The paper presents the empirical evaluation in a Mind Genomic format of five sets of 16 elements each, previously generated entirely by AI, and dealing with the issue of aspects of a police officer’s job focused in a school, in a small town in Pennsylvania. The respondents, ages 18-30, read combinations of messages (elements) about the job, these elements combined by experimental design into vignettes comprising 2-4 elements per vignette. The results from all five studies revealed the very strong performance of the elements when the respondents were divided into mind-sets. Three studies each generated three mind-sets, two studies in turn, each generated two clear mind-sets. The entire process — from the generation of the ideas to the validation with people — required approximately four days and was done in an affordable fashion with available technology, generating easy-to-understand, immediately actionable messaging. The five studies along with the rapid generation of the ideas using generative AI open up the possibilities that AI may help to better communicate with people, through the combination of LLM (large language models) and Mind Genomics empirical thinking and experimentation.
Keywords
Generative AI, Mind genomics, Police recruitment, Synthesized mind-sets
Introduction
In the companion paper, “School Crossings and Police Staffing Shortages: How Generative AI Combined with Mind Genomics Thinking Can Become “Colleague,” Collaborating on the Solution of Problems Involved in Recruiting,” we presented four strategies to approach the issue of recruiting for a police officer position in TOWNX. Strategy 3 in that paper dealt with the creation of questions and answers. The answers were to be given by four AI-synthesized mind-sets: Dedicated Public Servant, Compassionate Protector, Community-Focused, and Proactive Problem Solver. Thus, Strategy 3 generated questions about the topic of recruiting, and answers to the questions from four simulated mind-sets. There was no guidance of the process from a human being, other than the basic question of how one gets a person to consider a career in law enforcement. This paper continues that work, looking at these AI-generated, best- guess questions and answers, not with artificial intelligence alone, but with actual respondents living in the state of Pennsylvania and of the proper age, 18 to 30, with a high school diploma, who might be interested in having a career in law enforcement. That is, how well do the ideas generated by artificial intelligence end up performing when given to real respondents in the Mind Genomics platform?
Mind Genomics
Mind Genomics is an emerging science with origins in experimental psychology and statistics and consumer research. The background to Mind Genomics and the computational approaches have been well documented and presented elsewhere [1-3]. Here are some of the specifics relevant to the data presented in this paper:
The researcher identifies a topic of interest. Here, the topic is what communications are effective to get a young person (ages 18-30) to want to join the police force and be part of the effort to help at school properties, among other tasks.
The researcher creates four questions. Figure 1 shows the requirement to fill in the four questions (Panel A) and the four questions that were filled in (Panel B).
Figure 1: The BimiLeap.com screen guiding the user to provide or create the four questions (Panel A) and then the completed screen as typed in by the user (Panel B).
It is at this point that many prospective researchers “hit a blank wall,” feeling that they are unable to create questions. The Mind Genomics platform has been augmented with generative AI (ChatGPT 3.5) [4- 7]. The user accesses the AI through Idea Coach. Strategy 3 in the companion paper shows how AI can generate 21 questions of interest, with a simple prompt. This paper uses the 21 questions from Strategy 3 to create the questions needed for five separate experiments using the Mind Genomics platform. For each question, the researcher is instructed to provide four answers. This task is simpler, less daunting. In the companion paper, we created the questions. For each question, we generated four answers reflecting the way different types of people with different ways of thinking about the problem would answer the question. Table 1 also shows the four answers for each question. The answers were provided by AI, in the companion paper, but have been edited to be more “standalone.”
Table 1: The five questions and the four answers to each question.
Properties of the Vignettes Created by the Underlying Experimental Design
The basic unit of evaluation at the level of the individual respondents is the set of 24 vignettes, presented to and evaluated by the respondent one vignette at a time, in an interview lasting about three minutes, and done on the internet. Each respondent evaluates a different set of 24 vignettes. Rather than having to “know” the best range to test, the approach allows anyone to become an expert simply by testing many elements in this format [8]. The vignette comprises a combination of 2-4 elements, viz., message (see Figure 2, Panel B as an example of a vignette). These vignettes are created according to an experimental design. The design prescribes that there be four sets of four statements each. The statements are “elements” in the language of Mind Genomics. Each vignette comprises a minimum of two elements and a maximum of four elements. Each vignette has either one or no elements from a question. Thus, a vignette can never comprise two mutually exclusive or contradictor elements, viz., different answers or elements from the same question. The experimental design prescribes the specific composition of each vignette or combination of the 24 vignettes. For each set of 24 vignettes allocated to one respondent, each of the 16 elements appears exactly five times, once in five different vignettes, and absent from the remaining 19 vignettes. The 16 elements are statistically independent of each other, allowing the researcher to use statistical modeling (e.g., ordinary least squares regression analysis, OLS regression) to estimate the linkage between the presence of the 16 elements, and the rating that will be assigned by the respondent [9].
Figure 2: The respondent experience. Panel A on top shows the self-profiling classification in a pull-down menu. Panel B on the bottom shows one of 24 vignettes that the respondent will evaluate.
The Respondent Experience
These studies are typically run with respondents who have agreed to participate, signing an agreement with an online research panel “provider.” These research panels comprise thousands of individuals from all over the country and all over the world. The panel members are invited to participate, usually by email. They receive some remuneration for each participation, with the remuneration administered by the panel company. The user is guaranteed that these are not bots, but rather real people. The respondents are invited to participate by an email based upon the qualifications requested by the researcher. The respondents who agreed to participate press a link and are led to the interview. The interview itself is simple and the explanation of the interview is done by a series of slides at the beginning of the interview. The researcher first obtains some additional classification from the respondent using a pull-down menu (Figure 2, Panel A). Currently, the platform, BimiLeap.com, provides the user with up to 10 self-profiling questions, two of which are fixed: age and gender, respectively. That information can be extended dramatically to many more questions. The respondent then reads an orientation, and is led to the set of 24 vignettes, presented one vignette at a time. Figure 2, Panel B shows an example of the vignette that the respondent sees. The vignette itself comprises two to four elements as noted above, along with a short introduction to the project present in each vignette and of course the rating scale present in each vignette. The respondent reads the orientation, usually once, skips to the vignette, reads the vignette, and then assigns an answer. The objective is to get the respondent’s immediate impressions, almost a so-called “gut feeling,” where it is not judgment but feelings which are dominant.
The spare design of the vignette, without any connectives, may seem unpolished. The reality is that this spare profile of the vignette reduces fatigue. The respondent “grazes” for information in a comfortable manner, rather than having to wade through the thickets of text to get to the ideas. The respondent evaluates the vignette, considering the 2-4 elements as one idea, scoring the vignette on the scale. The Mind Genomics platform records the rating, and the response time (RT), defined as the number of seconds elapsing to the nearest 100th of a second from the time the vignette was presented to the time the rating was assigned.
Automated Preparation of the Data for Statistical Analysis
The Mind Genomics platform now creates a database which is set up to facilitate analysis. The database comprises of records for each vignette. Since each respondent evaluated 24 vignettes, each respondent generates 24 rows of data. The first set of columns is reserved for information about the respondent, generated from a self- profiling classification. This information includes gender, age, and up to eight additional self-profiling classification questions. The second set of columns is reserved for the information about the 16 elements. Each element has its own column. When the element is present in the vignette the value is “1” in the cell. When the element is absent, the value is “0” in the cell. Each vignette rated on the 5-point rating scale is converted to a binary scale, R54x or “JOIN.” A rating of 5 or 4 is converted to 100 to denote interest in joining. A rating of 3, 2, or 1 is converted to 0, to denote not interested in joining. Then, a vanishingly small random number (<10-5) is added to the newly created binary variable. The rationale is to ensure that even when a respondent rated all 24 vignettes high (5 or 4), or all 24 vignettes low (3, 2, or 1), there will be some minimal variation in the newly created binary variable. That minimal variation is necessary for the data from a single respondent or in fact any group of respondents to be analyzed later on using OLS (ordinary least-squares) regression.
Statistical Analysis — OLS Regression to Find Linkages Between Elements and Binary Variable R54x
The Mind Genomics process is now standardized. The experimental design ensures that all of the elements for each respondent are independent of each other. This up-front effort ends up allowing OLS (ordinary least squares) regression to relate the presence/absence of the 16 elements to the binary dependent variable R54x (viz., interested in joining).
The equation is simple: R54x = k1A1 + k2A2… + k16D4.
The foregoing equation can be estimated at the level of the individual respondent, at the level of any group of respondents, and of course at the level of the total panel. Note that the equation has no additive constant. The ingoing rationale is that in the absence of elements we should have a rating of 0. There is no reason to “join” when there are no elements to communicate the job. The coefficients show the driving power of the elements as a motivator of joining. A coefficient of 20 is twice as much driving power to join as a coefficient of 10. A coefficient of 20 is 2/3 of the driving power of a coefficient of 30, and so forth. The coefficients can be thought of as psychological measures of probability saying “I will join” when the element is in the mix of messages. We should look for coefficients around 21 or higher.
Creating Mind-Sets
A key hallmark of Mind Genomics is the search for mind-sets, defined as groups of respondents with similar patterns of coefficients, who think the same way. These individuals are not necessarily like each other in other ways, but they do think similarly for the topic. The topic here is the messages which drive the respondent to say they would like to join. The approach to find these groups, so-called mind-sets, is called clustering. Clustering uses the individual sets of 16 coefficients as inputs. Clustering tries to put the respondents into a small number of predefined groups (e.g., 2 or 3), so that the pattern of coefficients of the individuals within the cluster or group is similar. At the same time, the average profile on the 16 coefficients for the two or three groups is different. The clustering program used by Mind Genomics, k-means clustering, works entirely by mathematics. It is only afterwards that we try to interpret the meaning of these clusters [10]. The clusters are called mind-sets.
Interpreting the Data
When we look at Figure 2, Panel B, viz. the sample vignette, we see that the structure of the vignette does not lend itself to “gaming the system.” There are 24 vignettes, so there is no point in expending a great deal of effort. The sheer number of vignettes militates against trying to outguess the researcher. Another aspect, namely the spare structure of the combinations, and the fact that to the untrained eye these vignettes seem to be random. Every respondent sees a different set of 24 vignettes, with the elements in the vignettes seeming to be put in or taken out by random. The respondent quickly goes into a sense of indifference and guesses, rather than focusing on being correct and pleasing the respondent and pleasing the interviewer. The respondent participating on a computer simply proceeds, going through the evaluation. As noted above, the OLS (ordinary least squares) regression analysis shows the driving power of the elements. Table 2, column labelled Total Panel, shows the 16 coefficients for the elements below. When we look at the coefficients from the total panel, we have a coefficient as high as 22, and a coefficient as low as 14. Only one element moves beyond the pre-set criterion of coefficient C1 — What you do: I actively engage with residents and address their concerns. The remaining columns show the other groups, gender and age. Respondents not appropriate for the secondary requirements (viz., age outside the allowable range) were not considered for specific analyses, but were included in the Total Population, and in the self- profiling classifications about marital status and children. Once again, we see relatively few elements which score strongly. Only Element C1 scores consistently strongly. To make interpreting easier, keep in mind that the numbers in the body of the table are coefficients from regression. They can also be interpreted as “the increment percent of people who, reading this element, will say I will join.” Also keep in mind that we would like strong performing elements. Looking now at the Total Panel, we find that C1 has a coefficient of 22. This means that when element C1 appears in a vignette (What you do: I actively engage with residents and address their concerns), we get 22% more people saying, “I would like to join.” On the other hand, when we put in A4 for whatever reason (Advantage: I identify potential safety threats and implement preventive measures), only 14% say they will join. That’s about 2/3 as many. We clearly would want to put in Element C1. Verbalize results — look for opportunities — by looking down within a group, and across groups. The numbers can all be compared to each other, and added together, at least up to four elements, no more than one element from a question. The sum provides us with a sense of the likely percent of respondents who say they will join. The consequence of this analysis is a powerful tool to understand, and to compose, all done in a matter of hours.
Table 2: Coefficients for the 16 elements for Study 1, for Total Panel, gender, age, and self-profiling status of marriage and children.
Thinking Differently at the Granular Level of Everyday Life — The Challenge of Mind-Sets
One of the hallmarks of Mind Genomics is this belief that in every area of everyday life, people differ in the way that they deal with the objectives, the goals, the messages. These are not the major differences in people, but rather everyday differences which are systematic, repeatable, and useful for things as different as medical advice and advertisements for shopping. The approach to find these so-called mind-sets, these differences in the way we approach issues, is very straightforward. Recall from above that we have regression analysis for each of our 100 respondents who saw the 24 combinations. So instead of doing the analysis at the level of all 100 people pooled together, let us do the regression analysis for each one of our 100 people, and let’s store 100 sets of the 16 coefficients in a database. When we do that analysis, we end up with 100 different models, 100 rows each with 16 columns. Each row is a respondent, one of our 100 respondents. The numbers are the coefficients estimated from the individual-level regression analysis. That difference is not based on who the people are, but rather on how the people respond to specific, relevant messages describing a small aspect of daily life. In other words, we are not interested in who people are, what they do, but how they think in a very local granular situation. There are a variety of metrics, ways to quantify the dissimilarity between respondents. We use the measure of distance between pairs of respondents, based upon the correlation of the coefficients. The distance between pairs of respondents is defined (1 – Pearson Correlation), computed on the corresponding pairs of the 16 coefficients. When the 16 coefficients of one respondent correlate perfectly with the 16 coefficients of another respondent, they are defined as having 0 distance. When the 16 coefficients of the two respondents describe opposite patterns, their distance is +2. We do not supervise the program. We simply allow the program to come up with these groups so that the patterns of the respondents within a group, within a cluster, are very similar, but the averages of the cluster on the 16 elements are very different across the three mind-sets. When we do the analysis, we find that the strongest result emerges when we ask the clustering program, the K-means clustering program, to create three groups. The bottom line is that even without intellectually thinking through the study, the regression analysis and clustering end up with radically different interpretable groups, as shown in Table 3. The important thing here is that the clusters are interpretable, the coefficients are very high, and it makes sense. What’s also important is that the coefficients are high for one group and reasonably low for the other group. We are really dealing with different mind-sets, responding to different messages as motivators. The important thing for this study is that the generation of these elements by artificial intelligence, Strategy 3 in the companion paper, with slight editing, ends up showing remarkably different types of people, suggesting the power of artificial intelligence revealed by human responses in a situation where respondents can game the system.
Table 3: The performance of all elements in Study 1, for Total Panel and for the three mind-sets generated by k-means clustering (MS1, MS2, MS3). Strong performing elements are shown by shaded cells.
How do we know that the clustering produces real mind-sets? This is an important question. The goal in Mind Genomics is to discover truly different ways of thinking about the same topic. Two factors come into play. One fact is that the data should show elements which have high coefficients, with these elements “telling a story.” The other is that the data should show elements which have low coefficients. It is not sufficient to generate high coefficients everywhere. That would show better elements, but not show radically different mind-sets. In recent studies, the authors have introduced the index called IDT, Index of Divergent Thought. The IDT is a way to show the net effect of the two forces: high coefficients for some sets of interpretable elements, and low coefficients for the other elements. Table 4 shows the computations. Simulations of data sets showing high coefficients for elements relevant to the mind-set and low coefficients elsewhere suggest that an IDT around 70 is best. The data in Study 1 suggest an IDT of 71, almost perfect.
Table 4: The data for the IDT (Index of Divergent Thinking) and the calculations.
Using AI to Summarize the Results, Considering Only the Strong-Performing Elements
The final analysis in this study deals with how AI analyzes the results and the strong elements for each mind-set. These appear in Table 5. The notion here is that AI can act as a second pair of eyes, as a coach, as an interpreter of the results. The table is laid out in the form of a set of questions to be answered for each mind-set, based upon the pattern of elements scoring 21 or higher for that mind-set. The questions themselves range from a summarization of the mind-set, the elements which perform strongly, and then onto questions about innovations and messaging.
Table 5: AI summarization of the key findings and opportunities for each mind-set, based upon the patterns generated for strong performing elements for that mind-set.
The questions are answered automatically, once the study is completed. The results here are done automatically, provided at the end of the study, within 30 minutes. In the interest of standardizing our understanding, the questions are fixed, answered in every Mind Genomics report, for key groups, including Total Panel, Self-Profiled Groups (e.g., gender), and mind-sets such as the three mind-sets reported here. Over time, it is straightforward to update the Mind Genomics platform, BimiLeap, so that the platform becomes even more complete, recognizing only that the updated platform will be used for every report and every key subgroup within the report.
Discussion and Conclusions
The data presented in this paper, in Study 1 above, and in Studies 2-5 in the appendices, suggest that we are only beginning to see the fruits of an AI which can help us to solve practical problems about recruitment and similar issues in a way never before possible. It is important to note that the study ran here, this first study, emerged from the questions and the answers generated by AI. Mind Genomics began to incorporate AI in 2023, typically to solve the problem of researchers “freezing” at the task of developing questions and then answers to those questions (so-called elements). The early work was so successful that it led to the incorporation of AI in the form of Idea Coach. It was with the exploration of AI beyond requesting questions and answers that the power of AI would emerge even more forcefully. The companion paper demonstrated the possibility of creating questions about a topic, and then different answers to the same question, those answers provided by AI-synthesized mind-sets. Everything, therefore, was under the control of AI, which moved from a coach to “unfreeze the researcher” into a true researcher, one almost independent of the human researcher. If we were to summarize the importance of this paper and of the companion paper, we would probably come out with the idea that we have now a tool, which in a very short period of time, hours and days, can produce information both in a deep way from generative AI and from actual people responding to the relevant stimuli as AI considers them to be. The consequence is the promise of increased expertise in the field for the professional, and an increased ability to learn how to think critically for younger students. We are sitting here on a cusp now, where learning through the computer can be made targeted, fun, quick, easy, and even gamified with the results from the Mind Genomics experiment. The simple fact that all of the material presented here was done in less than one week (really 5.5 days), starting from absolute zero is witness to the fact that we are on the cusp of an intellectual revolution, where information, validated information, about issues related to people can be dealt with quickly, both in terms of quote library type research through AI, and then human experiments.
Acknowledgment
The authors would like to thank Vanessa Marie B. Arcenas and Isabelle Porat for their help in producing this manuscript.
Abbreviations
AI: Artificial Intelligence, ChatGPT: Chat Generative Pre-Trained Transformer, IDT: Index of Divergent Thought, LLM: Large Language Model, OLS regression: Ordinary Least Squares regression
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A woman’s transition through menopause is a multifaceted experience that encompasses more than just the end of reproductive capacity. It presents unique challenges and opportunities for mental health scholars and practitioners. Importantly, hormonal fluctuations that occur during menopause impact a woman’s neurological and cognitive functioning. As a result, women may experience a variety of cognitive challenges commonly collectively referred to as brain fog. To this end, brain fog is a hallmark symptom of menopause. The application of neurocounseling to menopausal mental health care presents a novel pathway for holistic, personalized treatment. This article presents current information regarding cognition during menopause and neurocounseling. The article concludes with recommendations for the application of neurocounseling as a treatment approach for brain fog within a menopausal mental health care.
Keywords
Menopause, Brain fog, Cognition, Neurocounseling, Neuroscience, Mental health
Cognition During Menopause
During menopause, many women experience noticeable changes in cognition, often referred to as brain fog . These cognitive changes are largely influenced by hormonal fluctuations, particularly the decline in estrogen, which affects brain function. Estrogen plays a key role in cognitive processes, including memory, attention, and learning. As estrogen levels drop during perimenopause, women may face several cognitive challenges. For example, many women report memory problems such as forgetfulness or difficulty recalling information, especially short-term memory issues. A decline in attention span and focus is common, making it harder to concentrate and complete tasks that require sustained attention. During this time some women notice that it takes longer to think through tasks or solve problems than before suggesting that some perimenopausal women have a lower processing speed. In addition, perimenopausal women may struggle to find the right words during conversations, leading to feelings of frustration. Overall, many women experience a general sense of mental cloudiness or difficulty thinking clearly, affecting problem-solving and decision-making during menopause, and perimenopause in particular [1-5].
