumer science ends up working with what exists, or with a meaningful variation of what currently exists. It is the very focus on the quotidian, the daily, the ordinary, which has become the north star of Mind Genomics

Mind Genomics studies follow a template approach, the objective of which is to create an easy-to-implement experiment, along with an easy-to-understand set of results that anyone can use and build-upon. The goal is to democratize research, making it inexpensive, easy, fast, and iterative, with the ability to scale the study from a small sample of say 20-30 respondents in a local area to a world-wide study with dozens or more countries, each with hundreds of respondents. Rather than re-inventing the research process again and again, making the process specific for a topic, the development of Mind Genomics was done with the vision of creating a DIY, do-it-yourself knowledge-&-insight acquisition system. The approach is the ultimate in the much maligned, misunderstood, and overlook ‘cookie cutter approach.’ The vision underlying Mind Genomics was the industrial-scale creation of deep knowledge through systematized data structures.

Our focus in this paper is on what young people think about themselves in terms of descriptions. The description is not psychology nor behavior, but rather what types of general ‘metaphors’ best describe them. General metaphors encapsulate a great deal of descriptive information into a phrase. The result is that we can learn about the ‘hooks of identity’ for young people.

We proceed now with the exploratory study. As the data will reveal, one study can lead to dozens, each just as easily implemented as this, exploring a new world efficiently, and with the excitement to spark and maintain the interest of students as well as professionals.

Method

The Mind Genomics program is embodied in a website, www.BimiLeap.com, openly available, with the only charges being the processing costs (including recruitment of respondents, if desire). All screen shots come from that website.

Step 1: Give the Study a Name (Figure 1, Left Panel)

This step may seem vacuous, but it is not. Naming a study forces the researcher to think about the issues. The name of this study reflects that thinking. The effort to name the study ended up producing a simple, non-descriptive name, Study 1, because the author struggled without success to create a shortened name. It is worth noting that the more fundamental studies of Mind Genomics end up being hard to name, and that experienced difficulty in naming a study is itself something to explore. Either one does not know the topic, has not thought deeply about the topic, or perhaps the topic is going into very new areas which are terra incognita and cannot really be encapsulated by a name (Figure 1).

FIG 1

Figure 1: Set up for a Mind Genomics study. Left panel = assign the study a name. Middle panel = provide four questions which tell a story. Right panel = provide four answers to each question.

Step 2: Create four questions or topics which are logically connected, or which tell a story (Figure 1, middle panel)

The questions explore different aspects of the topic. The respondent never sees the questions. Rather, the questions are developed by the research (and as of 2023, with the aid of AI), to create the framework of answers The four questions can be considered both a way to guide the researcher, and the abovementioned bookkeeping device, which ensure that two or more answers of the same type (viz., from the same question) never appear in the same vignette. This bookkeeping action will be important when the Mind Genomics process creates vignettes, combinations of answers.

Step 3: For Each Question Provide Four Different Answers (Figure 1, Right Panel)

The answers must address the question. They may be mutually contradictory answers because they will never appear together. Ideally, the answers should consist of phrases rather than single words. The phrases should paint a word picture, if possible. Once the questions are selected the answers are straightforward to create, unless the topic requires technical knowledge. In most cases, however, by the time the researcher has created the four questions, the four sets of answers are easy to develop. The hard thinking has been done already in the act of constructing the questions.

Table 1 presents the questions and the answers. As noted above, the questions are abbreviated phrases rather than full questions. Since the questions are not shown to the respondent the abbreviated format of the questions makes little practical difference to the study itself. The answers are simple phrases which create a word picture, but one of a very general nature. The answers could be refined and particularized should the researcher wish to do so. In this study it was sufficient to put in a general phrase.

Table 1: The four questions (topics), and the four answers (elements) for each question

TAB 1

Step 4: Create Test Vignettes according to an Underlying Experimental Design

Mind Genomics ‘works’ by creating combinations of elements (messages, answers), testing these combinations (called vignettes) among respondents, and using the combination of experimental design and ratings to create a model or equation showing how each element ‘drives’ a response. Step 4 prescribes the composition of the vignettes. Each respondent ends up testing exactly 24 different vignettes. The experimental design ensures that of elements, so that each vignette comprises a minimum of two elements, a maximum of four elements, and that each question contributes at most one element or answer to a specific vignette. In the end, for each respondent, every element appears exactly five times in 24 elements. Thus, a question contributes 4 (elements) x 5 (appearances per element), viz., contributes to 20 out of 24 vignettes, and does not contribute to four out of 24 vignettes. The 16 elements appear in a statistically independent fashion. Finally, each respondent evaluates a unique set of vignettes, created by permuting the combinations, but keeping the mathematical structure the same. This is called a permuted design [8]. Figure 2 shows a set of vignettes, along with the rating assigned by the respondent, and the response time, defined as the time (in seconds) elapsing between the presentation of the vignette to the respondent and the time that the respondent assigns the rating. Figure 2 comes from a database, created after the study. The actual screen shot of the vignette presented in the study appears Figure 4 (right panel).

FIG 2

Figure 2: Example of vignettes presented to the respondent, based upon the permuted experimental design.

Step 5: Create a Self-profiling Questionnaire (Figure 3, Left Panel)

Social researchers often want to learn more about the people who participate in their studies. To do so, they create what is known as a self-profiling questionnaire, which instructs the respondent to answer certain questions about WHO she/is, what she/he THINKS, what she/he DOES, and so forth. Often this material is used to divide the respondent population into new subgroups, each subgroup studied separately, and the results compared, hopefully revealing relevant group to group differences which knowledge adds to the contribution of the study.

The Mind Genomics program, BimiLeap, is programmed to obtain the gender and age of the respondent in every study, doing so automatically. The researcher can ask an additional 1-8 questions, each question allowing 2-8 answers. The self-profiling questionnaire is completed at the start of the study, before the respondent has read/rated the vignettes. As the third self-profiling questionnaire, BimiLeap instructed the respondent to provide a sense of the respondent’s mental horizon.

FIG 3

Figure 3: Screen shot of the self-profiling classification question (left panel), the respondent orientation (middle panel), and the rating scale (right panel).

Step 6: Create the Orientation Page

Most respondents coming into a Mind Genomics study do not know what is expected of them. The the act of reading paragraphs of information is well known, but not the somewhat artificial situation of reading lists of messages, 2-4 messages in each list, with no effort to link together the messages into to a coherent whole. The respondent must be introduced to what to do and how to evaluate through an explanation, the ‘orientation.’ Figure 3 (middle panel) shows the orientation for this study.

Step 7: Create the Rating Scale (Figure 3, Right Panel)

The scale is a 5-point scale.

The original aim was to have five different scale points as shown below.

R1=These are NOT important to define both me and NOT to define other people

R2=These are not important to define me but important to define other people

R3=Can’t answer about these

R4=These are important to define only me but not to define other people

R5=These are important to define both me and to define other people

By accident, the word ‘NOT’ was omitted from R1, so that R1 and R5 are the same. Thus, in the analysis we will refer to the two scales as R51, and will merge their data, leaving us with a 4-point scale.

R2=These are not important to define me but important to define other people

R3=Can’t answer about these

R4=These are important to define only me but not to define other people

R5 & R1 (R51)=These are important to define both me and to define other people

Step 8: Record Final Thoughts about the Project (Figure 4, Left Panel)

This section in the Mind Genomics study is reserved for the research as an ‘aide memoire’ for the study. Quite often studies are run, but the researcher may or may not recall some of the issues involved, or the subtleties recognize at the start of the experiment. The final thoughts serve as a written record of the study.

FIG 4

Figure 4: Screen shots showing the requirement for the researcher to describe the study for archival purposes (left panel), the number of respondents to be select, and the request for privatization if desired (middle panel), and an example of how the program is instructed to present a vignette to the respondent (right panel).

Step 9: Select the Number of Respondents, How the Respondents Will Be Chosen, and Whether or Not the Study Results Will Be Made Private (Figure 4, Middle Panel)

The study called for 30 respondents from New York, and 30 respondents from Alabama, ages 16-21. The ingoing hypothesis was that there might emerge big differences in geography, based upon the common belief that the ‘coasts’ generate different ways of thinking from the ‘heartland’ of America, including the less developed south. Alabama was chosen as the state to represent the south, a hypothesized opposite world from New York.

Step 10: Invite the Respondents to Participate, have Them Go Through the Self-profiling Classification, Read the Orientation and then Evaluate 24 Vignettes

Figure 4 (right panel) shows an example of a vignette as the respondent might see it on the screen of a smartphone. The screen shot shows the text with the information that the BimiLeap program uses to adjust the font. In the actual experiment the instructions at the top are shown in simple text format without the formatting instructions used by BimiLeap.