These cognitive changes can affect daily life and work, contributing to emotional distress such as anxiety and frustration. This is further exacerbated by the fact that there is no universal assessment or benchmark for the onset of perimenopause [5]. Thus, brain fog is many women’s first encounter with the symptoms of menopause. As a result, many women struggle to detect their symptoms as those of menopause and not simply aging or stress [6]. While this may be inconsequential for some women, the lack of knowledge and preparedness can create significant psychological distress for others. For most women, cognitive changes are temporary and tend to improve post-menopause. Nevertheless, the impairment can last years as a women undergoes perimenopause and be quite debilitating [5]. Lifestyle interventions like exercise, healthy diet, mental stimulation, and stress management can help mitigate cognitive difficulties during this phase. Moreover, menopausal hormone therapy (MHT), such as the use of prescribed estrogen via patches, pills, vaginal creams, combined estrogen-progesterone pills, gel-based applications, and certain intrauterine devices (IUDs), can help mitigate the underlying hormonal cause of cogntitive impairment. However, these interventions do not address the neurological aspects of a woman’s hormonal fluctuations that contribute to psychological distress [1,7].
Neurocounseling
Neurocounseling is an interdisciplinary approach that integrates neuroscience with counseling practices to better understand and address mental health issues. It focuses on how brain function and neurological processes influence behavior, emotions, cognition, and overall mental health. By incorporating knowledge of the brain and nervous system, neurocounseling helps mental health practitioners design more effective interventions tailored to the biological underpinnings of a person’s mental health challenges [8,9].
The neurocounseling approach involves using tools like brain imaging studies, neurofeedback, mindfulness practices, and cognitive-behavioral strategies to promote positive changes in brain functioning and emotional regulation. The goal is to help patients and clients improve their mental health by combining traditional therapeutic methods with insights from neuroscience, fostering a deeper understanding of how the brain and nervous system respond to therapy. Neurocounseling is particularly useful in treating conditions such as anxiety, depression, trauma, ADHD, and other disorders where brain function plays a critical role. The use of neurocounseling to support menopausal women is relatively unexamined [10-12].
Implications for Menopausal Mental Health Care
Mental health practitioners can use neurocounseling to effectively treat cognitive challenges such as brain fog during menopause by incorporating neuroscience-based techniques that target both the brain and behavior. Given that cognitive changes during menopause, such as memory issues, difficulty concentrating, and brain fog, are often linked to hormonal fluctuations, neurocounseling offers a holistic and empowering approach to managing these challenges. A summary of how six aspects of neurocounseling can be used to address cognitive aspects of menopausal mental health follows.
Psychoeducation
Psychoeducation is an integral aspect of neurocounseling [11]. Mental health practitioners can educate patients and clients about the neurological basis of cognitive changes during menopause, helping them understand that these difficulties are normal and often temporary. This awareness can reduce anxiety and foster a more compassionate view of their menopausal experience.
Cognitive-behavioral Therapy (CBT)
Neurocounseling can integrate CBT techniques to help clients and patients manage negative thought patterns that may arise from cognitive struggles [10]. For example, women who feel frustrated by forgetfulness can learn strategies to reframe their experiences and reduce the emotional burden associated with menopausal cognitive challenges.
Mindfulness and Relaxation Techniques
Since stress exacerbates cognitive decline, mental health practitioners can teach mindfulness-based stress reduction (MBSR) and relaxation techniques [13]. Mindfulness has been shown to positively affect brain plasticity, promoting cognitive resilience by enhancing focus, attention, and emotional regulation. For women undergoing cognitive changes due to menopause, MBSR can be particularly useful, empowering women to assert greater control their attention and emotional regulation.
Neurofeedback
This tool allows patients and clients to monitor their brain activity in real time and learn how to regulate their brain waves. Neurofeedback can improve concentration, memory, and mental clarity, which are often impacted by menopause [10].
Memory and Attention Training
Mental health practitioners can use brain-based exercises to strengthen cognitive functions such as working memory and attention. Techniques like brain games, puzzles, and structured mental exercises can improve cognitive flexibility and processing speed [14]. As a tool within the neurocounseling framework, memory and attention training can empower women while creating an entertaining outlet. The latter may be particularly important given that many women report an increase in social isolation and a decreased participation in pleasurable activities during the menopausal transition [15].
Lifestyle Guidance
Neurocounseling emphasizes the connection between brain health and lifestyle choices. Mental health practitioners can encourage physical exercise, proper nutrition, and adequate sleep, all of which are linked to better cognitive function. They may also recommend activities that stimulate the brain, like reading, puzzles, or learning new skills, which promote neuroplasticity and cognitive improvement [13].
Conclusion
By using neurocounseling, mental health practitioners can offer women experiencing cognitive difficulties during menopause a comprehensive treatment plan that addresses both the psychological and neurological aspects of their symptoms, helping them regain confidence and mental clarity. For patients who can and are willing to take MHT, mental health practitioners using neurocounseling approach can work collaboratively with a woman’s medical healthcare provider who can prescribe MHT [1]. In this case, a combination of MHT along with neurocounseling presents a meaningful clinical pathway for the treatment of hormonal fluctuations, psychological implications, and neurological aspects of cognitive concerns among menopausal women. Women who are unable or unwilling to take MHT can also benefit from neurocounseling for symptom management and improved quality of life.
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In the pharmaceutical industry, a major challenge is ensuring consistent quality of finished products as the batch scale shifts from laboratory to pilot to commercial levels. This review article aims to provide insights into the current industry practices and understanding of scale-up calculations and factors involved in the production of oral solid dosage forms. Pharmaceutical manufacturing encompasses various unit operations for oral solid dosage forms, including blending, wet granulation, dry granulation via roller compaction, milling, compression, and coating processes such as Wurster and film coating. Each unit operation’s parameters significantly influence the final product’s quality. As batch sizes increase, it becomes crucial to control various process parameters strategically to maintain product consistency. This article discusses the application of scale-up and scale-down calculations throughout different stages of unit operations, highlights the importance of scale-up factors in technology transfer from pilot to commercial scales, and reviews the current methodologies and industry perspectives on scale-up practices.
Oral solid dosage forms are final drug products designed to be ingested orally. Once swallowed, these forms dissolve in the gastrointestinal tract and the active ingredients are absorbed into the bloodstream. Examples of oral solid dosage forms include powders, granules, tablets, capsules, soft gels, gummies, dispersible films, and pills. These dosage forms are preferred for several reasons: they are relatively easy to administer, they can be clearly distinguished from one another, and their manufacturing processes are well-established and understood. Among oral solid dosage forms, tablets and capsules are the most common. Both consist of an active pharmaceutical ingredient (API), also known as the drug substance, along with various excipients. The manufacturing process for these dosage forms involves several unit operations, including blending, wet granulation (using a rapid mixer granulator or fluid bed processor), dry granulation (via roller compaction), milling, compression, Wurster coating, and film coating [1,2].During the early stages of drug product development, formulations and processes are created using active pharmaceutical ingredients (APIs) and excipients to ensure the quality, safety, and efficacy of the final drug products at the laboratory scale [3]. Once this formulation is established, the process is scaled up from the laboratory to pilot and eventually to commercial scales [4,5]. Throughout this technology transfer, the laboratory-scale formulation is generally finalized and remains unchanged, while process parameters are adjusted. For instance, as the scale of the granulation container increases, both the powder weight and the sizes of components like the impeller and chopper, as well as operational parameters, may need adjustment. These changes can impact the quality of the finished product [6]. Successful scale-up relies on a thorough understanding of the process parameters and the ability to adjust them appropriately to maintain the same quality observed at the laboratory scale.
Successful scale-up of a manufacturing process hinges on a deep understanding of the fundamental principles and insights into each unit operation, which are derived from mechanical insights into the process. The Food and Drug Administration (FDA) has introduced the Quality by Design (QbD) approach to facilitate the efficient and timely production of high-quality pharmaceutical products [7,8]. According to the International Conference on Harmonisation (ICH) guidelines specifically ICH Q8 (Pharmaceutical Development), ICH Q9 (Quality Risk Management), and ICH Q10 (Pharmaceutical Quality System) the scale-up process should be conducted to ensure product quality in alignment with the QbD principles. To meet these regulatory requirements, it is essential to establish methods for reducing variability during scale-up through a systematic understanding of the manufacturing process and the application of the QbD approach [9]. This review examines the application of mathematical considerations in scale-up calculations and explores various methodologies used in scaling up different unit operations for oral solid dosage forms. It aims to provide a systematic strategy to ensure the quality of finished dosage forms in the pharmaceutical industry.
Methods
Scale Up Process Basic Understanding
Using scientific approaches and mathematical calculations for process scale-up or scale-down can significantly reduce the risk of failure, ensure regulatory compliance, and lower costs associated with trial batches. These calculations help to establish robust and realistic parameters for scaling up or down pharmaceutical formulations [10]. When scaling unit process parameters, key considerations include equipment size, shape, working principle, and associated parameters. According to process modeling theory, processes are deemed similar if they exhibit geometric, kinematic, or dynamic similarity.
Scale-Up Strategy for Oral Solid Dosage Forms
The manufacturing of oral solid dosage forms tablets and capsules involves several key unit operations such as blending, granulation, milling, tableting, Wurster coating and film coating. Each of these operations requires a carefully planned scale-up strategy to ensure product quality and process efficiency. Detailed overview of the scale- up strategy for each unit operation are discussed.
A) Blending/Mixing in Pharmaceutical Manufacturing
Blending is a critical unit operation in the manufacture of oral solid dosage forms (e.g., tablets and capsules). It ensures uniformity of the final product by mixing active pharmaceutical ingredients (APIs) with excipients. Equipment used in pharmaceutical blending unit operations are Double-Cone Blenders, Bin Blenders, Octagonal Blenders, V-Blenders and Cubic Blenders [11,12]. The blend should have a degree of homogeneity during blending to ensure the quality of solid dosage forms, such as tablets and capsules [13,14]. The blend homogeneity is influenced by several factors, such as material attributes (for example particle size distribution, particle shape, density, surface properties, particle cohesive strength) and process parameters (for example blender design, rotational speed, occupancy level, and blending time) [15]. These factors affect the agglomeration and segregation of the blend during the blending process, which affect the blend homogeneity. However, experiments with appropriate scale up calculations are sufficient to confirm changes in the agglomeration and segregation of the blend caused by these factors [16]. Scale up considerations and current industry practices in scale up calculations for blending unit operations are presented in Table 1a. Different types of blenders (Figure 1) such as Mass Blenders, Ribbon Blenders, V Cone Blenders, Double Cone Blender, Octagonal Blender, Drum Blender, Bin Blenders and Vertical Blenders, working principles, key factors and advantages are presented in Table 1b.
Table 1a: Process parameters, quality attributes, scale up considerations and industry practices for Blending unit operation.
Figure 1: Different types of Pilot/commercial scale model blenders used in pharmaceutical blending unit operation; 1. Mass Blenders; 2. Ribbon Blender; 3. V Cone Blenders; 4. Double Cone Blender; 5. Octagonal Blender; 6. Drum Blender; 7. Bin Blender; 8. Vertical Blender.
Table 1b: Different types of blenders in pharmaceuticals and its working principles, key factors and advantages.
B) Granulation in Pharmaceutical Manufacturing
Granulation is a crucial process in the pharmaceutical industry, particularly in the manufacture of solid dosage forms like tablets and capsules. It involves the formation of granules from a mixture of powders, which can improve the properties of the final product. Purpose of Granulation is to improve flow properties, enhance compressibility, reduce dust and improve uniformity [17,18]. Currently pharmaceutical industry adapted different types of granulation methods such as
i). Dry Granulation
Involves compressing powders into slugs or sheets and then milling them into granules. This method is used when the API is sensitive to moisture or heat. The process includes roller compaction and slugging. Typically roller compactors are used in dry granulation process.
ii). Wet Granulation
Involves adding a liquid binder to the powder mixture, which forms a wet mass that is then dried and sized into granules. This method typically includes preparation of binder solution, granulation, drying and sizing. Typically high shear rapid mixer granulators are used for wet granulation.
iii). Semi-Wet Granulation
A combination of wet and dry process involves in this granulation techniques, where a small amount of liquid binder is used, and the granules are only partially dried. Typically low shear fluid bed granulators are used in semi-wet granulation process.
i). Dry Granulation – Scale-Up Consideration and Industry Perspectives
Dry granulation is an alternative to both direct compression and wet granulation, particularly suited for active pharmaceutical ingredients (APIs) that are sensitive to moisture, have poor flow properties, or possess other physicochemical characteristics that are incompatible with direct compression or wet granulation. Unlike wet granulation, dry granulation does not involve the use of solvents or additional heating, which can introduce challenges related to physical or chemical stability, especially in formulations with amorphous solid dispersions or those prone to chemical degradation. Dry granulation offers several advantages over wet granulation, including a simpler process that is particularly beneficial for APIs that are sensitive to heat or water [19]. The two most commonly used methods for dry granulation are roller compaction and slugging.
Roller Compaction (RC) is a dry granulation technique that simultaneously densifies and agglomerates the powder blend to achieve increased packing density and granule size. In this process, the blend is compacted into ribbons using rollers, which are then milled into granules. Roller compaction reduces the risk of segregation, minimizes dust formation, and produces ribbons that can be processed into granules with improved flow properties. These granules are suitable for various subsequent processes, such as sachet filling, capsule filling, or tableting. Different scales and schematic representation of the roller compaction process is depicted in the Figure 2.
Figure 2: Different types of Roller compacters a) Lab scale model b) Pilot/Commercial Scale c) Schematic presentation of Roller Compactor.
Scaling up of roller compaction involves utilizing traditional large- scale experimental designs to optimize the dry granulation process. This approach can be time-consuming and resource-intensive. To streamline scale-up and minimize the number of experiments, it is crucial to have a deep understanding of the process parameters and the attributes of both the ribbons and granules produced [20]. Key process parameters for roller compaction include roll gap, roll pressure, feed screw speed, roll speed, and roller shape. These parameters must be carefully adjusted to achieve the desired granulation outcomes [21]. The quality of the ribbon, which is the primary product of the roller compaction process, is assessed based on several attributes like Ribbon Density (an indicator of how compacted ribbon is), Ribbon Strength (reflects the mechanical strength of the ribbon), Ribbon Thickness (Affects the granule size and uniformity), Young’s Modulus (Measures the ribbon’s elasticity and rigidity), Ribbon Shape (Impacts the subsequent granule formation), and Moisture Content (Ensures the ribbon’s stability and suitability for further processing). By focusing on these parameters and attributes, it is possible to optimize the roller compaction process effectively while reducing the need for extensive experimentation. The rolling theory for granular solids developed by Johanson describes the pressure distribution along the rolls considering the physical characteristics of the powder and the equipment geometry. The dimensionless number frequently used in roller compaction is determined based on the Johanson theory [22]. Johanson proposed a model that predicts the density of ribbons made by roller compaction using the nip area and the volume between the roll gaps. Johanson proposed distinguishing two regions between the rolls, (i) a slip region where the roll speed is faster than the powder and there is only rearrangement of the particles and (ii) a nonslip region where the powder gets trapped between the rolls and becomes increasingly compacted until the gap. The transition from slip to nonslip region is defined by the so-called nip angle. In the nonslip region, it is assumed that the powder behaves as a solid body being deformed as the distance between the rolls narrows down to the gap. It is further assumed that the deformation has only one axial component such that it can be idealized as uniaxial compression. One source of discrepancy between the predictions of Johanson’s and Reynolds’ model and the ribbon density measurements is the different compaction behaviour of the powder in the roller compactor compared to uniaxial compression tests [23,24] demonstrated that the roller compactor and a compaction simulator lead to different ribbon densities and built a model to account for that difference.
Rowe et al. extended Johanson’s model and proposed a modified Bingham number (Bm*) that represented the ratio of yield point to yield stress as follows:
Where Cs is the screw speed constant, 0 is pre consolidation factor, ρtrue is true density, is circumference of the roll circle, D is the roll diameter, W is roll width, SA roll is roll surface area, S is roll gap, NS is feed screw speed and NR is roll speed. Bm* is easy to determine because the input parameters of Bm*consist of those that can be generally measured in the compaction process. The model- predicted values and the actual test results from WP 120 Pharma and WP 200 Pharma (Alexander werk, Remscheid, Germany) models are shown. By maintaining Bm*, it was possible to obtain a consistent ribbon density between the two operating scales. It was suggested that Bm* can be effectively used for the development of roller compaction scale-up [25]. Case studies suggest that dimensionless numbers for the prediction of ribbon density in dry granulation processes can be used successfully during the Scale-up process (Table 2).
Table 2: Critical process parameters, quality attributes, scale up considerations and industry practices for Roller compaction unit operation.
ii). Wet Granulation – Scale-Up Consideration and Industry Perspectives
Wet granulation is a key process in pharmaceutical manufacturing used to produce granules from powders by incorporating a liquid binder (Figure 3).
Figure 3: Different types of wet granulation process equipments used in pharmaceutical development a) Lab model rapid mixer granulator b) Pilot/Commercial scale rapid mixer granulator.
This process is crucial for ensuring that the final granules exhibit desirable properties such as uniformity, good flowability, and compressibility [26]. The choice of equipment for wet granulation includes high-shear Rapid mixer granulators (RMG) and low shear fluid bed granulators (FBG). RMG involves mixing powders with the binder in a high-shear environment. The impeller and chopper facilitate the formation of granules by applying mechanical forces. FBG involves spraying the binder solution onto the powder bed in a fluidized state. The fluidized bed aids in the uniform distribution of the binder and granule formation. Granulation Process involves the dry powders, including the active pharmaceutical ingredient (API) and excipients (e.g., fillers, disintegrants, lubricants), are blended to ensure a uniform distribution. The blended powders are loaded into the high-shear granulator’s mixing bowl. The liquid binder (e.g., water, ethanol, or polymer solution) is sprayed onto the powder bed. This binder helps in forming granules by adhering powder particles together. The impeller rotates on a horizontal plane, creating a high-shear environment that facilitates mixing and initial granule formation. The chopper, rotating either vertically or horizontally, breaks up large lumps and ensures the uniform size of granules by cutting and mixing [27]. Granulation end point determined by the granules continue to grow as the binder is added until they reach the desired size and consistency. The process is typically monitored to ensure that granules are not over granulated or under-granulated. The process is carefully controlled by adjusting parameters such as binder addition rate, impeller speed, and chopper speed. A predefined endpoint, based on granule size or moisture content, is set to determine when the granulation is complete. Scaling up a Rapid Mixer Granulator (RMG) involves translating process parameters from a smaller, laboratory-scale unit to a larger, production-scale unit while maintaining the desired granule quality and consistency (Table 3a and 3b). This process requires careful consideration of equipment design, power requirements, and process parameters. Below tabulated are the guide to some common scale-up calculations for RMG.
Table 3a: Critical process parameters, quality attributes, scale up considerations for RMG granulation unit operation.