The respondents are invited to participate by a local field service or provided by the researcher. Experience of over 40 years suggests that it is best to work with specialists who can recruit respondents to participate. Rather than saving money by depending upon the good will of a person to participate, it almost always proves more beneficial to incentivize the respondent, and to work with a the specialty company which delivers respondents eager to participate. The study presented here took about 45 minutes to complete, after launching, with the respondents recruited by Luc.id Inc., the specialty company. Lucid contracts with field services worldwide to direct panelists to a study, efficiency far greater than realized any other way. Furthermore, Luc.id itself is only one of a growing number of companies specializing in providing respondents for the on-line studies.

Step 11: Create a Database for the Study, Similar in Form to an Excel File

Each respondent generates 24 rows of data, one row for each of the 24 vignettes.

a. Column 1=study name

b. Column 2=panelist identification number (later recoded, when multiple studies are combined into a single database

c. Column 3 and 4 – gender and age of the respondent

d. Column 5 and 6 – state where the respondent lives and answers how the respondent feels.

e. Column 7 Test order of vignette (01-24).

f. Column 8 – 23 Reserved for the structure of the vignette. Each column of the 16 columns is reserved for one of the 16 elements. A ‘1’ in the cell means that the element is present in that vignette. A ‘0’ in the cell means that the element is absent from that vignette.

g. Column 24 – The rating assigned to the vignette on the 5-point scale.

h. Column 25 – The response time for the specific vignette.

i. Note that Columns 1-6 are repeated 24 times, once for each of the 24 vignettes evaluated by the respondent. The information does not change.

Afterwards, create five new variables, which are binary transformations based upon the rating to the vignette assigned by the respondent:

a. R2=100 when the rating is 2, otherwise R2=0 (when the rating is not 2).

b. R3=100 when the rating is 3, otherwise R3=0.

c. R4=100 when the rating is 4, otherwise R4=0.

d. R51=100 when the rating is either 1 or 5, otherwise R51=0. This transformation codes those responses where the respondent feels that she/he is the same as others.

e. R42=100 when the rating is either 4 or 2, otherwise R42=0. This transformation codes those responses where the respondent feels that she/he differs from others.

f. To each of the newly created binary transformed variables add a vanishingly small random number (<10-5), to ensure that there will be some minimum level of variation in the binary transformed variable. That minimum level of variation ensures that the binary transformed variable will allow the regression analysis. Regression analysis fails when the dependent variable has no variation (when are the ratings are the same, e.g., R51 is all 0’s or all 100’s).

Step 12: Create Models (Equations) Which Relate the Presence/Absence of Elements to Ratings

By creating the vignettes according to a permuted design, the researcher makes it possible to use statistical methods to deconstruct the rating into the contributions of the 16 elements. The most common method is OLS (ordinary least-squares) regression. The equation is expressed as: DV (Dependent variable, viz. The binary transformed rating)=k1(A1) + k2(A2) … k16(A16). The equation is estimated without an additive constant. Estimating the equation with an additive constant will generate a high correlated set of 16 coefficient, but the coefficients estimated without the additive constant will be systematically higher than the same coefficients estimated with an additive constant. It becomes easier to understand the ‘meaning’ of the coefficients without using an additive constant. The equation can be created either at the level of the Total Panel (all respondents), at the level of a self-defined subgroup (e.g., age, gender, response to the self-profiling classification), or at the level of the individual respondent.

Step 13: Retain Strong Performing Element by Key Self-defined Subgroup, Based on R51 (Like Me)

Mind Genomics studies comprising coefficients for 16 elements by many groups returns a great deal of data from even the simplest analysis. All too often the plethora of coefficients obscures important patterns which exist in the data, but simply fail to be detected, the signal masked by the noise. It is often best to eliminate the weaker performing elements, suppressing the noise, to let the signal through. For this analysis and for the subsequent analyses, we eliminate all coefficients 14 or lower. These elements may be relevant, but with enough of these weaker scoring elements retained in the data, the pattern fails to emerge. Table 2 shows the strong elements for each group. Even with the effort to prune out weaker performing elements it becomes clear from Table 2 that no strong patterns are to be discerned.

Table 2: Strong performing elements describing WHO I AM (R51 as the dependent variable). The respondents are self-defined by who they ARE, and by what occupies their mind when THINKING ABOUT THE FUTURE. Very strong performing elements (coefficients of 20 or higher) are shown in shaded cells.

TAB 2

Step 14: Identify New to the World Mind-sets by Creating Individual-level Models & Clustering

The permuted design used by Mind Genomics ensures that each respondent evaluates a different set of 24 vignettes, but with each respondent evaluating the exact right set of vignettes to generate an equation for that respondent. The individual-level model thus enables the researcher to create a database of coefficients for the different respondents, and use the patterns generated by the coefficients to create or discover a limited number of patterns that can be interpreted.

We will use two dependent variables, R51 (same as me), and R42 (different from me). We can create these equations because we set up the 24 vignettes for each respondent by experiment design, and because we added a vanishingly small number to the value of R51 and another vanishingly small number of the value of R42. We create these two groups of coefficients because we do not know whether we will be success. We don’t know how young people think. Do they think in terms of ‘like me,’ or in terms of ‘different from me.’

The data processing is the same for each group, the first group with R51 as the dependent variable, the second group with R42 as the dependent variable. Clustering is a well-accepted procedure in exploratory statistics to identify groups [9]. The computation is done without considering the ‘meaning’ of the clusters, but rather simply use the clustering procedure in an ‘automatic’ fashion, and only later try to name the clusters. The emergent clusters may be considered to be different, interpretable regions of what really ends up being a ‘cloud.’ That is, the emergent clusters may not really exist as hard and fast, totally separable groups. This is some ‘wiggle room’ at the borders of the clusters. Nonetheless, clustering is a good way to get a sense of the nature of the dependent variable by identifying a small number of different levels/examples of the dependent variables.

Step 15: Discover Mind-based Upon Strong Performing Elements

Table 3A shows the emergence of two seemingly hard-to-interpret clusters (mind-sets) based upon clustering using R51 (Like Me). Table 3B shows the emergence of three easier-to-interpret clusters based upon clustering using R42 (Not Like Me). Table 4 shows the base sizes of the mind-sets based upon Total Panel as well as the mind-set emerging from using R54 (Different From Me) as the dependent variable.

Table 3: Very Strong performing elements emerging when the respondents are segmented based on R51 (Same as Me) versus segmented based on R42 (Different From Me).

TAB 3

Table 4: Base size of respondents for Total Panel and the three mind-sets emerging from using R42 (Different From Me) as the dependent variable in the individual-level regression modeling.

TAB 4

Discussion and Conclusions

The study presented here breaks new ground in the application of Mind Genomics to the development of the person. Traditionally, Mind Genomics has been used to understand how people respond to external stimuli, such as products, or more recently student expectations of what 3rd grade mathematics will be like in the years to come (Mendoza et. al., 2023). Mind Genomics has explored people in society, and the mind of the juror evaluating facts of a case [10-12].

With this paper Mind Genomics is moving into a new area, the study of how young people think about themselves. Rather than asking the respondent to introspect or rather than having an expert assess the individual based upon the expert’s experience and training, Mind Genomics approach works with the person evaluating her or his reaction to an ambiguous statement, a metaphor. Rather than asking the respondent to describe how she or he defines himself, the ‘production’ approach of psychology, we present the respondent with combinations of metaphors, such as family, work, etc. All we require the respondent to do is assign the combination to one of four groups, the four answers. It is impossible for the respondent to ‘game the system’, or to ‘freeze up’. Tables 3B shows that despite this seemingly to-respondent meaningful to these sets of 24 different combinations, the results appear to make sense, and give insight into the nature of the way the respondent thinks.

If we were to the future for new directions, perhaps the best result from this study is the infusion of a new way of experimenting with the already well-trod field of metaphors as tools to understand psychological processes. Just a few references should suffice to show the scope of what has been done, both in understanding the young person’s trip into maturity [13,14], as well as understand a person’s mind through a new lens [15-17]. Add to that the power of experimentation through Mind Genomics and we may be at the threshold of a new direction for psychology, coupling a deep study of the mind and experimentation using metaphors.