Table 3b: Scale up considerations and industry practices for RMG granulation unit operation.
iii). Semi-Wet Granulation – Scale-Up Consideration and Industry Perspectives
Fluid Bed Processor (FBP) for granulation operates by passing hot air at high pressure through a distribution plate located at the bottom of the container, creating a fluidized bed of solid particles. This fluidized state, where particles are suspended in the air, facilitates drying. Granulating liquid or coating solutions are sprayed onto these fluidized particles through a spray nozzle, followed by drying with hot air. The fluidized bed processor operates on the principle of fluidization, where a gas (typically air) is passed through a bed of solid particles at a velocity sufficient to suspend the particles in the gas stream. Air is introduced through a perforated plate or distributor at the bottom of the bed, and as it flows upwards, it lifts the particles, making them behave like a fluid. During fluidization, various processes can be carried out: a binder solution or melt is sprayed onto the particles, causing them to agglomerate; hot air removes moisture from the particles; and a coating solution is applied, which is then dried. The air, now carrying moisture or coating material, exits through the top of the bed. Scaling up a FBP in the pharmaceutical industry involves several calculations and considerations to ensure that the process can be effectively transitioned from a laboratory or pilot scale to full- scale production [28,29]. The process must maintain product quality, efficiency, and compliance with regulatory standards. Here’s a detailed guide on scale-up calculations and key factors for Fluidized Bed Processors. Scaling up a FBP involves maintaining similar fluidization conditions and process outcomes as in smaller scales. Key principles include maintaining the same fluidization regime, similar granulation or coating characteristics, and ensuring that drying or granulation efficiency scales proportionally (Table 4).
Table 4: Scale up considerations and industry practices for FBP granulation.
C) Compression in Pharmaceutical Manufacturing
Tablet compression is a critical process in pharmaceutical manufacturing that involves transforming powdered or granulated substances into solid tablets (Figure 4).
Figure 4: Different types of compression machines used in pharmaceutical development a) Lab model Single Punch Tablet Press and b) Pilot/commercial Scale Single Rotary Tablet Press.
Compression is a critical and challenging step in tablet manufacturing. The way a powder blend is compressed directly impacts tablet hardness and friability, which are crucial for dosage form integrity and bioavailability. While the tablet press is essential for the compression process, the preparation of the powder blend is equally important to ensure it is suitable for compression. Understanding the physics and principles of the compression process is vital for managing these operations effectively. For high-dose or poorly compressible drugs, the study of compression becomes particularly important, especially when the relationship between compression force and tablet tensile strength is non-linear. A thorough grasp of compression dynamics also helps resolve many tableting issues, which often stem from various compression-related factors [30,31].
Compression Cycle
Understanding the different stages of the compression cycle is essential for comprehending how powder materials are compacted into tablets. It also provides valuable insights into the various formulation and compression variables that impact the quality of the finished tablet. Compression cycle is divided into following 4 phases: Pre-compression, Main-compression, Decompression and Ejection.
Pre-compression
As the name implies, pre-compression is the initial stage where a small force is applied to the powder bed to create partial compacts before the main compression. This is typically achieved using a pre- compression roller that is smaller than the main compression roller. However, the size of the pre-compression roller and the level of pre- compression force can vary based on the properties of the material being compressed. For instance, powders that are prone to brittle fracture may require a higher pre-compression force compared to the main compression force to achieve increased tablet hardness. In contrast, elastic powders need a gradual application of force to minimize elastic recovery and allow for stress relaxation. Optimal tablet formation is often achieved when the sizes of the main and pre- compression rollers and the forces applied are similar.
Main Compression
During the main compression phase, inter particulate bonds are formed through particle rearrangement, which is followed by particle fragmentation and/or deformation. For powders with viscoelastic properties, special attention to compression conditions is necessary, as these conditions significantly influence the material’s compression behavior and the overall tableting process.
Decompression
After the compression phase, the tablet experiences elastic recovery, which introduces various stresses. If these stresses exceed the tablet’s ability to withstand them, structural failures can occur. For instance, high rates and degrees of elastic recovery may lead to issues such as tablet capping or lamination. Brittle fractures can also occur if the tablet undergoes brittle fracture during decompression. To alleviate stress, plastic deformation, which is time-dependent, can occur. The rate of decompression also influences the potential for structural failure. Therefore, incorporating plastically deforming agents, such as PVP or MCC, is recommended to enhance the tablet’s ability to handle these stresses.
Ejection
Ejection is the final stage of the compression cycle, involving the separation of the tablet from the die wall. During this phase, friction and shear forces between the tablet and the die wall generate heat, which can lead to further bond formation. To minimize issues such as capping or laminating, lubrication is often used, as it reduces ejection forces. Powders with smaller particle sizes typically require higher ejection forces to effectively remove the tablets from the die. Industry perspective is to overall understanding the theoretical aspects of compression helps in selecting the optimal compression conditions for a given tablet product and at the same time can avoid the potential tableting problems thus saving significant time and resources.
D) Wurster Coating in Pharmaceutical Manufacturing
The Wurster fluid bed coating technique is renowned for its versatility and efficiency in coating applications [32]. This method is distinguished by its rapid heat and mass transfer capabilities and its ability to maintain temperature uniformity. Unlike traditional fluidized bed coating, which uses a more straightforward approach, the Wurster method employs a nozzle located at the bottom of a cylindrical draft tube to spray the coating solution. Particles are circulated through this tube, periodically passing through the spraying zone where they encounter fine droplets of the coating solution. This circulation not only ensures thorough mixing but also provides precise control over particle movement and coating quality. Wurster Coating Process is extensively utilized in the pharmaceutical industry for coating powders and pellets. Wurster systems can handle batch sizes ranging from 100 grams to 800 kilograms. This process is ideal for coating particles as small as 100 µm up to tablets. The Wurster coating chamber is typically slightly conical and features a cylindrical partition about half the diameter of the chamber’s bottom. At the base of the chamber, an Air Distribution Plate (ADP), also known as an orifice plate, is installed. The ADP is divided into two areas: the open region beneath the Wurster column, which allows for greater air volume and velocity, and the more restricted areas. As air flows upward through the ADP, particles move past a spray nozzle positioned centrally within the up- bed region of the ADP. This nozzle, which is a binary type, has two ports: one for the coating liquid and one for atomized air. The nozzle creates a solid cone spray pattern with a spray angle of approximately 30-50°, which defines the coating zone. The region outside the cylindrical partition is referred to as the down-bed area. The choice of ADP is based on the size and density of the material being coated. The height of the column regulates the horizontal flow rate of the substrate into the coating zone. As the coating process progresses and the mass of the material increases, the column height is adjusted to maintain the desired pellet flow rate.
Scaling up the Wurster coating process involves increasing the equipment size to handle larger batch capacities, ranging from small lab-scale units to industrial-scale machines (Figure 5).
Figure 5: Different types of Wurster coating equipments used in pharmaceutical development a) Lab model b) Pilot/Commercial scale model.
Larger systems require careful design to maintain consistent coating quality and process efficiency. Equipment dimensions, including the height and diameter of the coating chamber and the size of the Air Distribution Plate (ADP), must be scaled proportionally to ensure effective particle fluidization and coating (Table 5).
Table 5: Scale up considerations and industry practices for Wurster coating.
As batch size increases, maintaining optimal airflow dynamics becomes crucial. The airflow rate, velocity, and distribution must be adjusted to ensure uniform coating. Larger systems may require modifications to the ADP to accommodate increased air volume and maintain desired particle circulation and spray pattern. The configuration of spray nozzles needs to be scaled to match the increased batch size. Ensuring consistent liquid atomization and spray pattern is essential to achieve uniform coating thickness. In larger systems, multiple nozzles may be used to cover the expanded coating zone. Process parameters such as temperature, airflow, and coating solution viscosity must be carefully calibrated. Industry perspectives as scale- up introduces more variables, precise control of these parameters is necessary to maintain coating uniformity and avoid issues such as over or under coating. Scaling up involves adjustments in material handling to accommodate the larger volume and ensure smooth transfer and processing of the particles. This includes considerations for feeding systems, particle flow control, and uniform distribution within the coating chamber.
E) Film Coating in Pharmaceutical Manufacturing
Film coating is a widely used technique in pharmaceutical manufacturing to apply a thin layer of coating material onto tablets, and other dosage forms (Figure 6).
Figure 6: Different types of Film coating equipment used in pharmaceutical development a) Lab model and b) Pilot/commercial Scale film coating equipment.
This coating process enhances the appearance, improves the stability, and controls the release of active ingredients in pharmaceutical products. Different film coating formulations can be used to achieve controlled or modified-release properties. This allows for the gradual release of the drug over time, improving therapeutic outcomes and patient compliance. Film coatings can improve the appearance of dosage forms, making them more appealing to patients. Additionally, they can mask the taste of unpleasant drugs, making oral administration more acceptable [33]. Choosing the wrong film coating equipment or using subpar technology can lead to significant film coating defects. These defects can greatly affect the quality, efficacy, and appearance of pharmaceutical products. It’s essential to identify and address these issues to maintain product integrity and ensure compliance. Below is an overview of common film coating defects and their potential causes, as detailed in Table 6a. Scaling up of film coating processes in pharmaceutical manufacturing involves several important considerations to ensure that the coating process remains effective and consistent as production volumes increase Table 6b.
Table 6a: Pharmaceutical film coating defects, route cause and remedial action.
Table 6b: Scale up considerations and industry practices for Film coating.
Current Industry Persepctives
Current industry perspectives on scale-up calculations emphasize a comprehensive understanding of both the scientific and operational aspects of production. By leveraging the scale up calculations, advanced methodologies such as Design of Expert (DoE) and quality by design (QbD), along with a keen focus on cost, equipment selection, and regulatory compliance, pharmaceutical companies can navigate the complexities of scaling up oral solid dosage forms effectively. Adapting to technological advancements and maintaining a proactive approach to risk management will be crucial for success in an increasingly competitive landscape.
Conclusion
The scale-up of oral solid dosage forms (OSDFs) is a critical phase in pharmaceutical development that directly influences product quality, regulatory compliance, and market success. The successful scale-up of OSDFs is a multifaceted challenge that requires strategic planning and execution. By focusing on these critical factors integrated processes, quality assurance, economic considerations, regulatory compliance, technological advancements, risk management, and continuous improvement pharmaceutical industries can enhance their chances of delivering high-quality products to the market. As the industry evolves, maintaining a forward-thinking approach will be essential for navigating complexities and ensuring sustainable success in a competitive landscape.
Conflicts of Interest
The authors declare no conflict of interest
Acknowledgement
Authors acknowledge Dr. Sudhakar Vidiyala, Managing Director, Ascent Pharmaceuticals Inc. for his support and encouragement in writing this review article.
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Tobacco use remains a global health problem. It is the leading cause of preventable death. It causes and/or exacerbates many diseases, including cancer, COPD, cardiovascular disease, HIV infection and tuberculosis. All of these diseases are major public health challenges for the 21st century and require tobacco control efforts. Smoking cessation is an essential component in the treatment of smoking-related diseases. It improves patients’ quality of life and life expectancy, reduces respiratory decline, the risk of cardiovascular disease and HIV mortality, and contributes to the cure of tuberculosis. Healthcare professionals need to be involved in tobacco control and effective, evidence-based smoking cessation strategies.
Keywords
Smoking, Cancer, Cardiovascular, HIV infection, COPD, Tuberculosis
Introduction
In 2022, over 20% of the global population smoked. Despite a decline in smoking prevalence, it remains a major global health issue, responsible for over 8 million deaths including 1.3 million non-smokers, a year [1]. There is a clear link between tobacco use and a range of health problems, including cancer, COPD, maladies cardiovasculaires, HIV infection, tuberculosis (TB). Tobacco control is essential to address these issues.
Cancer, COPD, HIV Infection, Cardiovascular Disease, TB and Smoking
Cancer
The International Agency for Research on Cancer (IARC) [2] has recorded 20 million new cancer cases and 9.7 million cancer deaths in 2023. Smoking is a major risk factor for cancer, especially lung cancer (12.4% of all new cancer cases and 18.7% of all cancer deaths). The incidence of lung cancer is increasing worldwide and may increase by 47% between 2020 and 2040 [3].
Chronic Obstructive Pulmonary Disease (COPD)
It affects 10.3% of the global population, accounts for 4.7% of annual global mortality and has a significant economic burden. COPD is characterized by progressive, partially reversible airflow limitation caused by chronic inflammation of the airways. Smoking is the major risk factor for COPD [4].
Cardiovascular Disease
It is a leading cause of death, with nearly two million deaths annually attributed to smoking-related heart disease, including myocardial infarction, stroke, abdominal aortic aneurysm, and peripheral arterial disease. Smoking is a major risk factor for acute cardiovascular events, with smokers more at a younger age [5].
HIV Infection
39.9 million people worldwide are living with HIV; 630,000 die each year and 1.3 million are newly infected. The advent of antiretroviral therapy has reduced AIDS-related mortality, but the proportion of deaths from non-AIDS diseases has increased. Smoking is prevalent in this population; it causes cardiovascular and pulmonary (e.g. COPD, lung cancer) diseases and reduces life expectancy of life in patients [6-8].
Tuberculosis
In 2023, 7.5 million new cases of tuberculosis (TB) were diagnosed. TB was responsible for 1.3 million deaths, and 410,000 people developed multidrug-resistant or rifampin-resistant tuberculosis. Over 80% of TB cases and more than 90% of deaths occur in low- and middle-income countries. The main drivers of the TB epidemic remain the spread of HIV and the emergence of drug-resistant TB; however, tobacco use is estimated to account for 17.6% of TB cases and 15.2% of TB-related deaths in high-incidence countries [9,10]. Smoking (active or passive) increases the risk of tuberculosis infection, progression to TB disease, mortality and recurrence [11].
Stopping Smoking and Tobacco Control
Stopping Smoking: Component of Treatment for Smoking- related Diseases
Smoking is the leading cause of cancer, particularly lung cancer, but quitting improves the quality of life and life expectancy of cancer patients. It reduces the decline in lung function, the frequency of COPD exacerbations, the risk of death in patients with cardiovascular disease or HIV infection, but improves the prognosis of tuberculosis and adherence to anti-tuberculosis treatment [12]. Therefore, health professionals need to help smokers quit with evidence-based smoking cessation interventions [13,14].
Tobacco Control: Public Health Priority
The WHO Framework Convention on Tobacco Control (FCTC) [15], adopted by over 190 countries, aims to protect present and future generations from the health, social, environmental and economic consequences of tobacco consumption and exposure to tobacco smoke. It provides a framework for a global tobacco control strategy to reduce smoking prevalence and exposure to tobacco smoke.
Conclusion
Tobacco use is a major cause of many diseases with a significant impact on public health. Tobacco control has become a major public health issue that must mobilise governments and all healthcare providers.
Conflict of Interest
The authors have no conflict of interest to declare
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The paper presents a four-step process for using generative AI to solve a problem, such as motivating young people to apply for a police job in a small town. Step 1 is a simulated town hall meeting to discuss the local population’s responses and issues with school safety. Step 2 is the simulated open house meeting, where AI simulates a meeting to encourage volunteering for a career in local law enforcement. Step 3 simulates four different mind- sets among potential recruits for police office and police-school officer roles. Step 4 presents a set of questions and synthesizes the four answers to each question based upon AI’s simulation of the response of each mind-set. The paper shows the power of AI to become a colleague and “knowledge worker.”
Keywords
Generative AI, Mind genomics, Police recruitment, Synthesized mind-sets
Introduction
The negative perception of law enforcement among young people is a significant factor in their decision to pursue careers in the police force. The perceived risks, challenges, and long hours can deter candidates who seek more stable and less stressful careers. This leads to fewer candidates and, in turn, to issues in meeting demands for salaries, benefits, and additional training. It should come as no surprise that the overall pool of qualified candidates may continue to shrink, making it difficult for police departments to fill vacant positions and maintain adequate staffing levels [1].
To address this issue, police chiefs should reassess recruitment strategies and identify potential barriers to attracting young people to law enforcement careers. This may involve reaching out to local schools and community organizations, offering internship programs, mentorships, and career development opportunities, and engaging young people about the benefits and opportunities of becoming a police officer [2].
To address the topic, this paper presents four strategies using generative AI as a coach and mentor. The generative AI, ChatGPT 3.5, enables the user to simulate and synthesize situations and solutions in a short time, develop insights, and then validate these insights using empirical research. As such, the paper constitutes a “vade mecum,” a guide to how one might approach this vexing problem, doing so with generative AI in a matter of 24 hours, at low cost, with the opportunity of developing critical insights about the topic along with testable suggestions.
As technology advances and automation replaces traditional jobs, the need for skilled and dedicated workers in critical roles like law enforcement becomes even more pronounced. To solve this problem, generative AI can be used to analyze and understand the underlying factors driving young people’s decisions to pursue or avoid careers in law enforcement. This data-driven approach will enable tailoring messaging, training programs, and support systems to better meet the needs and expectations of prospective candidates, ultimately increasing recruitment success.
The immediate and severe issues regarding staffing in law enforcement was brought home in an example of a town, called here TOWNX. The challenge was put forward to use a combination of generative AI and human research to address the problem. Could a system be created which could address some of the seemingly impossible-to-solve problems?
This paper presents the synthesized approach to ameliorating some of the problem, although the problem may be far from actually solvable in its entirety. Nonetheless, the structure generated here emerged with the help of generative AI (specifically ChatGPT 3.5), along with the emerging science of Mind Genomics. The paper is presented as a “work in progress,” but which can be used immediately.
The actual paper comprises four different strategies. The first two strategies deal with the reports of “meetings,” and are meant to simulate what happened, giving the reader a sense of what is going on. The second two strategies deal with mind-sets of individuals who are the likely candidates, and then questions given to these mind-sets, and how each mind-set answers the same question.
Strategy 1
The town hall meeting to discuss how the local population “came up” with answers. This strategy is based upon the book Looking Backward by author Edward Bellamy, where the forecast of what would happen in the future is written as a historical account of what had already happened.
Strategy 2
The open house meeting, where the focus was on the simulation of a meeting held to help drive volunteering to become a police officer. The vision here was to get AI to bring the reader below the surface, to discuss questions — what they really mean and how candidates might be thinking about these questions — and issues dealing with recruitment.
Strategy 3
Simulating mind-sets. The AI was told that there exist different mind-sets among the potential recruits for the police office and police-school officer roles. The AI synthesized four different possible mind-sets and provided relevant insights into the mind-sets.
Strategy 4
Creating questions about the job and then generating likely answers that each of the four simulated mind-sets would give to the same question. These are the four mind-sets used in Strategy 3. It will be the questions and answers in Strategy 3 that will be used for the empirical Mind Genomics project and reported as an accompanying paper.
Strategy 1 — A Simulated Town Meeting
A simulated town meeting with generative AI can foster creativity and innovation by providing a platform for brainstorming and idea generation. The AI can introduce new perspectives and ideas that human participants may not have considered, leading to novel solutions. Additionally, the AI can facilitate real-time collaboration and idea sharing, enabling participants to build upon each other’s thoughts and concepts. By stimulating creative thinking and encouraging out-of-the-box solutions, the simulated town meeting with generative AI can inspire innovative approaches to complex problems.
A simulated town meeting could involve a diverse group of people coming together to discuss various problems and brainstorm potential solutions. The goal is to encourage open communication, collaboration, and creativity to address complex issues facing the community. By creating a space where everyone’s opinions are heard and valued, there is the hope that innovative ideas will emerge, and practical solutions will be developed.
One potential strategy within a simulated town meeting could be to introduce generative AI that is programmed to simulate discussions about different problems and propose solutions as if they were already solved. This could help stimulate thought and generate new perspectives that human participants may not have considered on their own. The value of having AI generate solutions lies in its ability to generate ideas quickly and without bias. The AI can analyze vast amounts of data and information to offer potential solutions that may not have been considered by human participants.
However, the lack of human creativity and intuition could be a weakness of this approach. While the AI is capable of generating solutions based on existing data and knowledge, it may struggle to come up with truly innovative or groundbreaking ideas that require a high degree of creativity. Human intuition and gut instincts play a crucial role in problem-solving by guiding decision-making processes and identifying opportunities that may not be apparent through data analysis alone. By relying solely on AI-generated solutions, there is a risk of missing out on the unique insights and perspectives that human creativity can offer.
Despite the potential limitations, a simulated town meeting utilizing generative AI has the potential to be a powerful tool for problem-solving. By leveraging the strengths of AI technology, such as quick data analysis and innovative idea generation, participants can benefit from a more efficient and effective problem-solving process. This approach can help foster collaboration, creativity, and diverse perspectives within the community, ultimately leading to more sustainable and impactful solutions.