References

  1. Mendoza C, Mendoza C, Deitel Y, Rappaport SD, Moskowitz HR (2023) Empowering young researchers through Mind Genomics: What will third grade mathematics look like in 10 years? Psychology Journal, Research Open 5: 1-15.
  2. Porretta S, Gere A, Radványi D, Moskowitz H (2019) Mind Genomics (Conjoint Analysis): The new concept research in the analysis of consumer behaviour and choice. Trends in Food Science & Technology 84: 29-33.
  3. Stevens SS (1975) Psychophysics: Introduction to Its Perceptual, Neural and Social Prospects. John Wiley & Sons.
  4. Craven BD, Islam SM (2011) Ordinary least-squares regression. The SAGE Dictionary of Quantitative Management Research 224-228.
  5. Easterling RG (2015) Fundamentals of Statistical Experimental Design and Analysis. John Wiley & Sons.
  6. Moskowitz HR, Gofman A, Beckley J, Ashman H (2006) Founding a new science: Mind genomics. Journal of Sensory Studies 21: 266-307.
  7. Moskowitz HR, Gofman A, Lieberman LE, Ray I, Onufrey SR (2011) Sequencing the genome of the customer mind by RDE and intervention testing. Journal of Academic and Business Ethics 3: 4-14.
  8. Gofman A, Moskowitz H (2010) Isomorphic permuted experimental designs and their application in conjoint analysis. Journal of Sensory Studies 25: 127-145.
  9. Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recognition 36: 451-461.
  10. Green PE, Srinivasan V (1990) Conjoint analysis in marketing: new developments with implications for research and practice. Journal of Marketing 54: 3-19.
  11. Moskowitz H, Kover A, Papajorgji P. eds., (2022) Applying Mind Genomics to Social Sciences. IGI Global.
  12. Moskowitz HR, Wren J, Papajorgji P (2020) Mind Genomics and the Law. LAP LAMBERT Academic Publishing.
  13. Evans K, Furlong A (2019) Metaphors of youth transitions: niches, pathways, trajectories, or navigations. In Youth, Citizenship and Social Change in a European Context. Routledge 17-41.
  14. Wyn J, Lantz S, Harris A (2012) Beyond the ‘transitions’ metaphor: Family relations and young people in late modernity. Journal of Sociology 48: 3-22.
  15. Barker P (1992) Using metaphors in psychotherapy. Psychology Press.
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Aqueous Nasal Spray in Treatment of Rhinitis and Rhinosinusitis: Adverse Event Focusing on Epistaxis

DOI: 10.31038/JCRM.2023612

Abstract

Introduction: Rhinitis and rhinosinusitis are common in the general population and Intranasal corticosteroid (INCS) sprays are generally safe and effective in the treatment of these conditions. However, they are often burdened by side effects that can reduce compliance with therapy, one of the most common of which is epistaxis.

Objective: To review the current literature about the most common adverse events of beclometasone dipropionato aqueous nasal sprays therapy in chronic rhinosinusitis and allergic rhinitis, focusing on epistaxis.

Material and Methods: Using different search engines, the most common adverse events were reviewed and a total of 64 full-length articles were examined for eligibility. After applying inclusion and exclusion criteria, a total of 2 articles were reviewed.

Results: BDP is counted among the group of INCS with the lowest frequency of epistaxis reported as a side effect in the studies analyzed.

Conclusion: BDP aqueous nasal spray is one of the most frequently prescribed INC for rhinitis and rhinosinusitis, with a low frequency of epistaxis. The otolaryngologist and the general physicians should therefore consider prescribing this active principle, particularly to a target group of patients at increased risk of epistaxis.

Introduction

INCS are supported by level-1 evidence for medical management of numerous chronic nasal diseases such allergic rhinitis (AR) and chronic rhinosinusitis (CRS) reducing airway inflammation and improving symptom control [1]. The ability to undergo multiple formulations, such as nasal sprays, aerosols, dry powder inhalers, and ointments means that they can deliver a powerful local anti-inflammatory effect [2].

The intranasal administration of drugs, used for many centuries, has been increasing widespread in recent years, both due to the availability of molecules with specific activity on the airways and the numerous technological innovations that have increased the efficiency of devices available in clinical practice.

The success of inflammatory disease management with intranasal medications depends on the activity of the drug, its pharmacokinetic and pharmacodynamic properties [2]. However, the clinical efficacy of topic INCS is conditioned by some limitations related to possible side effects, due to the bioavailability of the drug. For INCS, these adverse events (AEs) are certainly less frequent and less serious than those observed with oral steroids, but they can considerably limit adherence to treatment, especially in pediatric patients, adolescents and the elderly [3]. The most common AE of INCS treatment is epistaxis [4]. Regardless of the cause, epistaxis is a common emergency encountered by primary care physicians. Up to 60% of the general population experience epistaxis, and 6% seek medical attention for it [5]. Possible causes are factors that damage the lining of the nasal mucosa, affect the walls of the vessels or alter the coagulability of the blood and related drugs such as nasal steroids [6].