Table 1 shows how the AI was prompted. Table 2 shows the summary of eight interchanges at the simulated town hall meeting.
Table 1: Prompting the AI to simulate a town hall meeting to discuss the problem.
Table 2: Part of the summarization of the town hall meeting, showing 11 different suggested topic areas, suggested by AI-created individuals assumed to have participated. The essence of the idea is shown in bold letter.
Strategy 2 — The Open House to Identify Ways to Encourage People to Volunteer
This second strategy focuses directly on using AI to provide interesting ideas to attract prospect candidates. Rather than solving a problem, as done in Strategy 1 above, the open house strategy focuses directly on the problem and how to solve it. Furthermore, this second strategy uses AI to simulate questions that would be given by the attendee, the importance of the question, as well as the motivating power of the answer. Even if the AI cannot really “dive” into the mind of the prospective candidate, the exercise itself provides a way to prepare oneself with a structured way to approach the necessary “back-and-forth” which can transform an audience member into a candidate. Table 3 shows the prompt given to the AI. Table 4 shows the AI results, comprising 14 questions along with the four answers to each question.
Table 3: Prompt given to the AI to simulate an open house devoted to recruiting.
Table 4: Results from the AI, showing 14 questions, and the four answers to each question provided by AI.
Strategy 3 — Using AI to Synthesize Mind-Sets Regarding Police Offers Specializing in School Safety
Generative AI is a powerful tool that can help companies identify the mind-sets of individuals interested in a specific job and create appealing slogans. By defining the task and asking AI to specify these mind-sets, companies can gain valuable insight into the potential target demographic for a particular job opportunity. The AI has the computational power to analyze vast amounts of data and come up with unique and innovative ideas that may not have been considered otherwise. Additionally, AI may be able to synthesize more nuanced aspects of mind-sets that are not easily quantifiable or represented in data. One potential benefit of using generative AI is that it can help companies better target their recruiting efforts by tailoring messaging to appeal to these synthesized mind-sets. The result would be more effective recruiting campaigns and ultimately result in finding the right candidates for the job. However, limitations to the effectiveness of generative AI in this context may include biases in the data used to train the AI model, which can result in inaccurate or skewed insights.
Table 5 shows the prompts given to the AI to generate mind-sets of individuals interested in a career or at least a job in school safety. Table 6 shows four mind-sets synthesized by AI for the professionalization in law enforcement and school safety.
Table 5: Prompts given to AI to generate mind-sets of individuals interested in a career or job in law enforcement and school safety.
Table 6: Four minds-sets synthesized by AI, interested in a career or least a job in law enforcement and school safety.
Strategy 4 — Have AI Generate Targeted Messaging Appropriate for Synthesized Mind-Sets and Then Test These Messages in Mind Genomics Studies with Target Age Respondents
Based on scientific principles of experimental psychology (psychophysics), statistics (experimental design, regression clustering), and consumer research (conjoint analysis), Mind Genomics ends up allowing the user to gain a deep and actionable understanding of human decision behavior, most important in the world of the everyday. Its rigorous, data-driven world view and methods provide a comprehensive analysis of the subject population for a topic, appropriate in our case for identifying key factors young individuals consider when choosing a career in law enforcement. By creating a unique profile of the target audience based on their responses to survey questions, Mind Genomics can reveal hidden patterns and trends in their thinking and decision-making processes. This helps a person or even a “chat bot” better connect with them on a personal level. In turn, the connection helps to create targeted messaging and recruitment campaigns which each alone and more strongly together, resonate with the specific needs and desires of young police officer recruits in Pennsylvania. The companion paper will show how Mind Genomics is used to evaluate the ideas generated in Strategy 4.
Strategy 4 provides a set of 21 questions, each with four answers, one answer from each mind-set. The objective of Strategy 4 is to show how a simple set of prompts (Table 7) end up creating a rich set of 21 questions and four answers to each question (Table 8). Table 8 was created in a matter of two iterations of Idea Coach in the BimiLeap. com platform, requiring less than a minute in total.
Table 7: The prompts used to create the questions and for each question four answers, one answer from each mind-set created and discussed in Table 6.
Table 8: The 21 questions created by AI, and for each question four suggested answers, one answer from each of the four mind-sets presented in Table 6.
Discussion and Conclusions
Generative AI has the potential to revolutionize the way individuals navigate their professional paths by providing creative and inventive solutions to common obstacles faced during the process of finding employment. By hosting town hall events or recruitment nights, individuals can openly discuss their professional goals and challenges, fostering a team effort to find solutions. Generative AI creates fresh perspectives which push the boundaries of conventional career paths, allowing the opportunity to explore unconventional ideas and find career paths that align with their passions and talents. Engaging in stimulating conversations with different AI-generated mind-sets can provide a diverse array of perspectives and valuable insights, broadening knowledge of different career possibilities and opening up new avenues for one’s future.
Generative AI also has the potential to completely transform the way we learn and create. Imagine a world where individuals can tap into the power of AI to generate new ideas, innovate, and discover their creative talents in ways they never thought possible. This technology opens up a whole new world of possibilities, inspiring people to think outside the box and pursue unconventional career paths previously deemed impossible.
Generative AI provides personalized learning experiences tailored to individual interests and preferences, sparking curiosity and igniting a passion for lifelong learning. Collaborating with AI to generate fresh ideas and explore new possibilities empowers people to unleash their creativity and think in innovative ways that were previously unimaginable.
In a future where generative AI is integrated into every aspect of our lives, it has the power to revolutionize the way we acquire knowledge, encouraging individuals to explore their creative side and venture into unconventional career paths. With generative AI as a guiding force, people can imagine new possibilities, dream bigger dreams, and pursue their passions with a newfound sense of purpose and excitement. One can only imagine what will emerge then, in terms of the practicalities of creating critical thinkers in school, and then having this critical thinking be part of the package one uses to create one’s job, and one’s future.
Acknowledgment
The authors would like to thank Vanessa Marie B. Arcenas and Isabelle Porat for their help in producing this manuscript.
Wilson JM (2012) Articulating the dynamic police staffing challenge: An examination of Supply and Demand. Policing: An International Journal of Police Strategies & Management 35(2).
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Purpose: Climate change poses a significant threat to human health, particularly affecting the endocrine system. This study aims to explore the impact of climate change on various endocrine pathways and its implications for morbidity and mortality rates.
Methods: A review of literature was conducted to investigate the relationship between climate change and the endocrine system. Relevant databases were searched for studies on the effects of temperature changes, air pollution, and vector-borne diseases on hormone levels and endocrine health. Factors influencing the degree of impact, such as climate-related stressors and individual susceptibility, were also examined.
Findings: Climate change exerts a notable influence on the endocrine system, leading to hormone imbalances and increased mortality rates. Direct and indirect effects of climate change events, including temperature changes, air pollution, and vector-borne diseases, contribute to these disruptions. Certain components of the endocrine system, such as the adrenal gland, thyroid gland, HPA axis, and reproductive organs, are particularly vulnerable to environmental changes.
Implications: Understanding the mechanisms by which climate change affects endocrine disorders is crucial for addressing this global health issue. Efforts to mitigate the impact of climate change on human health should consider the specific vulnerabilities of the endocrine system and prioritize interventions to minimize morbidity and mortality associated with endocrine-related conditions.
Climate change, primarily caused by human activities like greenhouse gas emissions from power generation, manufacturing, and deforestation, results in significant alterations in weather patterns and temperatures. This has profound implications for public health, increasing morbidity and mortality due to extreme temperature conditions [1,2]. Recent research spanning from 2000 to 2019 indicates that approximately 5 million deaths worldwide can be attributed to non-optimal temperatures, with a substantial portion occurring in East and South Asia, highlighting regional disparities in climate change impacts [3]. Furthermore, environmental factors influenced by climate change can impact the endocrine system, potentially leading to disruptions in hormonal balance and the development of disorders. This paper will explore the multifaceted impact of climate change on human health, shedding light on both direct consequences, such as mortality, and indirect consequences related to endocrine disorders. By examining these connections, the goal is to deepen our understanding of the challenges posed by climate change in the global public health landscape and contribute to discussions on mitigating its adverse effects to safeguard public health.
This literature review provides a comprehensive analysis of the effects of climate change on the endocrine system, an area that remains relatively unexplored in current knowledge. By mixing the results of different published studies, we explore new insights into how climate-related stressors such as temperature fluctuations, air pollution, and vector-borne diseases disrupt hormonal balance and contribute to increased morbidity and mortality. Our approach shows the direct and indirect pathways through which climate change produces its impacts and provides a detailed understanding of the mechanisms involved in such processes. This research not only fills a major gap in the literature but also highlights the urgent need to address endocrine health in the context of climate change.
Climate Change
Climate Change Mechanisms and Human Health
Climate change encompasses significant alterations in regional and global climate over time. These changes affect various climate parameters; average and peak temperatures, humidity, precipitation, atmospheric pressure, water salinity, and the shrinking of mountain and polar glaciers [4]. In 2020, the global average surface temperature rose by 0.94 degrees Celsius compared to the average between 1951 and 1980. Projections suggest that by 2100, this average could increase by 4 degrees Celsius above the average recorded between 1986 and 2005, significantly surpassing previous projections [5]. The primary driver of Earth’s warming is the emission of greenhouse gases resulting from human activities, particularly methane and nitrous oxide. These gases retain warmth within the lower atmosphere, leading to temperature increases which leads to both short-term and long-term threats to human health and well-being.
Extreme Events
Sub-Optimal Temperatures
Extremes in temperatures, both low and high, increase mortality rates. Deaths from suboptimal temperatures were estimated to be 9.43% of total deaths worldwide from 2000 to 2019 [6]. Being exposed to high temperatures is linked with an increased likelihood of emergency department visits and hospital admissions because of cardiovascular, respiratory, and endocrine disorders [7-9], the exact temperature thresholds for these health impacts may not be explicitly stated in the cited literature (Figure 1).
Figure 1: Annual average excess deaths due to non-optimal temperature and regional proportion for 2000-19 by continent and region.
Blauw and colleagues’ research, investigating the relationship between fluctuations in outdoor temperature and the prevalence of diabetes mellitus in the USA from 1990 to 2009, demonstrated that for every 1-degree Celsius increase in outdoor temperature, there could be an association with more than 100,000 new cases of diabetes in the country, Blauw also proposes that brown adipose tissue (BAT) metabolism correlates with temperature, where lower temperatures lead to increased fatty acid metabolism by BAT and increased insulin sensitivity, the opposite is hypothesized to be true. More data is needed to understand the direct link between the two [10]. This aligns with the interconnected nature of climate change and diabetes, knowing that individuals with diabetes are at a higher risk of dehydration and cardiovascular events during extreme heat. Evidence has also shown that high temperatures can trigger behavioural and mental disorders, leading to an increase in cases of anxiety, depression, and suicidal attempts [11,12]. Schwartz’s analysis of 160,062 deaths in Wayne County, Michigan, among individuals aged 65 or older found that patients with diabetes faced a higher risk of mortality on hot days, highlighting the vulnerability of this demographic to heat stress [13]. Associations were also observed between daily maximum temperatures and visits to the Emergency Department in Atlanta, Georgia, for cases of internal diseases including diabetes, emphasizing broader health implications related to rising temperatures[14]. An analysis of 4,474,943 general practitioner consultations in Great Britain spanning from 2012 to 2014 indicated increased odds of seeking medical consultation linked to high temperatures [15]. Furthermore, the African continent faces a heightened risk of rising temperatures, as projections suggest increasingly severe heat extremes occurring over shorter durations. Kapwata et al.’s findings suggest potential temperature increases of 4-6 degrees Celsius for the African region during the period 2071-2100, posing heightened vulnerability to heat-related illnesses, especially among young children and the elderly [16]. Overall, these findings underscore the urgent need to tackle the intricate interplay among climate change, rising temperatures, and the heightened susceptibility to diabetes and associated health complications.
Wildfires
High temperatures and low precipitation increase the risk of wildfires, which can directly lead to burns, injuries, and premature deaths [17]. Compounds released during wildfires, such as benzene and free radicals, can have far-reaching consequences on human health. In fact, the exposure to environmental pollutants from wildfires can disrupt the endocrine system, leading to imbalances in hormonal regulation. Specifically, the released compounds may impact the functioning of the digestive, hematopoietic, and reproductive systems, creating a cascade effect that elevates the susceptibility to endocrine disorders [18].
Floods and Storms
Floods, instigated by heavy rainfall and elevating sea levels, are one of the most prevalent and devastating natural disasters. They lead to injuries and drownings and exacerbate the risk of water and vector-borne diseases like dengue, malaria, yellow fever, and West Nile virus [19,20]. Diabetic patients have altered immune responses to different pathogens and are at risk of developing severe infections leading to increased morbidity and mortality.
Air Pollution
Air pollution results in decreased lung function in both children and adults, exacerbating the symptoms of asthma and chronic obstructive pulmonary disease (COPD). In addition, several studies have shown an association between air pollution and insulin resistance, leading to a heightened risk of developing diabetes (Figure 2) particulate matter smaller than 2.5 um increases the probability of developing diabetes and exacerbates the risk of diabetes-related complications [21,22].
Figure 2: Pathophysiology of air pollution and insulin resistance.
Food Insecurities
Extreme weather events may lead to decreased crop production, which can in turn lead to famines and malnutrition of populations leading to increased risk of infectious diseases [23].
Vulnerable Populations in Healthcare
Climate change may impact a particular section of a community more due to risk factors in those individuals. The vulnerable population in a community i.e. poor, elderly, disabled, children, prisoners, and substance abusers encounter heightened levels of mental and physical stress as a result of exposure to natural disasters. Reviews have shown that social factors affecting vulnerable individuals have been correlated with more or less capacity to adjust to changing environmental conditions and natural disasters [24]. In the upcoming decades, disparities may intensify, not solely due to regional variations in environmental changes such as water scarcity and soil erosion, but also due to disparities in economic status, society, human capital, and political influence [25].
Impact of Climate Change on Endocrine Disorders
Climate change exerts multifaceted effects on environmental factors, including temperature, pollution, and exposure to chemicals, which contribute to endocrine disruption. The interplay between these elements underscores the intricacy of the correlation between climate change and endocrine health. Temperature fluctuations, altered seasons, and extreme weather events induced by climate change can have significant repercussions on endocrine health [26]. Furthermore, altered seasonal patterns can disrupt circadian rhythms, affecting the production and regulation of hormones critical for various physiological processes. Extreme weather events, such as heatwaves or hurricanes, may cause stress responses in the body, potentially influencing the endocrine system and contributing to hormonal imbalances [27]. Climate-induced changes in food availability, quality, and contaminants also play a pivotal role in influencing endocrine disorders. Shifts in temperature and precipitation patterns can affect crop yields and nutritional content, impacting the availability of key nutrients essential for hormonal balance. Additionally, climate change may lead to the introduction of new contaminants into the food chain, potentially disrupting endocrine function. Pesticides, pollutants, and other environmental chemicals can mimic or interfere with hormones, contributing to the development of endocrine disorders [28].
Endocrine Disorders
Specific Endocrine Disorders and Climate Change
Thyroid Dysfunction: Research by Brent et al. (2010) reported that environmental agents influencing the thyroid may also trigger autoimmune thyroid disease, which is often the cause of functional thyroid disorders. Detecting abnormal thyroid function associated with environmental exposure is commonly attributed to direct effects of agents and toxicants triggering autoimmune thyroid problems with consideration to factors like thyroid autoantibody status, iodine intake, smoking history, family history of autoimmune thyroid disease, pregnancy, and medication use. Recognition of thyroid autoimmunity as a contributing factor to changes induced by environmental agents is crucial in these studies, illuminating the pathogenesis of autoimmune thyroid disease and exploring the possible influence of environmental factors in its development, a broad range of environmental pollutants can interfere with thyroid function; many of which have direct inhibition of thyroid hormone or induce detectable high levels of TSH consequently. Other substances such as polychlorinated biphenyls (PCBs) have a thyroid hormone agonist effect [29]. Additionally, air pollutants with levels as low as PM2.5 have been associated with hypothyroidism. There is robust evidence of increased cardiovascular morbidity and mortality associated with subclinical hypothyroidism (Figure 3) [30].
Figure 3: Overview of endocrine system.
Diabetes: Ratter-Rieck et al. (2023) reported that individuals with diabetes face heightened susceptibility to the dangers posed by elevated ambient temperatures and heatwaves due to impaired responses to heat stress. This is of particular concern as the frequency of extreme heat exposure has risen in recent decades, coinciding with the growing number of people living with diabetes, which reached 536 million in 2021 and is expected to rise to 783 million by 2045 [31]. Furthermore, evidence has linked PM concentrations to increased oxidative stress, impaired endothelial function, and it is also suggested that PM alters coagulation and inflammation cascades via epigenetic disruption [32].
A Chinese study concluded that higher PM10 levels are associated with an increase in mortality short term with a 5% overall mortality rate from diabetes with high levels of PM10 [32].
Reproductive Disorders: The “Lancet Countdown on health and climate change: code red for a healthy future report for 2021” emphasizes on the critical health risks posed by climate change [33], affecting human health through food shortages, water quality reduction, displacement, and increased disease vectors. The effects can manifest directly, such as heat stress and exposure to wildfire smoke impacting cellular processes, or indirectly, resulting in vector-borne diseases, population displacement, depression, and violence. It underscores the discrepancies in exposures among various sociodemographic groups and pregnant individuals. Vulnerable populations, including women, lower-income individuals, pregnant women, and children are disproportionately affected. The mechanisms underlying these effects involve endocrine disruption, reactive oxygen species induction, DNA damage, and disruption of normal cellular functions [33].
Regulatory Mechanisms
The endocrine system is regulated by feedback mechanisms involving the hypothalamus, pituitary gland, and target organs [34-36]. The feedback mechanism can be either positive or negative, depending on the effect of the hormone on the target organ [36]:
– A negative feedback mechanism occurs when the original effect of the stimulus is reduced by the output [36].
– A positive feedback mechanism occurs when the original effect of the stimulus is amplified by the output [35].
The hypothalamic-pituitary axis involves the production of hormones by the hypothalamus, which either stimulate or suppress the release of hormones from the pituitary gland [34]. Consequently, the pituitary gland produces hormones that stimulate or inhibit the release of hormones from other endocrine glands [34]. The target organs produce hormones that provide feedback, either positive or negative, to the hypothalamus and pituitary gland to regulate hormone production (Table 1) [34].
Table 1: EDC Drugs: Site of Action and Organ Impact.
EDC Name
Target Organ
Hormones Effects
Phthalates
Thyroid
Reduced free and serum T4 Increased TSH
Perchlorate
Thyroid
Inhibited Iodine uptake Inhibited thyroid synthesis Reduced neonate cognitive function
Diabetes mellitus is a condition defined by high blood glucose levels. Type 1 diabetes mellitus results from autoimmune destruction of pancreatic β-cells whereas, type 2 diabetes mellitus is due insulin resistance. Severe hyperglycemia symptoms include excessive urination, thirst, weight loss, and blurred vision. Chronic hyperglycemia can impair growth, increase infection susceptibility, and lead to life-threatening conditions like ketoacidosis or hyperosmolar syndrome. Long-term complications of diabetes include retinopathy, nephropathy, peripheral and autonomic neuropathy, cardiovascular diseases, hypertension, and lipid metabolism abnormalities [37].
Diabetes can be diagnosed using plasma glucose criteria, which include either the fasting plasma glucose (FPG) value, the 2-hour plasma glucose (2-h PG) value during a 75-gram oral glucose tolerance test (OGTT), or A1C criteria.
Thyroid Disorders
Hyperthyroidism
Hyperthyroidism is characterized by excess thyroid hormone production. Causes include thyroid autonomy and Graves’ disease, which affects younger women and the elderly [38]. Symptoms of hyperthyroidism include heart palpitations, tiredness, hand tremors, anxiety, poor sleep, weight loss, heat sensitivity, and excessive sweating. Common physical observations include tachycardia and tremor [39]. Diagnostic measures involve assessing thyroid hormone levels: elevated free thyroxine (T4) and triiodothyronine (T3), alongside suppressed thyroid-stimulating hormone (TSH) [40]. Treatment often starts with thionamide and radioiodine therapy, the latter is preferred, especially in the US, considering pregnancy risks for women [38].
Hypothyroidism
Hypothyroidism is a thyroid hormone deficiency. It occurs more frequently in women, older individuals (>65 years), and white populations, with a higher risk in those with autoimmune diseases [41,42]. Symptoms of hypothyroidism include fatigue, cold intolerance, weight gain, constipation, voice changes, and dry skin. However, the clinical presentation varies with age, gender, and time to diagnosis. Diagnosis relies on thyroid hormone level criteria. Primary hypothyroidism is defined by elevated TSH levels and decreased free thyroxine levels, while mild or subclinical hypothyroidism, often indicating early thyroid dysfunction, is characterized by high TSH levels and normal free thyroxine levels [43]. Early thyroid hormone therapy and supportive measures can prevent progression to myxedema coma [43-45].
Polycystic Ovary Syndrome (PCOS)
PCOS is a prevalent endocrine disorder among females, affecting 5% to 15% of the population. Diagnosis requires chronic anovulation, hyperandrogenism, and polycystic ovaries with Rotterdam’s criteria incorporating two of these three features [46]. PCOS is often underdiagnosed, leading to complications like infertility, metabolic syndrome, obesity, diabetes, cardiovascular risks, depression, sleep apnea, endometrial cancer, and liver diseases. Management involves lifestyle changes, hormonal therapies, and insulin-sensitizing agents [47].
Adrenal Disorders
Cushing’s Syndrome
Cushing syndrome, also known as hypercortisolism, is a condition resulting from high cortisol levels, often due to iatrogenic corticosteroid use or herbal therapies [48]. It causes weight gain, fatigue, mood swings, hirsutism, and immune system impairment. Treatment options include tapering exogenous steroids, surgical resection, radiotherapy, medications and bilateral adrenalectomy for unresectable ACTH tumors [49].
Addison’s Disease (Primary Adrenal Insufficiency)
Addison’s disease is a rare but life-threatening adrenal insufficiency. It primarily impacts glucocorticoid and mineralocorticoid production and can manifest acutely during illnesses. It is more common in women aged 30-50 and is often linked to autoimmune conditions. There are two main types of Addison’s disease: primary adrenal insufficiency, where the adrenal glands themselves are damaged, and secondary adrenal insufficiency, where the dysfunction is due to a lack of stimulation from the pituitary gland or hypothalamus [50]. Diagnosis is challenging due to its variable presentation, and it can lead to an acute adrenal crisis with severe dehydration and shock [51].
Pheochromocytoma
Pheochromocytomas are rare, benign tumors in the adrenal medulla or paraganglia, often causing symptoms like high blood pressure, headaches, heart palpitations, and excessive sweating [52]. These tumors are often linked to genetic mutations, and it is recommended that all patients undergo genetic testing. Symptoms are caused by overproduction of catecholamines. Radiological imaging helps locate the tumor and determine its spread and elevated levels of metanephrines or normetanephrines confirm the diagnosis. Surgery is the only effective therapy while drugs are used to address hypertension, arrhythmias, and fluid retention before surgery [53].
Adrenal Hemorrhage
Adrenal hemorrhage is a rare condition involving bleeding in the adrenal glands, causing a range of symptoms from mild abdominal pain to serious cardiovascular collapse. It is caused by various factors like traumatic abdominal trauma, sepsis, blood clotting issues, blood thinner use, pregnancy, stress, antiphospholipid syndrome, and essential thrombocytosis, it can occur unilaterally or bilaterally. Rapid diagnosis requires high clinical suspicion, and diagnostic techniques involve imaging and biochemical evaluations [54].
Primary Hyperaldosteronism
Also known as Conn syndrome, is a condition causing hypertension and low potassium levels due to excessive aldosterone release from the adrenal glands. It’s a common secondary cause of hypertension, especially in resistant cases and women. Diagnosis involves aldosterone-renin ratio assessment, confirmatory tests such as saline infusion tests or captopril challenge, and imaging studies like adrenal CT or MRI scans [55]. Treatment options include aldosterone antagonists, potassium-saving diuretics, surgery, and corticosteroids [56].
Medical Impact of Endocrine Disorders
Health Consequences
Cardiovascular Complications
Uncontrolled diabetes is associated with vessel damage due to atherosclerosis, which increases the risk of heart attacks and strokes [57-59]. Hyperglycemia also contributes to inflammation, oxidative stress, and endothelial dysfunction, further promoting the development of cardiovascular complications [52-54].
Metabolic Disturbances
Conditions with hormonal imbalances, like PCOS and hypothyroidism, cause weight gain and obesity. Obesity is a major risk factor for numerous health issues, including type 2 diabetes, heart disease, and joint problems [57-59].
Autoimmune Disorders
Autoimmune disorders are common in endocrine disorders such as Hashimoto’s thyroiditis and Graves’ disease [57-61]. These conditions can increase the risk of developing other autoimmune disorders, such as rheumatoid arthritis or lupus [57-59].
Quality of Life Implications
Physical Well-being
Many endocrine disorders, such as diabetes, are associated with physical symptoms such as fatigue, pain, weakness, and discomfort [62-64].
Emotional and Psychological Well-being
Hormonal imbalances affect neurotransmitters in the brain, leading to mood disorders such as depression, anxiety, and irritability [62-65].
Social and Relationship Impact
Individuals with endocrine disorders may experience social isolation due to their symptoms or the demands of managing their condition, limiting their social interactions [62-64].
Financial Burden
Treating and managing endocrine disorders often involves ongoing medical costs, medications, doctor visits, and surgical procedures. These expenses can create a financial burden on individuals and their families, impacting their overall financial well-being [62-64].
Self-esteem and Body Image
PCOS or hypothyroidism result in weight gain and changes in appearance, which may impact self-esteem and body image. Individuals with obesity-related endocrine conditions may experience societal stigmatization and discrimination, which can lead to emotional distress and lower self-esteem [62-64].
The Intersection: Climate Change and Endocrine Disorders
Endocrine Disruption Mechanisms
Endocrine Disrupting Chemicals (EDCs)
Endocrine disruptors, are foreign substances interfering with the endocrine system’s physiologic function, leading to detrimental effects. EDCs exposure may occur through food, water, and skin exposure in adults. Fatal exposure happens through placental transmission and breastfeeding. Direct effects are seen in the exposed population and subsequent generations. EDCs are the most well-known with more than 4000 agents polluting the environment [66]. EDCs mimic endocrine action by binding to a variety of hormone receptors and may act as either agonists or antagonists. Nuclear and membrane-bound receptors are typically the two messenger systems utilized by EDCs [67]. The most commonly recognized EDCs may be agricultural, and industrial chemicals, heavy metals, drugs, and phytoestrogens [66,67]. Human exposure mostly occurs through the act of consuming these substances that accumulate in fatty tissue due to their affinity for fat. Furthermore, endocrine-disrupting chemicals interfere with the production, function, and breakdown of sex hormones affecting the growth of the fetus and reproductive abilities. These factors are associated with developmental problems, infertility, hormone-sensitive malignancies, and disruptions in energy balance. Moreover, EDCs disrupt the functioning of the hypothalamic-pituitary-thyroid and adrenal axis. However, evaluating the complete extent of ECDs influence on physical health is difficult, because of the delayed consequences, diverse onset ages, and the susceptibility of certain demographics [67] (Table 2).
Table 2: Impact of Environmental Disruptors on Endocrine Pathways by Organ.
Organ
Environmental disruptor
Impact on endocrine pathways
Pituitary
Pesticides, PVC.
Precocious puberty. Delayed puberty. Disruption of circadian rhythm.
EDCs can interfere with hormonal homeostasis in different mechanisms such as affecting hormone synthesis, release, transport, metabolism, and action, mainly utilizing the structural similarity with thyroid hormones [68]. The impact of EDCs on the endocrine system is not fully understood. However, existing literature and medical research suggest an association between EDCs and endocrine disorders, with EDCs being implicated as potential causes of obesity, diabetes mellitus, and other diseases [28,69-72].
Phthalates, as a chemical commonly used in pharmaceuticals [73], have been described to have a negative association with free and total serum T4 and increased TSH levels [74] . Perchlorate, a significant EDC, mainly inhibits iodine uptake with doses as small as 5 mg/kg/day via reducing iodine uptake in the thyroid, with higher doses inhibiting thyroid synthesis [75]; its exposure risks reduced cognitive function in neonates [76] . In the adrenals, EDCs work by inhibiting or inducing enzymes, including steroid acute regulatory protein (StAR), aromatase, and hydroxysteroid dehydrogenases [77,78], involved in steroidogenesis with a minor role in aldosterone synthesis disruption [66] . Drugs like etomidate, ketoconazole, and cardiac glycosides may inhibit steroidogenesis by interfering with 11β-hydroxylase. The interference obstructs the process of converting 11-deoxycortisol into cortisol, which affects the production of glucocorticoids and mineralocorticoids (Figure 4).
Figure 4: Feedback regulation of hormone production: positive and negative.
Decreased cortisol levels impact the body’s reaction to stress and its metabolism, which may result in symptoms of adrenal insufficiency. Moreover, the interruption of aldosterone production might potentially lead to disturbances in electrolyte levels and the regulation of blood pressure. The many intricacies of this route emphasize the need of maintaining a precise hormonal equilibrium for optimal physiological functioning [66]. Furthermore, environmental toxins play a critical role in influencing reproductive health outcomes. Heavy metals and other environmental toxins can impair reproductive health in females by altering hormone function, leading to adverse reproductive health events. In males, these toxins could affect semen quality, altering sperm concentration, movement, and morphology [90,91] (Table 3).
Table 3: Climate change impact on different phases of reproductive life.
Phase
Impact Factors
Description
Preconception
Temperature Extremes
Affects fertility and sperm quality; heat stress can reduce conception rates.
Air Quality
Exposure to pollutants can reduce reproductive health and increase the risk of infertility.
Nutrition and Food Security
Poor nutrition can lead to decreased fertility; food insecurity affects overall health.
Water Quality and Availability
Contaminated water can lead to reproductive health issues; water scarcity impacts general health.
Pregnancy
Temperature Extremes
Higher risk of heat stress, preterm birth, and other complications.
Air Quality
Poor air quality can lead to pregnancy complications such as preeclampsia and low birth weight.
Nutrition and Food Security
Malnutrition can affect fetal development; food insecurity can lead to nutrient deficiencies.
Water Quality and Availability
Risk of waterborne diseases affecting maternal health.
Childbirth
Temperature Extremes
Increased risk during labor and delivery, particularly in hot climates.
Air Quality
Respiratory issues during childbirth due to poor air quality.
Water Quality and Availability
Clean water is essential for safe delivery; water scarcity and contamination can lead to complications.
Postpartum
Nutrition and Food Security
Impact on breastfeeding and recovery; malnutrition can affect milk production and maternal health.
Temperature Extremes
Heat stress can affect the health of both mother and infant.
Air Quality
Poor air quality can affect respiratory health of both mother and newborn.
Climate Change Factors Affecting Endocrine Health
Temperature Extremes and Hormonal Responses
Humans can survive in extreme environmental conditions and adapt to varying temperatures ranging from humid tropical forests to polar deserts [79,80]. Stress hormones and other stress-initiated systems have a major role in adaptive responses to environmental changes [81]. Mazzeo et al.’s study and Woods et al.’s investigation found that catecholamine and cortisol responses to physical exercise differ under conditions of hypoxia versus normoxia; with higher concentrations noted under hypoxic conditions [82,83]. The secretion of catecholamine (particularly adrenaline and noradrenaline) and cortisol during physical stress is influenced by the presence of either low oxygen levels (hypoxic) or normal oxygen levels (normoxic) in the body. When there is a lack of oxygen (hypoxia), the body responds by releasing more catecholamines, which increases the heart rate and respiratory rate. Moreover, the levels of cortisol are elevated, which promotes the mobilization of energy stores. Under typical oxygen conditions, exercise still stimulates the synthesis of catecholamines and cortisol, although the quantities may not reach the same levels as observed in low oxygen environments (hypoxia). The body’s physiological responses during physical activity are facilitated by these endocrine reactions, which are crucial for adapting to varying amounts of oxygen in the surroundings [84]. The endocannabinoids lipid mediators and N-acyl ethanolamines are closely linked to acclimation at various physiological levels, including central nervous, peripheral metabolic, and psychologic systems in response to environmental factors to achieve physiological homeostasis [85].
Environmental Toxins and Endocrine Dysfunction
Numerous epidemiological studies have indicated a link between exposure to environmental toxins and the risk of developing type 2 diabetes mellitus. Inflammation triggered by exposure to particulate matter (PM2.5) in air pollution is a prevalent mechanism that might interact with other proinflammatory factors related to diet and lifestyle, influencing susceptibility to cardiometabolic diseases [86]. Exposure to PM2.5 has been demonstrated to disrupt insulin receptor substrate (IRS) phosphorylation, leading to impaired PI3K-Akt signaling and inhibition of insulin-induced glucose transporter translocation [86] (Figure 5).
Figure 5: PM2.5 exposure impact
Foodborne toxins such as cereulide produced by Bacillus cereus might contribute to the increase in the prevalence of both type 1 and 2 diabetes through its uncoupling of oxidative phosphorylation by permeabilizing the mitochondrial membrane [87]. The production of mitochondrial ATP is crucial for generating insulin in response to glucose stimulation. Therefore, cereulide’s toxic effects on mitochondria could particularly harm beta-cell function and viability.
Altered Nutritional Patterns and Metabolic Health
Prudent diets including fruits, vegetables, whole grains, fish, and legumes have been associated with favorable effects on bone metabolism including lower serum bone resorption marker C-terminal telopeptide (CTX) in women; higher 25-hydroxyvitamin D (25OHD) and lower parathyroid hormone in men [9]. Also linked to favorable effects on glucose metabolism such as lower insulin and homeostatic model assessment insulin resistance (HOMA-IR) [9]. Consuming Western diets, characterized by the intake of soft drinks, potato chips, french fries, meats, and desserts, has been linked to adverse impacts on bone metabolism which include elevated levels of bone-specific alkaline phosphatase and reduced levels of 25OHD in women, as well as higher CTX levels in men [9]. They were also linked to higher glucose, insulin, and HOMA-IR [88].
Stress, Mental Health, and Endocrine Function
Climate-induced Stress and Hormone Levels
The primary responses to heat stress involve the activation of the hypothalamic-pituitary-adrenal axis, leading to a subsequent rise in plasma glucocorticoid levels. Epinephrine and norepinephrine are the main hormones elevated in prolonged exposure to environmental heat stress. Plasma thyroid hormones have been observed to decrease under heat stress as compared to thermoneutral conditions. Decreasing levels of thyroid hormones and plasma growth hormones have a synergistic effect in the reduction of heat production. Decreased growth hormone is necessary for survival during heat stress and Insulin-like growth factor-1 (IGF-1) has been found to be decreased during summer months [88].
Mental Health Implications for Endocrine Patients
Cortisol is a well-researched hormone in psycho-neuroendocrinology, crucial for stress-related and mental health disorders [92]. It’s assessed through serum, saliva, or urine for short-term levels, while hair cortisol can indicate long-term levels [93]. Stress triggers acute and chronic responses in cortisol release, affecting the HPA (hypothalamus-pituitary-adrenal) axis [88]. The HPT (hypothalamus-pituitary-thyroid) axis is also impacted by stress, showing transient activation and suppression with acute and chronic stressors [94]. Stress persistence is crucial in understanding stress reactions and their connection to cortisol levels and HPA axis dysfunction, which is linked to various mental disorders like major depressive disorder [95,96].
Empirical Evidence and Research Findings
Epidemiological Studies on Climate and Endocrine Disorders
Diabetes and Climate-Related Factors
Diabetes and climate change are interconnected global health challenges. Rising temperatures, heat waves, heavy rainfall, and extreme weather events are impacting diabetic patients [31,97]. These individuals exhibit heightened vulnerability to heat and climate-related stress due to impaired vasodilation and sweating responses, also diabetic patients’ susceptibility to complications will be increased [10]. Additionally, Mora et al., in their systematic review, showed that 58% of infectious agents were aggravated by climate change, posing a high risk of infections and their complications on diabetic patients with compromised immune systems [98].
Thyroid Function and Environmental Exposures
Recent research highlights the impact of environmental factors on thyroid function, assessed through TSH, FT4, and FT3 levels. Exposure to high levels of particulate matter (PM2.5) is associated with lower FT4 levels and a higher risk of hypothyroxinemia [99-101]. PM2.5, composed of fine particles carrying contaminants including heavy metals, is breathed and enters the circulation, causing disruption to thyroid function. Hypothyroxinaemia, caused by decreased levels of FT4, has a negative impact on fetal development throughout pregnancy. Exposure to PM2.5 also induces inflammation and oxidative stress, which further disrupts the control of the thyroid [102]. In adults, elevated TSH and decreased FT4 levels were associated with long-term exposure to nitric oxide and carbon monoxide [103]. Additionally, outdoor temperature was negatively linked to TSH and FT3 but positively correlated with free thyroxine and the FT4/FT3 ratio. A 10 μg/m3 increase in fine particulate matter (PM2.5) was linked to a 0.12 pmol/L decrease in FT4 and a 0.07 pmol/L increase in FT3, with a significantly negative association between PM2.5 levels and the FT4/FT3 ratio [104].
PCOS and Dietary Changes
Diet and lifestyle choices are pivotal in the development and management of PCOS. Key factors include addressing insulin resistance, considering weight and body composition, adopting a balanced diet, and the potential benefits of a low-glycemic Index (GI) diet. Tailored dietary plans aim to reduce chronic inflammation through increased antioxidant intake [105]. Additionally, research indicates a link between air pollutants, such as Q4, PM2.5, NOx, NO2, NO, and SO2, and a higher risk of PCOS, as shown by Lin et al in 2019 [106].
Case Studies in Climate-Impacted Regions
Vulnerable Populations and Healthcare Access
Climate change debates must consider demographic groups’ disproportionate healthcare access and vulnerability. Low-income communities, elderlies, children, disabled people and those with pre-existing endocrine disorders face unique climate change challenges. Climate gentrification harms vulnerable residents’ health. Environmental pollutants and endocrine disorders are more common in marginalized climate-vulnerable communities. Addressing vulnerable populations’ healthcare access disparities to reduce climate change’s endocrine and health effects is essential. Vulnerable populations in climate and health research should not be seen as homogenous due to age, health issues, geography, or time. Climate assessments should address health and inequality.
A systematic overview highlights the unique challenges faced by women in low- and middle-income countries (LMICs) due to climate change and natural disasters. The review suggests a wide range of harmful effects on female reproductive health, noting that the mean age of menarche has decreased due to climate change. Additionally, some research found a statistically significant relationship between rising global temperatures and adverse pregnancy outcomes. All of these effects are more pronounced in LMICs, which are more severely impacted by climate change [107].
Environmental Justice Considerations
Climate and endocrine diseases research must consider environmental justice which involves all socioeconomic groups in lawmaking, implementation, and enforcement. Climate-related harm is unequally distributed by population exposure, sensitivity, and adaptation [1]. Climate risks and environmental degradation disproportionately affect low-income and minority groups, who often face greater exposure to environmental hazards and have fewer resources to adapt or recover. This exacerbates health disparities and highlights the need for inclusive environmental justice policies. Additionally, there should be more consideration of vulnerable groups within these communities, who are at higher risk from climate-related events [107].