Among the various molecules available for the treatment of CRS and AR, beclometasone dipropionato aqueous (BDP) nasal spray represents a possible “first choice”, since this molecule has an excellent efficacy and safety profile boasting decades of use experience [7]. The potential drug interaction risk of beclomethasone dipropionate is low as the drug has limited systemic bioavailability: Paul H. et al. confirmed this showing lower systemic exposure with intranasal administration than with oral inhalation [8]. Patients with nasal chronic inflammatory diseases often require long-term strategies to control symptoms: although the efficacy and safety of INCs are well established, concerns remain regarding systemic AEs including epistaxis, headache, anosmia, ageusia/dysgeusia, among others [9]. The aim of this review is to evaluate the BDP nasal spray adverse event reported in the literature, focusing on epistaxis.

Materials and Methods

To evaluate the studies that analyzed epistaxis as a side effect of BDP in the treatment of inflammatory sinonasal disease, a Pubmed research was conducted searching for articles written by 2010 and 2022, exclusively in English language, including randomized clinical trials, cohort studies, meta-analyses, case reports, and case series and excluding non- English studies, abstract and articles about non nasal Inhalation corticosteroids.

Search criteria included all occurrences of the following terms in the title or abstract: beclometasone dipropionato aqueous; one between “epistaxis”, “adverse event”, “adverse effect” and “complications”.

The corresponding results in the literature dating back to the last 10 years were examined for eligibility and 64 articles were identified: 53 articles were assessed for eligibility. Finally, after applying the above-mentioned inclusion/exclusion criteria, 2 reviews were analyzed [9,10].

fig

Results

The first article analyzed was published by Salma Ahsanuddin and addressed the Proportional Reporting Ratios (PRR) and Reporting Odds Ratios (ROR) for different AEs related to different drugs used to treat CRS and AR, referring to the “Adverse Event Food and Drug Administration Reporting System” and evaluating the relationship between AEs and 10 different INCSs.

BDP nasal spray collocates in the group with least adverse event, accounting for only 1,4% of the total AEs founded, contrary to Fluticasone Propionate and Mometasone, which instead represented the majority of the side effects identified in the analysis, representing 47,7% and 16,7% of total AEs, respectively.

Epistaxis was listed among the top 300 AE for each medication studied together with headache.

The PRR value for epistaxis of the INCs analyzed ranged from 1 to 27,2, with an average value of 4,64: the PRR value of epistaxis due to BDP was 1,5.

Similarly, the ROR value for epistaxis ranged from 1 to 30.8, with an average value of 5: the ROR value of epistaxis due to BDP was 1,5 (Table 1, modified).

Table 1: Intranasal corticosteroid Spray and Reported Epistaxis in FAERS

Corticosteroid

N. patients

PRR (95%CI)

ROR (95% CI)

Fluticasone propionate 50 mcg

578

2.88 (2.65, 3.12)

2.90(2.67, 3.15)

Fluticasone propionate 90 mcg

211

4.66 (4.08, 5.33)

4.73(4.13, 5.42)

Mometasone

142

2.02 (1.72, 2.38)

2.03(1.72, 2.39)

Budesonide

81

1.55 (1.25, 1.93)

1.56(1.25, 1.94)

Triamcinolone

42

1.32 (0.98, 1.79)

1.33(0.98, 1.79)

Fluticasone furoate

15

27.24(16.93,43.82)

30.76(18.54,51.03)

Beclometasone dipropionato

9

1.49(0.78, 2.87)

1.50(0.78, 2.88)

From: Adverse Events Associated with Intranasal Sprays: An Analysis of the Food and Drug Administration Database and Literature Review. Ahsanuddin S et al. 2021.

The second study analyzed was written by Wu EL et al and identified randomized control trials of INCs for treatment of AR that reported incidence of epistaxis: 72 articles with 82 distinct INCS-versus-placebo comparisons were included for meta-analysis.

For all the included comparisons, the meta-analysis demonstrated an overall risk ratio of 1.48 (95% CI, 1.32-1.67) for epistaxis.

In the studies analyzed, the INCSs associated with an increased risk of epistaxis after comparison with placebo were beclomethasone HFA, fluticasone furoate, mometasone furoate, and fluticasone propionate, while patients treated with BDP, ciclesonide HFA, and ciclesonide aqueous did not shown an elevated risk of epistaxis compared to patients treated with placebo (Table 2, modified).

Table 2: INCS-Related Epistaxis: Meta-analyses

 

Studies in Review, n

Epistaxis

 

INCS

Quantitative (82)

RR (1,48)

95% CI (1.32-1.67)

P value (<.001)

Beclomethasone HFA

6

2,35

1,06-5,20

.03

Fluticasone furoato

15

1,85

1,46-2,34

>.001

Mometasone furoato

14

1,48

1,06-2,07

.02

Fluticasone propionato

17

1,36

1,00-1,85

.05

Ciclesonide HFA

4

1,26

0.87-1.83

.22

Beclomethasone acqueous

8

1,24

0,84-1,81

.28

Ciclesonide acqueous

10

1,16

0,83-1,62

.39

Budesonide

5

2,49

0,91-6,79

.07

Triamcinolone

3

1,87

0,16-22,88

.62

Flunisolide

0

N/A

N/A

N/A

From: Epistaxis Risk Associated with Intranasal Corticosteroid Sprays: A Systematic Review and Meta-analysis. Wu EL et al. 2019.

Discussion

The most frequent adverse events in the treatment of AR and rhinosinusitis in the literature are revealed to be due to intranasal antihistamines and intranasal steroids, even if these AEs seem well tolerated. Many articles in literature report that epistaxis is the most frequent AEs following intranasal corticosteroids therapy. Epistaxis, while often minor and self-limiting, can result in lack of medication compliance, leading to patient and provider’s frustration resulting in additional procedures, medications, or ineffective treatments [11]. The possible causes are to be found among the thinning of the mucosa due to the vasoconstrictor effect or the direct trauma to the tip of the applicator at the level of the Kiesselbach’s plexus [9].

Conclusions

Intranasal corticosteroids are accepted as a safe and effective first line therapy for allergic rhinitis and rhinosinusitis [12,13], improving to decrease comorbidities and costs. Studies in literature have shown that satisfaction and comfort with an intranasal treatment device are likely to enhance adherence to that treatment among patients with AR and rhinosinusitis. Therapeutic compliance of these drugs depends on several factors, among which there are odor, taste, comfort of delivery, delivery devices (aerosol versus aqueous), patient cost [14] and the possible side effect such as epistaxis, headache, anosmia, ageusia/dysgeusia [9,15]. Waddell AN et al analyzed 16 randomized controlled trials which compared the efficacy of INCs and oral antihistamines in the treatment of allergic rhinitis, and found and incidence of epistaxis due to INCs between 17% and 23% versus an appreciable rate of placebo spray between 10% to 15% [16]. As pointed out by Wu E L, prescribers should be aware of which INCSs may place patients at a higher risk for epistaxis, and they should consider selecting an INCS with a lower risk of this side effect for patients with recurrent or persistent nose bleeding [10]. Only a few articles analyzed the frequency of epistaxis due to BDP, and agree that BDP is among the INCs who cause epistaxis less frequently.

In conclusion, patients with allergic rhinitis and rhinosinusitis represent a high portion of the population and they must chronically continue topical therapy to have optimal symptom control. Since epistaxis is one of the most common side effects of INCs, the otolaryngologist and the general physicians should consider those active principles that are least related to epistaxis, such as BDP.