Mitigation and Adaptation Strategies
Climate Change Mitigation Efforts
There are two categories of mitigation efforts; reduction of further greenhouse gas emissions and creation of carbon sinks to decrease atmospheric greenhouse gas. The Intergovernmental Panel on Climate Change, held in March 2023, called for global action to limit worldwide temperature increase to less than 1.5 C when compared to the preindustrial period [108]. Per the World Health Organization, a global effort requires political collaborations with medical professionals, which has prompted medical representation at United Nations Framework Convention on Climate Change (UNFCCC) meetings and Conference of the Parties (COP) [109]. Local and national endocrinology groups have been promoting climate change-based policies including suggesting a climate change agenda in the 2022-2026 Environmental Protection Agency strategic guidelines in the United States of America [110].
In addition, practitioners can contribute to the fight against climate change by adopting environmentally friendly practices and promoting treatments that lead to decrease greenhouse emissions and improve health [111]. One method physicians can use is carbon accounting, where physicians measure the amount of disposable goods that have been discarded by their practice and take measures to decrease usage [112]. When ordering supplies for medical centers, physicians can consider environmentally preferable purchasing, such as focusing on reusables, which supports companies committed to mitigating the impacts of product production on climate change [113].
Personal and Community Health Measures
By recognizing the importance of lifestyle modifications for both endocrine disorders and climate change, individuals can take control of their health while actively contributing to environmental preservation. By choosing to use their muscles rather than motors, people can both lessen their release of greenhouse gases and increase their calorie expenditure. Engaging in regular physical activity stands as one of the pillars of maintaining good health; it can enhance brain health, facilitates weight management, lower the risk of various diseases, strengthens bones and muscles, and enhances the capacity to perform daily tasks [114]. Food consumption has a direct relationship with the global epidemics of Obesity and Diabetes Mellitus [115,116]. Improved dietary decisions can lead to enhanced health outcomes for both individuals and the environment. Diets rich in plant-based foods and lower in meat consumption (flexitarian diets) not only contribute to health advantages but also diminish greenhouse gas emissions originating from agriculture [117].
EDCs disrupt hormone biosynthesis, metabolism, or function, leading to deviations from normal homeostatic control or reproductive processes. There is growing evidence that these hazardous chemicals contribute to the increased prevalence of cancers, cardiovascular and respiratory diseases, allergies, neurodevelopmental and congenital defects, and endocrine disruption. The Ostrava Declaration, adopted by the Sixth Ministerial Conference on Environment and Health in 2017, encourages the substitution of such hazardous chemicals and improving information availability [118].
Policy and Advocacy in Medical Research
Government Initiatives and Regulations
Integrating Climate and Health Policies
Governmental and nongovernmental bodies around the world such as CDC, WMO, WHO, and BAMS are increasingly recognizing the link between climate change and public health. Integrating climate and health policies is essential to mitigate the adverse effects of climate change by implementing regulations to reduce air pollution, vector-borne disease control, and supporting research into climate-related health impacts [119].
Surveillance and Reporting
A study on adaptation efforts by 117 UNFCCC parties established a global baseline for adaptation trends. National Communications data unveiled 4,104 distinct initiatives. Despite advanced impact assessments and research, translating knowledge into actions remains limited. Infrastructure, technology, and innovation dominate reported adaptations. Common vulnerabilities include floods, droughts, food and water safety, rainfall, diseases, and ecosystem health. However, vulnerable sub-populations receive infrequent consideration in these initiatives [120]. Also, Greenhouse gases are one of the important factors for climate change so (MERV) guidelines which stand for minimum efficiency reporting value and consist of four guidelines which are needed for the greenhouses to accurately determine their net GHG, and other, benefits: [121].
Improve data reliability for estimating GHG benefits.
Enable real-time data to allow adjustments during the project.
Establish consistency and transparency in reporting across various project types and contributors.
Boost project credibility with stakeholders.
NGO Efforts in Medical Research
Advocacy for Environmental Health
NGOs act as catalysts in advocating for environmental health policies and practices, with a significant presence in international environmental governance. While they are recognized as essential contributors to environmental protection, there’s a lack of systematic evaluations of their roles, especially in relation to other governing bodies [122].
Promoting Evidence-Based Practices
NGOs contribute significantly to medical research by promoting evidence-based practices instead of data analysis only within healthcare systems. They work to ensure that medical interventions and treatments are based on rigorous scientific research, which ultimately leads to better patient outcomes. In addition, NGOs play a vital role in connecting scientific breakthroughs with clinical application, guaranteeing that the most recent research insights translate into tangible advantages for patients in everyday practice.
Challenges and Public Health Implications
Climate change can have a significant impact on the incidence, diagnosis, and management of endocrine disorders. For instance, exposure to extreme temperatures, air pollution, and other environmental factors can disrupt the endocrine system and lead to hormonal imbalances [123].
The public health implications of endocrine disorders are significant. These conditions can cause long-term disability, reduce the quality of life of affected individuals, and increase the risk of mortality. The burden of endocrine disorders is expected to increase in the coming years due to climate change and other factors [123].
To mitigate the effects of endocrine disorders in the context of climate change, several strategies can be employed. These include reducing exposure to environmental toxins, promoting healthy lifestyles, and investing in research to better understand the link between climate change and endocrine disorders [124]. Additionally, public health campaigns can be launched to raise awareness about the risks of endocrine disorders and the importance of early diagnosis and treatment [125].
Technology can play a crucial role in mitigating the effects of endocrine disorders in the context of climate change. For instance, digital technologies can monitor and address the increase of climate-sensitive infectious diseases, thereby safeguarding the well-being of communities worldwide [125,126]. Additionally, telemedicine can be used to provide remote care to patients with endocrine disorders, reducing the need for in-person visits and improving access to care [127]. However, the use of technology in healthcare also poses several challenges. For example, the use of electronic health records (EHRs) can lead to privacy concerns and data breaches. Additionally, the use of artificial intelligence (AI) in healthcare raises ethical concerns, such as the potential for bias and discrimination.
Future Research Directions in Medical Climate Science
Knowledge Gaps and Areas of Uncertainty
While extensive research, primarily derived from animal studies, has shed light on the potential hazards, there are critical gaps in our knowledge regarding EDCs exposure routes and threshold concentrations. Comprehensive assessments of drinking water supplies are essential to bridge these gaps. Additionally, questions persist about the long-term impacts of EDC exposure, especially during crucial developmental stages, highlighting the need for further studies to unravel these uncertainties [69].
Long-Term Health Projections
The sudden surge in metabolic disorders, reproductive abnormalities, endocrine dysfunction, and cancers raises concerns about long-term health projections. With the escalation of these diseases linked to global industrialization and the pervasive presence of EDCs in our environment, understanding the future health landscape is crucial [28]. Long-term health projections must consider the complex interplay of EDCs with human biology, emphasizing the importance of ongoing research to anticipate and mitigate potential health crises in the coming years.
Artificial Intelligence (AI) and Technological Advancements and Innovative Solutions
Machine learning models, such as the optimized Random Forest (RF) models, have emerged as powerful tools to address knowledge gaps. These models, utilizing simple descriptors and data from extensive projects, provide accurate predictions of EDC effects, enabling a better understanding of the risks associated with various substances [128]. Incorporating AI in prioritization and tiered testing workflows not only fills existing knowledge gaps but also propels us toward more efficient and precise screening processes. These advancements also facilitate the development of targeted interventions and policies, ensuring a healthier future for generations to come.
Future Perspective and Conclusion
Further research should focus on finding relationships between various endocrine disorders and different environmental factors such as climate change and environmental toxins. Extensive understanding of the causative factors will facilitate the development of medical treatments and public health policies. Embracing diverse perspectives can enhance our understanding and foster breakthroughs. To conclude, urgent action should be taken to challenge the effects of climate change on endocrine disorders; public health policies should be implemented to decrease overall exposure to endocrine disrupting chemicals by utilizing testing of various substances to reduce potential exposure in different environmental settings. Additionally, technology could be integrated into public health and preventive medicine for purposes of disease tracking and increasing accessibility to telemedicine regardless of the patient’s physical location. Although promising, other factors should be taken in consideration when technology is used such to maintain patient’s confidentiality, including privacy concerns and ethical dilemmas regarding patient data. Effective strategies demand community education, NGOs involvement, and continuous monitoring. Addressing challenges, including financial inconsistencies, requires implementing measures that promote resilience.
Declarations
Acknowledgement
The authors would like to acknowledge ACC Medical Student Member Community’s Cardiovascular Research Initiative for organizing this project.
Funding
Authors received no external funding for this project
Conflicts of Interest
Authors wish to declare no conflict of interest.
Authorship contributions
Conceptualization of Ideas: Abdulkader Mohammad, Adriana Mares, Aayushi Sood
Data Curation
Abdulkader Mohammad, Adriana Mares, Aayushi Sood
Visualization
Abdulkader Mohammad, Adriana C. Mares, Abd Alrazak Albasis, Mayassa Kiwan, Sara Subbanna
Writing of Initial Draft
Abdulkader Mohammad, Adriana C. Mares, Abd Alrazak Albasis, Mayassa Kiwan, Sara Subbanna, Jannel A. Lawrence, Shariq Ahmad Wani, Bashar Khater, Amir Abdi, Anu Priya, Christianah T. Ademuwagun MS, Kahan Mehta, Nagham Ramadan, Anubhav Sood
Review and Editing
Abdulkader Mohammad, Adriana C. Mares, Harsh Bala Gupta, Aayushi Sood
Declaration of Interest
None.
Disclosures
The authors have no conflicts of interest to disclose. 1) This paper is not under consideration elsewhere. 2) All authors have read and approved the manuscript. 3) All authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. 4) The authors have no conflict of interest to disclose
Abbreviations: 25OHD: 25-Hydroxyvitamin D; ACTH: Adrenocorticotropic Hormone; AHA: American Heart Association; AI: Artificial Intelligence; BAMS: Bulletin of the American Meteorological Society; BAT: Brown Adipose Tissue; CDC: Centres for Disease Control and Prevention; COP: Conference of the Parties; COPD: Chronic Obstructive Pulmonary Disease; CTX: C-terminal Telopeptide; EDCs: Endocrine Disrupting Chemicals; EHRs: Electronic Health Records; FPG: Fasting Plasma Glucose; FT3: Free Triiodothyronine; FT4: Free Thyroxine; HbA1c: Hemoglobin A1c; HOMA-IR: Homeostatic Model Assessment Insulin Resistance; HPA: Hypothalamic-Pituitary-Adrenal; HPT: Hypothalamic-Pituitary-Thyroid; IRS: Insulin Receptor Substrate; IGF-1: Insulin-like Growth Factor-1; LDL: Low-Density Lipoprotein; LMICs: Low- and middle-income countries; MRI: Magnetic Resonance Imaging; MERV: Minimum Efficiency Reporting Value; NO2: Nitrogen Dioxide; NOx: Nitrogen Oxides; OGTT: Oral Glucose Tolerance Test; PCOS: Polycystic Ovary Syndrome; PG: Plasma Glucose; PI3K-Akt: Phosphatidylinositol-3-kinase-Protein Kinase B; PM2.5: Particulate Matter 2.5; SO2: Sulfur Dioxide; T3: Triiodothyronine; T4: Thyroxine; TRH: Thyrotropin-Releasing Hormone; TSH: Thyroid-Stimulating Hormone; UNFCCC: United Nations Framework Convention on Climate Change; WHO: World Health Organization; WMO: World Meteorological Organization
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Postpartum psychosis is a unique and serious mental health challenge. Women are more vulnerable to mental illness surrounding childbirth due to genetic, hormonal and psychosocial factors. The most severe form of postpartum mental illness, postpartum psychosis affects 1 to 2 of 1,000 women or 4,000 or more women in the U.S. each year (Friedman et al.2023; Griffen 2023; Perry et al. 2021; Postpartum Support International: PSI statement on psychosis related tragedies 2004; VanderKruik et al. 2017). These mothers are at an increased risk of suicide (4 to 5 percent), and infanticide, neonaticide and filicide (1 to 4 percent). For 40 to 50 percent of those with postpartum psychosis, this is a first occurrence with no prior history of mental illness (Friedman et al. 2023; Griffen 2023; MGH Center for Women’s Mental Health: Reproductive Psychiatry Resource & Information Center 2018; Perry et al. 2021; Postpartum Support International: PSI statement on psychosis related tragedies 2004). To prevent tragic outcomes, mothers with postpartum psychosis or severe depression with psychotic features require crisis intervention, immediate hospitalization and psychiatric treatment.
The article, “The criminalization of women with postpartum psychosis: a call for action for judicial change,” published in the Archives of Women’s Mental Health, promotes postpartum criminal laws in the U.S. and abroad when criminal culpability is linked to maternal mental illness. Similarly, it is essential to include postpartum psychosis as a diagnostic criteria and classification in the Diagnostic and Statistical Manual of Mental Disorders (DSM) (American Psychiatric Association 2013; Spinelli 2021).
These changes would have enormous influence in trial and sentencing when homicide cases are a consequence of maternal mental illness.
In the U.S., the judicial system has not utilized the advancements and growing body of scientific developments regarding reproductive mental health in the last decades in psychiatry and medicine. This is evident when women with postpartum mental illness are prosecuted. In more than thirty countries worldwide, there is treatment, rehabilitation and mitigation when women commit infanticide as a consequence of psychosis and severe postpartum mental illness in the year following childbirth.
In 1938, the U.K. emphasized treatment and prevention over punishment (Infanticide Act 1938) by adopting laws safeguarding mothers suffering from postpartum depression or psychosis. Currently in the U.K and many other countries, a woman who causes the death of her child within 12 months of delivery is presumed to be mentally ill.
In the U.S., Illinois is the only state that considers maternal mental health as a factor in cases of infanticide. Public Act 100–0574 signed into law in January 2018 and amended in 2019 by PA 101– 411, recognizes postpartum depression and postpartum psychosis as mitigating factors to be considered in trial and sentencing. This law allows women who are currently incarcerated to file for resentencing and allows consideration of postpartum mental illness in past, present and future cases.
It is essential to prioritize awareness and prevention of postpartum mental illness to save the lives of mothers and babies. We must enact comprehensive postpartum laws in every state to address screening and treatment as well as to consider mental health as a mitigating factor when unrecognized and untreated mental illness leads to tragedy and involvement with the criminal justice system. The time is NOW to enact postpartum criminal laws and judicial change throughout the U.S. and abroad.
Purpose: To challenge the collaborative process in a young research team with evidence on building research collaboration in university departments.
Methods: A structured literature review was combined with a hermeneutic analysis of data from a double survey conducted during a one-week seminar. Eight Norwegian participants provided data through a Strengths, Weaknesses, Opportunities, and Threats (SWOT) template.
Results: The literature review revealed two themes 1) Building a research network, and 2) Networking across university units. A naïve reading of the double survey data showed that participants enjoyed collaborating in research networks. A structured interpretation provided a contextual report on collaborative research processes across university units working to build research collaboration.
Conclusion: Excellent research collaboration emerges through focus, flexibility, trust, persistence, and leadership. A successful research group is dependent on positive engagement between members, the acknowledgment of individual contributions and ideas; and supportive team leadership which is especially facilitated through dialogical leadership.
Keywords
Hermeneutic analysis, Literature review, Leadership, Competence development, Qualitative, SWOT
Introduction
Research collaboration refers to “the working together of researchers to achieve the common goal of producing new scientific knowledge” [1]. In this context, occupational professionals who work in research and development are strategically managed [2] to improve knowledge transfers through transformational leadership [3]. This process is critical, as research collaboration is fundamental to scholarly research success. However, it is often difficult to build a collaborative research team [4]. To clarify the characteristics of such an endeavor, this study reviewed the literature on building collaborative research teams, then compared the results using a collaborative process experienced by a young, publicly funded healthcare research team that spanned multiple university units.
Background
Our initial literature review yielded 443 articles, of which we retained 394 after removing duplicates (Figure 1). Two of the authors then conducted independent screenings, resulting in 23 for potential inclusion. After reviewing the full texts of each, the authors excluded 15 for focusing on collaboration between international teams or separate universities rather than intradepartmental collaboration. Thus, the final sample contained eight articles, with various settings in the United States, Canada, Greece, the United Kingdom, and Ireland. One article introduced a new method for developing strategic research plans [5], while another investigated several issues at a specific research center, including collaboration, multidisciplinary approaches, support, and dissemination [6]. The remaining six articles primarily discussed the experiences of their respective authors and offered relevant reflections [7-12]. No article in our final sample provided a substantial literature review on the process of building a collaborative research team across different units within the same university department. Based on the evidence from these articles, we identified two main themes, including 1) Building a research network and 2) Networking across university units. An additional literature review conducted by two of the authors, in July 2023, did not result in new publications being included, so this topic does not appear to have had recent international research focus.
Figure 1: Flowchart of the literature review process
Building a Research Network
Organizational factors are essential for building research collaboration. To achieve success, three such factors are particularly important: leadership [5-7,9,12], mentorship [5-7,9,12] and cultural background [12]. In this regard, team leaders should promote team learning, serve as role models, support a favorable climate for cooperation, explain rational decisions, and help team members attain self-efficacy [9]. Thus, skilled team leadership and support are critical provisions for a thriving collaborative research team [6]. In a specific example, Best et al. [5] found that the research community was more likely to remain engaged and informed when the team leader frequently sent informative and humorous emails. A collaborative research team provides a platform of interaction for junior and senior researchers, thus facilitating training and mentorship. In the university context, team membership also helps individual researchers avoid isolation, while providing them with more opportunities to complete their own research [6]. According to Davis et al. [12], various challenges may arise when attempting to build a university-based collaborative research team, especially given the existence of different cultural backgrounds, heterogenous responsibilities, various academic practices, cultural factors, and politics. To establish an excellent, research-intensive environment, teams should help all members discuss their methods and struggles in ways that can unite them toward common goals [6].
Networking Across University Units
A researcher’s ability to network across university units depends on their individual values [7-8,10,12], the time available for research [5-7,12] and computer technology [7]. Meanwhile, an excellent collaborative research team requires focus, flexibility, trust, persistence, and leadership, which are developed through combined personal interests and common purposes. Thus, team success requires mutual respect for individual ideas and contributions as well as transparency during each step of the process [7]. Regarding issues faced by individual researchers, four articles mentioned the challenge of finding time to contribute to research teams [5-7,12], while another noted the constraints associated with simultaneous involvement in several international projects [7]. Under such conditions, it is crucial for both the whole group and individual team members to accept varying degrees of participation at different stages [9]. Based on their experiences in the healthcare field, Best et al. [5] explained how successful research collaboration could increase individual involvement in team aims while facilitating knowledge transfers to students and patients. Three articles emphasized that computer technology is essential for maintaining cooperation across university departments [6-7,9]. In one study, Steinke et al. [7] pointed out that personal computer skills are likely to vary among team members, which may create difficulty. Moreover, the strength and quality of the internet connection may pose challenges in cases where team members need to travel or communicate from different time zones during meetings [7]. Overall, these reports suggest that managers must remain aware of how research collaboration is influenced by personal values, contextual management, mentorship, and the time needed to conduct research. At the same time, the interpersonal elements of the research process depend on mutual trust, focus, and flexibility. In addition to the literature review, we conducted a qualitative study [13] based on a double Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. Specifically, the SWOT analysis employed a modified standard tool among Norwegian members of a university research team to identify key factors that influenced group performance; these factors were further examined to enhance strengths, optimize opportunities, improve weaknesses, and attenuate threats [14].
Method
Literature Search
We initially gathered evidence on collaborative research team building by searching databases with the assistance of a university librarian, including CINAHL, Medline in PubMed, and PsycInfo. We created search terms using different combinations of the following words: scholarly activities, research, nursing/nursing research, national relation/national cooperation, teamwork, cooperative behavior, and collaboration. For returned articles, we set the following inclusion criteria: peer-reviewed studies with abstracts/full-text articles from 2010 to 2020. These were searched using the Boolean/ phrase technique. We repeated the initial literature search in July 2023 for the time period 2020-2023 and added no new articles to our analysis and findings.
Participants in SWOT Analysis
The SWOT participants included eight members of a university research team established in 2017. Specifically, the team was comprised of four scholars, two lecturers, and two Ph.D. students, with an age range of 35 to 65 years.
Data Collection
We distributed the SWOT template on the first day of a weekly winter summit in 2019, with responses collected shortly after (100% response rate). As the survey took approximately 40 minutes to complete, it is assumed that participants gave their answers spontaneously. We repeated the data collection process on the last day; that is, after the program had ended, but before the evaluation session. Before the distribution of the SWOT template, the participants were informed about the study’s aim, the anonymity of their contribution, and their right to withdraw their written consent anytime. Each participant signed an informed consent form before data collection started. No participant withdrew their participation. In this paper, we have ensured their anonymity by using numerical designations when quoting any statements.
Data Analysis
We analyzed and interpreted the responses from participants with reference to Ricœur’s [13] theory of interpretation. This consisted of a three-level process: a naïve reading, followed by a structural analysis, and concluding with a comprehensive discussion. All authors read and reread the data from the SWOT templates [13], thus identifying a naïve understanding. In the structural analysis, we gathered sections of text (consisting of text portions across the SWOT templates) into larger units of meaning [15]. Finally, we comprehensively discussed the meaning of the text in reference to the selected theory and outcome of the initial literature search.
Results
Naïve Reading
The naïve reading indicated that the participants were responsible, cheerful, and helpful. In general, they enjoyed research collaboration. However, some participants found teamwork burdensome when certain members did not fulfill their obligations. They described new technology as exciting, noting that it streamlined their work. At the same time, good leadership was mentioned as inspiring, while the lack of leadership negatively influenced their ability to work effectively with colleagues. Participants also reported that the process of applying for research funding required too much time when compared to the research outcome, and also affected their ability to attend conferences and meet with the research team.
Structural Analysis
Our structural analysis focused on 311 statements taken from the two surveys (161 and 159 from the first and second rounds, respectively). In both surveys, dominant strengths emerged from statements the participants made about their individual characteristics (35 of 58 and 36 of 50 from the first and second rounds, respectively). On the other hand, weaknesses also emerged. For example, some participants emphasized the challenges of navigating additional tasks (19 of 42 statements from the first round), while others mentioned insufficient knowledge about methodology and scientific factors (14 of 34 statements from the second round). In both rounds, participants highlighted a major opportunity derived from the benefits of being part of a research group (15 of 28 and 19 of 32 from the first and second rounds, respectively). They also identified some threats, including those pertaining to the relationship between their aims and obligations (20 of 33 statements from the first round) and challenges between individuals and their participation in the research team (18 of 34 statements from the second round). Ultimately, we summarized the structural analysis into two themes, including 1) strengths, weaknesses, and threats to building research collaboration and 2) collaborative processes across university units. To keep track of individual statements, we assigned a unique number to each participant (i.e., numbers 1 through 8). Thus, all statements and quotations in the two following subsections are connected to the numbers of relevant participants; to further distinguish between the two rounds of SWOT template completion, we also attached the letter “b” in cases where those statements and quotations were from the second round.
Strengths, Weaknesses, and Threats to Building Research Collaboration
Frequently noted strengths included the ability to work effectively under time pressure (3, 5, 8, 4b, 5b, 6b, 8b) and adopt personal responsibility (2, 4, 7, 8, 2b, 3b, 6b, 8b). One participant stated: “Accountability is an integral part of me as a person” (2). Meanwhile, relevant qualities included courage (2), determination (6, 8, 5b, 8b), curiosity (3, 7, 6b), and commitment (2, 4, 7, 8, 2b, 3b, 4b, 6b). Another strength was the ability to both cope with deadlines and respect the deadlines of others (1, 6, 7). In both surveys, half of the participants said that too many tight deadlines could lead to issues such as pressure (1, 2, 7, 8, 8b) and sleep deprivation (1). Consequently, time pressure was considered a threat to their research activities (1, 1b, 2b, 4, 5, 8, 8b). Some participants wanted their work to be more systematic (2, 7b). For example, one said: “I’m not delivering well under strong pressure; then, I’ll be a little paralyzed” (6b). Only one participant said that she had become better at prioritizing over time (5b). Possessing competence in a specific research method was also considered a strength. In this regard, two participants said that they had extensive research competence (3b, 7b). At the beginning of the seminar, only two participants said that they lacked broad research experience (3, 8); however, four participants mentioned relevant personal weaknesses at the end of the seminar, in terms of either general research competence (3b, 6b, 8b) or a specific lack of expertise linked to quantitative methods (8b) or scientific theories (5b, 8b). Of note, half of the participants identified weaknesses in their own contributions to the research team (4, 5, 6, 8). Some also identified insufficient knowledge about methodology and a lack of fluency in speaking (1) or writing academic English (4, 5, 6, 8). One participant said: “I don’t feel that academic writing comes easy for me” (4). Three participants perceived opportunities regarding new technology, explaining that such provisions could facilitate research collaboration (2, 3, 6, 6b). While one participant said that new technology could enable more efficient work (6b), only one said that technology was an integral part of their field (2).
Collaborative Process Across University Units
As a theme, the collaborative process was focused on interpersonal relationships between participants. For example, they said that they enjoyed collaborating with others (3, 4, 2b, 4b, 8b), and had become more open-minded about each other’s perspectives through teamwork (5). They also perceived themselves as honest (7), loyal (2), and good at listening (5). One participant said that her personal weaknesses were speaking more than listening and losing patience with pessimists (7). Moreover, specific collaboration skills (4, 8, 2b, 6b, 7b, 8b) were considered essential for the development of positive collaborative processes and research networks, particularly including openness to ideas presented by other team members (1, 2, 4, 5b). When describing elements they believed were central to team collaboration, the participants used words and phrases such as encouragement (8), support for progress (8), and the ability to motivate others (6b). Openness to ideas proposed by other people was also a factor that contributed to new perspectives (7b). The participants identified multiple benefits of building research collaboration (1b, 3) and sharing experiences (1b). By jointly collecting data and writing articles, team members developed relationships that helped them enhance their research and produce high-quality publications (8, 8b). They considered research collaboration with both internal and external research partners to be desirable (2b, 8, 7b), noting its contribution to professional development (2, 7b). Other participants said that they received more advice from experienced researchers by building relationships in the research group (3b, 4, 5b), which became an arena for inspiration and support (4b).
Through these relationships, the participants gained access to a “room” where they could be open about their shortcomings and needs (7). There were also threats to building effective collaborative processes, including instances in which others dominated the group (2, 7, 1b, 2b, 4b, 6b, 8b), late (or no) responses from research group members (4, 6, 4b, 7b), the lack of ambition among participants (7, 8), and the absence of mutual trust or respect (5, 4b). Six of the eight participants said that the lack of participation from others was a possible weakness in the collaborative group process (1b, 2b, 3, 3b, 6, 6b, 7, 7b, 8b). For example, one said: “The worst thing when working in a team is when someone says they are going to do something, but they do not do it, or does it badly” (3). Five of the eight participants said that effective team management played a major role in building good collaborative relationships (2, 3, 5, 6, 7, 2b, 8b). As a point of emphasis, leadership was said to motivate and inspire relationships between group members (3, 7). By contrast, individual participants felt discounted when they believed that their leaders did not listen to them (2b, 8b). The ability to establish and maintain local, national, and international networks (7) was considered necessary for group collaboration. Other essential aspects were efforts to include (4) and connect people (2), guide and teach students (7), and teach others how to perform. For example, a phenomenological analysis was mentioned (1). One participant said that it was essential to become involved in research conducted by other members (7b).
Discussion
This study critically reviewed the recent literature on building research collaboration, then compared this evidence with the collaborative process experienced by a publicly funded healthcare research team that spanned multiple university units, as collected via a SWOT analysis. The literature review revealed two main themes:
building a research network and 2) networking across university units. The structured SWOT analysis also identified two themes:
strengths and threats in building research collaboration and
collaborative processes across university units (Table 1). In the following subsections, we incorporate a theoretical perspective to provide a comprehensive discussion that is relevant to our study aim.
Table 1: Themes identified from the literature review and local SWOT analysis
Theme
From:Literaturereview
From:LocalSWOT analysis
1
Building research network
Strengths and threats to building research teamwork
2
Networking across university units
Collaborative processes across university units
Building Research Networks
Evidence from the eight reviewed articles indicated that organizational factors could form barriers to research collaboration in the context of publicly funded specialized healthcare research teams. The SWOT participants mentioned similar issues. For example, their faculty leadership did not understand that tight time schedules influenced their ability to conduct research. As a specific hindrance, time pressure threatened their research activities because it reduced opportunities for sleep. In a previous study, Maslach and Leiter [16] found that burnout was more likely to occur when organizational demands exceeded individual capacities. Although work management abilities vary between researchers, they are still affected by relationships between the researcher, group leader, and faculty leadership [17]. Here, leadership styles matter. Autocratic leaders simply dictate group activities and work tasks [18], thus deciding how much group members should contribute without asking for their input [18]. This diminishes agency within the team, which can be solved through a more democratic leadership style that allows collaborative decision-making [18]. Our data analysis also showed that autocratic management styles could threaten research collaboration, especially when leaders demanded rapid solutions, as this further tightened the time schedule. In the literature review, four articles reported that insufficient financial support was a potential barrier [5,7,9,10]. The same problem was mentioned by three of the SWOT participants. Without funding, it can be much more difficult for researchers to test their ideas [19]. This also creates publication hardship. For example, Malhotra [20] found that most academicians in India faced considerable expenses when attempting to gain journal publication, especially in periodicals with high impact factors. However, our SWOT participants did not mention this barrier, perhaps because public universities in Norway offer publication funding.
Networking Across University Units
The SWOT analysis revealed that personal values, transformational leadership, mentorship, and access to financial resources could influence research network collaboration across university units. Interpersonal elements of the research process were also important, including mutual trust, consistent focus, flexibility, and the ability to find time for group collaboration. Previous research has also shown that collaboration groups can more easily work toward common goals when they are situated in excellent research-intensive environments (6). However, the ability to work effectively under time pressure varied considerably among our SWOT participants. Some expressed feelings of stress when navigating multiple tight deadlines, while others reported improved prioritization ability with increased experience. Mentorship can prevent burnout by helping inexperienced researchers learn how to balance different work tasks [6] and develop new skills [21]. This makes provision of mentorship especially important for young academicians. For nursing scholars, mentorship can encourage positive relational, attitudinal, behavioral, career, and motivational changes [22]. Our SWOT participants mentioned some additional barriers to research collaboration, including limited research experience and difficulties with academic English.
Of note was that four participants emphasized that their weaknesses in both research experience and academic English skills hampered their contributions to the research group, neither of which factors clearly emerged through our literature review. Nevertheless, Dorsey et al. [9] and Cohen et al. [6] said that collaborative group leaders and experienced researchers should jointly serve as role models. Functioning in such a capacity entails facilitating interactions with junior researchers, who can therefore benefit from better training and mentorship for life in academia. Differences in computer skills and internet access can affect availability, thus impacting the degree to which team members can collaborate [7]. As such, researchers should develop and employ technology to improve communication between team members who are geographically distant (Dorsey et al.[9]; Cohen et al. [6]. Moreover, collaborative groups can contact their university’s information/computer technology departments to ensure that necessary computer and web technologies are available [9]. Finally, Steinke et al. [7] recommended a backup plan if videoconferencing fails, including email correspondence or other free internet software applications. In the modern technological environment, numerous tools support collaboration and the development of professional skills in the university setting [23]. In fact, none of our SWOT participants mentioned computer technology as a barrier to the research process or group collaboration, suggesting that they worked in a technology- rich environment. At the same time, personal computers have become increasingly common in research environments.
Motivation is also essential for international collaboration [24]. In this regard, Bass et al. [25] argued that inspirational leadership with a motivational focus on personal behavior could provide meaning while challenging team members to efficiently achieve future goals. According to Anselmann and Mulder [26], transformational leadership can further help leaders identify potential areas of change and encourage necessary adjustments. However, an open-minded view of other perspectives can be interpreted as a wish to view collaborating partners as equals, which may be challenging when team members possess different skills and experiences [27]. According to our findings, mentorship can reduce problems related to time pressure and the lack of academic skills. This is greatly beneficial for inexperienced researchers, who can realize personal development, increased research productivity, and better career opportunities [28]. As a practical example, our SWOT participants expressed the desire to develop skills in writing applications under the guidance of senior members. Based on our experiences in this study, we envision opportunities for research group leaders to employ SWOT templates. Such an approach will clarify team strengths and weaknesses, which can help them customize their mentorship accordingly.
Study Strengths and Limitations
As regards strengths, this study conducted a preliminary comprehensive literature review, which became a benchmark when discussing our analysis and findings. However, there were also some limitations. First, the participants were exclusively invited to participate in the research seminar, and may have therefore been more positive and open toward both their own development and SWOT factors in general. However, the group was also comprised of novice and expert researchers, who addressed a situation that similar research teams may experience, which constitutes a strength. Second, the participants were required to complete the SWOT template within a limited time, which may have elicited superficial answers to the four explored areas. However, they were also able to build on their initial answers during the second survey round, which thus constitutes a strength in data collection, as evident in the enhanced development of their responses.
Conclusion
This study found that supportive leadership and active mentorship between experienced and inexperienced team members could facilitate the research process and increase collaboration in the context of a publicly funded specialized healthcare research team. Of note, supportive leadership is highly essential. Our SWOT participants said that their ability to motivate and support other team members depended on whether the team leader offered the same provisions. Our perception is that supportive and motivated team leaders can serve as positive role models for the entire team, thus creating a group culture that prevents non-participation or late responses from members. In most scientific endeavors, the establishment and maintenance of a collaborative research team are fundamental to success.
Implications for Nursing
Supportive leadership is highly essential for nurse researchers to flourish.
Nurse managers may not have research experience or necessary insight into the working conditions that support research collaboration
It is important to adopt a transformational leadership style in which a dialogical practice can support specialized healthcare research teams in their positions.
Conflicting Interest
None.
Disclosure
The authors report no conflicts of interest in this work.
Funding
We thank Nord University for funding a Winter Summit in 2019 and providing a grant for Mrs. Hansen to act as a research assistant for professor Uhrenfeldt in 2019-20.
Ethical Approval
No ethical approval was required for this research. The study is registered at Nord University (FSH by j.no 24.04.20).
Author Contributions
Study design: LU. Data collection (Literature search); quality appraisal and data analysis: MCH, LU, KI. Data collection (SWOT analysis): MCH supervised by LU. Manuscript preparation MCH, supervision, and critical review by LU, KI. All authors critically reviewed and approved the final manuscript.
Language Editing
This paper is edited by The Golden Pen.
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Modern sows are characterized by a high prolificacy as indicated by the increased number of total born piglets, which results in a higher pre-weaning mortality. Tonisity Px (TPx) is an isotonic protein drink administered to piglets from d2 to d8 of life during the suckling period to support intestinal health and development. The aim of the present study was to analyze the effects of TPx administration on the pre-weaning mortality under field conditions in 10 sow farms in Belgium and the Netherlands. Therefore, 10 sow farms with a pre-weaning mortality between 3.3 and 13.8% were enrolled in the study. Supplementation of Tonisity Px was compared with standard Control treatment in the same batch. Number of piglets on d2 and the day before weaning was counted and pre-weaning mortality was calculated. Subsequently, reduction in pre-weaning mortality between Control and Tonisity Px group was calculated at farm level. Based on these results, a scatterplot was designed and a trendline formula for the effect of Tonisity Px was calculated. Applying the trendline formula, an economic calculation was run to find the weaned piglets and end-nursery piglet market price for a positive return-on-investment (ROI = or > 1). Supplementation of Tonisity Px resulted in a significant reduction (P = 0.003) of pre-weaning mortality from 7.38 to 5.41%, which is a 23.40% reduction in pre-weaning mortality. Economic analysis revealed that Tonisity Px supplementation has a positive economic return-on-investment from 6.0% pre-weaning mortality onwards under the current end-nursery 25 kg piglet market prices. In conclusion, supplementation of Tonisity Px from d2-8 in the suckling period results in a 23.4% reduction in pre-weaning mortality with a positive return-on-investment from 6.0% pre-weaning mortality onwards.
Keywords
Tonisity Px, Pre-weaning mortality, Field results, Meta-analysis
Introduction
Modern sows are characterized by a high prolificacy as indicated by the increased number of total born piglets (TBP). Under Danish conditions, the number of TBP has increased from 12.9 in 2000 to 19.6 piglets per litter in 2020 [1-4]. Modern sows may commonly wean 33-35 piglets per sow per year, but herds with the highest productivity now wean more than 40 piglets per sow per year. The increased litter size is, however, accompanied by a clear decrease in the average piglet birth weight [5-9]. Moreover, due to the limited amount of available colostrum, a decrease in colostrum volume consumed per piglets could be observed [5,12]. This may increase the vulnerability of piglets born from modern high prolific sows [8], which in turn decreases livability from farrowing to weaning. In addition, the small intestine of newborn piglets undergoes major developmental changes during the first 10 days of life. Therefore, this critical period has been identified as a ‘window of opportunity’ for potential nutritional interventions to support the development of intestinal structure, including digestion, absorption and growth, and the maturation of the immune system resulting in potential lifelong effects [3,6,10,11]. These factors create an opportunity to provide supplemental nutrition in the first days of the piglets’ lives to increase livability. Tonisity Px™ (TPx) is a highly palatable isotonic protein solution that provides microenteral nutrition to the intestinal cells. Tonisity Px™ is administered as a 3% solution to neonatal piglets for a 7-day period from day 2 after birth (d2) until day 8 after birth (d8). Tonisity Px™ has been demonstrated to improve intestinal morphology with taller villi (+ 8.3%) and a thicker mucosal layer (+ 9.0%) by d9 in suckling piglets [7]. Furthermore, administration of TPx increased the abundance of beneficial bacterial populations, such as Lactobacillus and Bacteriodes species, and reduced potentially pathogenic bacterial populations, such as Escherichia coli and Prevotellaceae, in the pre-weaning period [1,2]. The aim of the present study was to analyze the effects of TPx administration during the suckling period from d2 to d8 on the pre-weaning mortality under field conditions in 10 sow farms in Belgium and the Netherlands.
Materials and Methods
Test Ingredient
The test ingredient consisted of an isotonic protein solution (Tonisity Px™; Tonisity Ltd, Dublin, Ireland), which provides easily-absorbable nutrients (glucose, amino acids, and peptides) and electrolytes that can be used directly by the enterocytes.
Study Population
Ten farrow-to-wean sow farms in Belgium and the Netherlands with an average number of 733 ± 182 productive sows (min. 200, max. 2000) were enrolled in the field study (Table 1).
The sow herds were run according to different batch-management systems (BMS), such as 1-week BMS (n = 2), 3-week BMS (n = 1), 4-week BMS (n = 6), and 5-week BMS (n = 1). The piglets were weaned at an average age of 23 ± 0.82 days of age (min. 21, max. 26). The average pre-weaning mortality was 7.4 ± 1.1% (min. 3.3, max. 13.8). All sow herds were high prolific with 15.5 live born piglets (LBP) and 32.1 piglets weaned per sow per year.
Table 1: Description of the relevant farm characteristics (obtained prior to the study enrollment) of all 10 sow farms included in the trial comparing standard piglet treatment to supplementation of Tonisity Px (Tonisity Ltd, Dublin, Ireland).