References

  1. Eugenio De Corso, Pipolo C, Cantone E, Ottaviano G, Gallo S, et al. (2022) Survey on Use of Local and Systemic Corticosteroids in the Management of Chronic Rhinosinusitis with Nasal Polyps: Identification of Unmet Clinical Needs. Identification of Unmet Clinical Needs. J Pers Med 12: 897. [crossref]
  2. Peter J (2014) Barnes Glucocorticoids History of Allergy. Chem Immunol Allergy 100: 311-316. [crossref]
  3. Carlo C, Giovanni AR (2017) Efficacy and safety of beclomethasone dipropionate Suppl. Recenti Prog Med 108: S1-S11.
  4. Corren J (1999) Intranasal corticosteroids for allergic rhinitis: how do different agents compare? J Allergy Clin Immunol 104: S144-9. [crossref]
  5. Womack JP, Kropa J, Stabile MJ (2018) Epistaxis: Outpatient Management. Am Fam Physician 98: 240-245. [crossref]
  6. Paul M (2004) Epistaxis Emerg Med Australas 16: 428-440.
  7. James Fowler, Brian W Rotenberg, Leigh J Sowerby (2021) The subtle nuances of intranasal corticosteroids. Journal of Otolaryngology – Head and Neck Surgery 50: 18. [crossref]
  8. Paul HR, Melchior A, Dunbar SA, Tantry SK, Dorinsky PM (2012) Pharmacokinetic Profile of Beclomethasone Dipropionate Hydrofluoroalkane after Intranasal Administration Versus Oral Inhalation in Healthy Subjects: Results of a Single-Dose Randomized, Open-Label, 3-Period Crossover Study. Clinical Therapeutics 4: 1422-1431. [crossref]
  9. Ahsanuddin S, Povolotskiy R, Tayyab R, Nasser W, Barinsky GL, et al. (2021) Adverse Events Associated with Intranasal Sprays: An Analysis of the Food and Drug Administration Database and Literature Review. Ann Otol Rhinol Laryngol 130: 1292-1301. [crossref]
  10. Wu EL, Harris WC, Babcock CM, Alexander BH, Riley CA, et al. (2019) Epistaxis Risk Associated with Intranasal Corticosteroid Sprays: A Systematic Review and Meta-analysis. Otolaryngol Head Neck Surg 161: 18-27. [crossref]
  11. Bridgeman MB (2017) Overcoming barriers to intranasal corticosteroid use in patients with uncontrolled allergic rhinitis. Integr Pharm Res Pract 6: 109-119. [crossref]
  12. EPOS 2020
  13. Management of allergic rhinitis and its impact on asthma. ARIA guidelines, 2019.
  14. Sher ER, Ross JA (2014) Intranasal corticosteroids: the role of patient preference and satisfaction. Allergy Asthma Proc 35: 24-33. [crossref]
  15. Eli O Meltzer, Greg W, Bensch, William W Storms (2014) New intranasal formulations for the treatment of allergic rhinitis. Allergy Asthma Proc 35: S11-S19. [crossref]
  16. Waddell AN, Patel SK, Toma AG, Maw AR (2003) Intranasal steroid sprays in the treatment of rhinitis: is one better than another? J Laryngol Otol 117: 843-855. [crossref]

Solitude Approach in the Digital Era

DOI: 10.31038/IJNM.2023413

 
 

Human entrance into a natural world evolves through an end facing next to something neither perceptible nor elucidated, as for all animate and inanimate surroundings. Sex difference ensures the individual’s continuation in a natural, audible, visible world. Love brings humans together, and a common interest or concern unifies them. More people join in matrimony, others remain allied without the official marriage document, or different amalgamated groups prefer physical and mental relations. Love remains a passionate attraction and a desire for someone, which can start a romantic relationship. A child develops in a climate offered by his biological parents, surrogate mother, or adoptive parents, with or without being relatives. No one can replace biological parents. Usually, their substitutes offer excessive affection, but sometimes it may be disapprovingly and adversely working. Biological parents recognize themselves as a part of their child’s thinking, action, and construction and readily accept and correct possible child’s genetic errors or mistakes. Still, the feelings of adoptive parents in such circumstances generate various effects that dissatisfy over a prolonged period. The child grows up; scientific, cultural, and social development depends on genetics, environmental characteristics, social networking, financial power, and ventures.
 

Human love is changeable over time; initial attraction and passion then convey affection for the child or nephew, making some parents feel differently. Many people experience loneliness sooner or later, even in their family, since diverse circumstances and various disorders modify the individual judgment. The deceptive ambiance and the impression of illusory feelings produce suffering in the heart and affect mental health. Pondering thoughts’ power to improve life quality and expectancy is necessary. In the digital era, no one can be alone. Other attractions can act concurrently through a bright existence. IT advancement put forward ideas of allure for choice. There is a variety of programs available online with the opportunity to watch on-demand-communications for fervour, relaxation, or instruction; instant connection with selected people, discussions, lectures of interest, movies, celeb paintings, games, and chosen musical programs according to personal preferences make an individual absorbed by other facts and the time to come back to the gloom-generating blue devils decreases. Another competitor for relaxing and improving well-being is walking. Nature expresses perfect creation; its soundness and colour harmony charm the individual. He takes great pleasure in watching the water flow, sun, and moon’s rays going over; the flowers’ scent and impressive trees surrounding produce enchantment. Physical exercise is a convenient tool for improving a person’s life quality and maintaining health, but it must be adjusted to the personal medical history and demographic data. We are assessing our beliefs and must accept the uncertain existence and its game that firms up the lifecycle as an unending night-day in seasons. All come in greatness and pass on. Reflecting more on the divine creation and selecting the procedures for peaceful moments in troubled times adds value to a better inner life. Contemplation on the beauty around, forgiveness, and kind-heartedness open the door through an individual sunny universe making shadows quietly end up.