Farm ID
# Sows
Breed
BMS1
Weaning age
% PWM2
LBP3
PSY4
Standard program
A
800
DanBred
4
21
7.4
15.67
32.60
Electrolyte solution
B
250
Topigs-Norsvin
1
26
3.9
14.83
31.23
No supplementation
C
400
Topigs-Norsvin
4
21
10.0
14.53
29.22
No supplementation
D
200
DanBred
4
21
4.9
16.41
35.17
No supplementation
E
1,150
DanBred
1
26
13.8
16.00
30.38
Water
F
2,000
Topigs-Norsvin
4
21
3.3
15.52
33.72
No supplementation
G
1,000
DanBred
4
21
5.3
16.61
35.47
No supplementation
H
1,000
DanBred
4
21
5.3
14.82
33.17
No supplementation
I
280
Hypor
3
26
8.6
15.01
29.45
No supplementation
J
250
Topigs-Norsvin
5
28
11.3
15.45
30.12
No supplementation
1BMS: Batch Management System 2PWM: Pre-Weaning Mortality 3LBP: Live Born Piglets 4PSY: Piglets Weaned per Sow per Year
Experimental Design
Litters within the same farrowing batch were allocated to one of 2 groups, Control or supplementation with TPx. The allocation was balanced according to sow parity (gilts vs. older sows) and number of LBP. In 8 out of 10 sow herds, the piglets in the Control group did not receive any supplementation. However, in farm A, the Control group received a supplementation with a standard electrolyte solution, and in farm E, the Control group was supplemented with water during the study period from d2 to d8. Litters in the TPx group were given 250 mL of 3% TPx solution on d2 of age, and from d3-8 of age TPx litters received 500-600 mL of 3% TPx once daily in a clean waterbowl.
Measurements
The number of piglets per litter was counted at d2, the start of TPx administration, and at the day prior to weaning. Pre-weaning mortality was calculated per litter and per batch as the number of dead pigs pre-weaning divided by the number of piglets at d2.
Meta-Analysis
The PWM results obtained in the Control and TPx group were plotted and a trendline was calculated for both the Control and TPx group. Based on the trendline formula of the TPx group, a simulation was performed on potential PWM reduction within the range of 4 to 15% PWM under field conditions. These data were subsequently applied to run an economic calculation for return-on-investment (ROI) of the test product.
Economic Calculations
Return-on-investment calculations were performed based on the number of extra piglets per 1000 piglets enrolled by TPx administration. The cut-off value of weaned and end-nursery piglet market price was calculated for a ROI value of 1.
Therefore, the following formula was used based on the cost of treatment for 1000 piglets enrolled: y = 390 / x, with x = number of extra piglets per 1000 piglets enrolled and y = cut-off value of weaned piglet market price. The cut-off value of end-nursery piglet market price was calculated by adding € 25.00 to the cut-off value of weaned piglet market price.
Statistical Analysis
Data were analyzed using JMP 17.0 and results were significant at P < 0.05.
Results
Pre-weaning Mortality
The results on pre-weaning mortality in both Control and TPx groups, including the overall percentage of reduction in pre-weaning mortality in the 10 farms enrolled in the study are given in Table 2.
Supplementation of TPx resulted in a significant (P = 0.003) reduction in PWM as compared to the Control group. In the Control group, PWM was between 3.3 and 13.8%, whereas in the TPx group PWM was between 2.7 and 8.6%. The overall percentage of reduction varied between 5.1% at minimum and 37.7% at maximum.
Table 2: Pre-weaning mortality and overall reduction in pre-weaning mortality in the farms enrolled in the study evaluating Tonisity Px supplementation from day 2 to 8 versus a standard on-farm program for the neonatal piglets.
Farm ID
PWM control (%)
PWM Tonisity Px (%)
% PWM reduction
A
7.4
5.7
23.0
B
3.9
3.7
5.1
C
10.0
5.6
44.5
D
4.9
3.3
32.7
E
13.8
8.6
37.7
F
3.3
2.7
17.2
G
5.3
4.7
10.8
H
5.3
4.0
23,8
I
8.6
8.6
10.9
J
11.3
8.1
28.3
Pre-weaning Mortality According to Breed, Weaning Age and Number of Live Born Piglets
Further detailed analysis of PWM according to breed, weaning age and number of live born piglets in both Control and TPx group are given in Table 3.
For sow breeds, DanBred sows in the Control group had an average PWM of 7.99% in contrast to other breeds (Topigs-Norsvin, Hypor) had a PWM of 6.47%. In the TPx group, both sow breeds had a lower PWM of 5.73% and 4.92% for DanBred and other breeds, respectively.
For weaning age, litters weaned at 21 days of age had a lower PWM (6.03%) as compared to litters weaned at a later age of 26-28 days (9.41%) in the Control group. In the TPx group, PWN decreased to 4.33% and 7.02% for litters weaned at 21 and 26-28 days of age, respectively.
For number of live born piglets, litters with an LBP < 16 piglets had a lower PWM (6.92%) as compared to litters with an LBP ≥ 16 (7.84%) in the Control group. In the TPx group, PWM decreased to 5.00% and 5.82% for litters with an LBP < 16 and an LBP ≥ 16, respectively.
Table 3: Detailed analysis of overall percentage of PWM in control and Tonisity Px supplemented group, considering sow breed (DanBred vs. other breeds), weaning age (early 21 d vs. late 26-28 d), and number of live born piglets (low LBP < 15.5 vs. high LBP ≥ 15.5). Significant differences at P < 0.05 are indicated with a different letter in supercript.
Parameter
PWM control (%)
PWM Tonisity Px (%)
% PWM reduction
All farms
7.38 ± 1.11
5.41 ± 0.67
23.40 ± 4.01
Sow breed
DanBred
7.99 ± 1.52
5.73 ± 0.89
26.04 ± 3.79
Other breeds
6.47 ± 1.71
4.92 ± 1.10
19.44 ± 8.71
Weaning age
21 d
6.03 ± 1.98
4.33 ± 0.50a
25.33 ± 4.85
26-28 d
9.41 ± 2.12
7.02 ± 1.12b
20.51 ± 7.55
Live born piglets
LBP < 16
6.92 ± 1.84
5.00 ± 1.04
24.27 ± 4.91
LBP ≥ 16
7.84 ± 1.41
5.82 ± 0.91
22.52 ± 6.92
Meta-analysis of Pre-weaning Mortality Data
The scatterplot of percentage PWM in Control and TPx group of the 10 farms enrolled in the study demonstrates the reduction in PWM percentage following supplementation of TPx from d2 to d8 (blue trendline) as compared to the PWM in the Control group (orange line) (Figure 1). The trendline formula obtained based on the results was: y = 0.5581 x + 0.0129 with an R² of 0.8561. This trendline formula will be used in the simulations for the economic calculation of ROI following supplementation of TPx in different scenarios of PWM percentages.
Figure 1: Scatterplot of percentage PWM in Control and Tonisity group of the10 farms enrolled in the study. Orange squares, datapoints of the Control group; blue squares, datapoints of the Tonisity Px supplemented group in relation to their initial percentage of PWM. Trendline shows the correlation between initial percentage of PWM (Control) and the percentage of PWM obtained following supplementation with Tonisity Px.
Economic Calculations
The simulation of PWM reduction following supplementation of TPx based on the trendline formula obtained in relation to the initial on-farm PWM with calculation of the number of extra pigs per litter and per 1000 born piglets based on the PWM reduction is given in Table 4. Simulation with the obtained trendline formula using an initial PWM ranging from 4.0% to 15% resulted in calculated PWM with TPx application from 3.52% to 9.66%, which was equal to a PWM reduction percentage of 11.9 to 35.6%. Based on these numbers, the number of extra piglets per litter and per 1000 piglets born were calculated. At 4.0% initial PWM, a reduction of 11.9% resulted in 0.08 extra piglets per litter and 5 extra piglets per 1000 piglets born.
Applying the formula y = 390/x, we obtained a weaned piglet price at ROI = 1 breakpoint of € 81.66 and of € 106.66 for end-nursery piglet price. Based on current market prices for weaned piglets and end-nursery piglets (20 September 2024; Flemish piglet price), TPx supplementation has an ROI of 1 or more starting from a PWM percentage of at least 6.0% (indicated by the dotted red line on the figure) (Figure 2).
Table 4: Simulation of PWM reduction following supplementation of Tonisity Px based on the trendline in relation to the initial on-farm PWM with calculation of the number of extra pigs per litter and per 1000 born piglets based on the PWM reduction. Calculation of weaned piglet price and piglet price end-nursery (25 kg) in relation to the return-on-investment breakpoint (ROI = 1.0) based on the average cost of Tonisity Px for 1000 supplemented piglets.
Initial PWM
PWM Tonisity Px
Simulated PWM reduction
Extra piglets/litter
Extra piglets per 1000 piglets born
Weaned piglet price at ROI = 1 breakpoint (€)
Piglet price end-nursery at ROI = 1 breakpoint (€)
4.0%
3.52%
-11.9%
0.08
5
€ 81.66
€ 106.66
5.0%
4.08%
-18.4%
0.15
9
€ 42.41
€ 67.41
6.0%
4.64%
-22.7%
0.22
14
€ 28.65
€ 53.65
7.0%
5.20%
-25.8%
0.29
18
€ 21.63
€ 46.63
8.0%
5.75%
-28.1%
0.36
22
€ 17.37
€ 42.37
9.0%
6.31%
-29.9%
0.43
27
€ 14.51
€ 39.51
10.0%
6.87%
-31.3%
0.50
31
€ 12.46
€ 37.46
11.0%
7.43%
-32.5%
0.57
36
€ 10.92
€ 35.92
12.0%
7.99%
-33.4%
0.64
40
€ 9.72
€ 34.72
13.0%
8.55%
-34.3%
0.71
45
€ 8.75
€ 33.75
14.0%
9.10%
-35.0%
0.78
49
€ 7.96
€ 32.96
15.0%
9.66%
-35.6%
0.85
53
€ 7.31
€ 32.31
Figure 2: Analysis of return-on-investment breakpoint (ROI = 1) related to market price of weaned piglets (6 kg; orange bars) and piglets at end of nursery (25 kg; green bars). The dashed red line is set at the piglet price (25 kg, end of nursery) of € 56.50 which is the current market price for end-nursery 25 kg piglets (20.09.2024; Flemish piglet price).
Discussion
Supplementation of Tonisity Px from d2 to d8 of life resulted in a significant (P = 0.003) reduction of PWM as compared to a simultaneous Control group in 10 farms with difference in management approach under field conditions. This observation is in line with previous studies on the effect of TPx (Carlson et al., 2019). In 8 out of 10 farms, PWM reduction due to TPx was compared to a non-supplemented Control group, whereas in 2 farms a standard supplementation of plain water or electrolyte solution was applied in the Control group. Moreover, the effect of TPx supplementation was evaluated in different sow breeds, such as DanBred (n = 5), Topigs-Norsvin (n =4) and Hypor (n = 1). These breeds are known to be highly prolific which can be confirmed by the high number of LBP (15.01 to 16.41 LBP per litter) and the number of piglets weaned per sow per year (29.22 to 35.47 PSY). As expected, farms with an already low PWM could only observe a mild to moderate further reduction in PWM (5-10%), whereas farms with a rather high PWM had a major reduction in PWM (37-44%).
Detailed analysis on TPx effect related to sow breed, weaning age and number of LBP revealed that in all scenarios, TPx supplementation resulted in a decrease of PWM percentage as compared to the Control. As observed in practice, DanBred sows have a higher PWM as compared to other breeds such as Topigs-Norsvin and Hypor. As expected, TPx supplementation resulted in a higher PWM reduction (26.04%) in DanBred sows as compared to other sow breeds (19.44%). Litters weaned at 26/28 days of age had a more than 50% higher PWM both in the Control and TPx group as compared to litters already weaned at 21 days of age. There is no clear explanation for this observation. Since most of the PWM occurs in the first 3-5 days of life, length of the lactation period should not further impact PWM. The difference in PWM for litters with more or less than 16 LBP was very limited, as was the reduction in PWM following TPx supplementation. Indeed, all 10 selected farms were highly prolific and therefore the range of LBP was quite limited (15.01 to 16.41 LBP per litter).
Analysis of the scatterplot of PWM percentage in the Control and TPx group resulted in a trendline formule of y = 0.5581 x + 0.0129 with 85.61% of the changes in y (PWM supplementing TPx) that could be explained by changes in x (PWM under standard control situation). Application of this trendline formula in a simulation with control PWM ranging from 4.0 to 15.0% resulted in a presumed PWM supplementing TPx ranging from 3.52 to 9.66%. It could be observed that a gradual increase in PWM reduction was present with higher initial PWM. Based on these data both the number of extra piglets per litter and per 1000 piglets born could be calculated (Table 4). These data were used to calculate the minimal piglet market value for a ROI of 1, both in weaned piglets, which are not regularly sold onto the market under our local Belgian and Dutch conditions, and end-nursery piglets sold at 25 kg standard weight. Further comparison of these end-nursery piglet prices to the current piglet market prices (Flemish pig price, 20.09.2024) demonstrated that TPx supplementation can result in a positive ROI (ROI equal to or higher than 1) from 6.0% PWM onwards.
Conclusions
The administration of TPx from d2 to d8 during lactation resulted in a significant reduction of PWM in 10 farms under field conditions in Belgium and the Netherlands. Supplementation of TPx resulted in a positive ROI (= or > 1) when PWM at farm level was equal to or higher than 6.0% under current end-nursery piglet market prices.
Abbreviations
PWM: Pre-Weaning Mortality; TPx: Tonisity Px™; d2: Day 2 After Birth; d8: Day 8 After Birth; TBP: Total Born Piglets; LBP: Live Born Piglets; ROI: Return-On-Investment
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Introduction: Cleft palate is a common congenital defect with several described surgical repairs. It is generally an isolated congenital abnormality but can be associated with multiple syndromes. Although there are a multitude of surgical options, many are variations of a previously described repair, and the most successful treatment modality remains a controversy.
Case Summary: The patient, a man, age 25 years old, had a Class III cleft lip and palate Veau classification, underwent a modified palatoplasty and acquired a favourable palatopharyngeal closure function, decreased hemorrhage and swelling.
Conclusion: In this study, we provide a modified palatoplasty for all palate cleft variations, it may benefit for uvula intact, reduce bleeding and swelling.
Cleft of the palate, CP, is one of the most prevalent orofacial birth defects around the world occurring in about 0.33 in every 1000 live births regardless of race, and there was no significant difference between men and women [1,2]. The cleft palate is generally an isolated congenital abnormality but can be associated with other anomalies or multiple syndromes, with or without the presence of lip or alveolar clefting [3]. According to the Veau classification, the cleft palate is divided into four groups depending on the extent of involvement: Group I is limited to the soft palate only; Group II involves the soft and hard palates; Group III includes the soft and hard palate as well as the lip; and Group IV is bilateral complete clefts Figure 1 [4].
Figure 1: Veau classification. A class I. defects of the soft palate only; B class II. Defects involving the hard palate and soft palate; C class III. Defects involving the soft palate to the alveolus, usually involving the lip; D class IV. Complete bilateral clefts.
Congenital palate defect is caused by disturbed embryonic development when the palatal shelves fail to fuse during the 6th~12th week of pregnancy [5]. It is multifactorial, influenced by genetic factors recessive or incompletely dominant polygenic inheritance and exogenous factors drugs, folic acid deficiency, viral infections, etc [6]. It has been difficult to point to a single etiologic mechanism responsible for this complex trait, resulting in severe speech, nutrition, and mental and social developmental disorders that significantly reduce patients’ quality of life [7].The diagnosis of cleft palate is not difficult because of its obvious features. Treatment of cleft palate ordinarily requires multiple interventions spanning time from birth to adulthood [8]. However, current treatment for this disease generally demands early surgery and face reconstruction procedures that may be revised during childhood and infancy, causing a great number of patient complaints and economic burdens on health systems that need to be minimized [9]. In this study, we report a modified operation of palatoplasty that provides a choice for these patients to shorten operation time, and reduce intraoperative bleeding, trauma, and postoperative swelling.
Case Report
A 25-year-old Chinese man came with a congenital cleft of lip and palate, he received lip repair in the local hospital when he was 4 years old. However, palatal repair was suspended because of a lack of money. Nowadays, the patient was referred to our hospital for palatal cleft repair which significantly affects pronunciation. The patient denied other abnormal parts of the body and his parents are both normal. After comprehensive examination and imaging evaluation by a professional maxillofacial surgeon, he was diagnosed with CP group III, Class III skeletal pattern malocclusion, microdontia, defect of dentition, and dental cavity Figure 2. At this time, the patient only wanted to receive palatal cleft repair.
Figure 2: Clinical information of the patient. A-C the profile photo of the patient; D-F the itro-oral film of the patient; G the computerized tomography imaging of the patient.
A cleft of the soft and hard palate with cleft lip postoperative was seen in our patient. Our modified palatoplasty involves: 1. relaxing incisions along the lateral edge of the hard palate, starting anteriorly near the palatomaxillary suture line, going posteriorly just medial to the alveolar ridge, and ending lateral to the hamulus, approximately to the tuberosity of the alveoli. 2. The incision posterior to the maxillary tuberosities was widened by blunt dissection, the hamulus was identified and the hamulus pterygoideus was broken. 3. The mucosa along the edges of the cleft starting at the palatal alveolar to anteriorly 5 mm of uvula was also incised Figure 3A and 3B. 4. The entire mucoperiosteum was then raised from the oral surface of the hard palate; care was taken to preserve the two neurovascular pedicles, the greater palatine pedicle posteriorly and the incisive pedicle anteriorly. Thus, bi-pedicled mucoperiosteal flaps were created on both sides of the cleft Figure 3C. 5. Three layers, including an oral mucosal layer, muscle layer, and nasal layer were dissected which tends to relieve tension on the repair and reduce the postoperative fistula rate. 6. Firstly, the nasal side of the cleft was closed, using redundant mucoperiosteum from the incision along the cleft edge Figure 3D. 7. Secondly, residual mucosa along the edges of the cleft uvula fissa was incised, and seamed the nasal layer. 8. Next, the muscle layer was closed approximately using an intravelar veloplasty. 9. Lastly, the bi-pedicled oral mucosal flaps were approximated to cover the oral surface of the cleft Figure 3E. A month later, the patient returned to our clinic, the palatine mucosa was integrity and the uvula recovered Figure 3F. The speech quality of this man was also improved and had a good velopharyngeal function (Figure 4).
Figure 3: Surgical procedures of the patient and postoperative manifestation. A-E the operative procedues of the patient; F one month postoperative follow-up of the patient.
Figure 4: Traditional surgery and modified operation. (A) a-c the traditional surgery of the palatoplasty; (B) d-g the modified surgery of the palatoplasty.
Discussion
The goals of palatoplasty are to acquire complete and intact closure of the palate and restoration of the velopharyngeal sphincter. Besides, reducing hemorrhage, avoiding palatal fistula, and decreasing postoperative swelling also should include care. After decades, there are many techniques for cleft palate repair and each has its advantages. To repair the soft palate, Intravelar Veloplasty, and Furlow Double-Opposing Z-Plasty are widely applied [10,11]. To repair the hard palate, the Von Langenbeck Palate palatoplasty, Veau-Wardill-Kilner palatoplasty, Two-Flap palatoplasty, and Vomer Flap techniques are employed around the world [11-14]. Nonetheless, the most successful treatment modality remains controversial. According to Veau classifications, surgeons are recommended to choose appropriate surgical techniques for the patients after evaluating the results as they see fit to provide the best functional outcomes for their patients [15]. However, all the techniques above mentioned may cause uvula injury due to incision without suture immediately and improve the occurrence of velopharyngeal incompetence. The rate of oronasal fistula following primary cleft palate surgery was about 3.8~6.1% [16]. In this study, we raise a modified palatoplasty: delayed incision of the uvula and earlier suture of the nasal layer. It is beneficial for uvula integrity, reducing uvula tears, and decreasing hemorrhage and swelling.
Fusion of particular orofacial structures during early gestation is required for proper development of the upper lip and jaw. Failure of this process leads to an orofacial cleft, which manifests as a gap in the tissue of the upper lip, the palate, or both [17]. Treatment of cleft lip and palate ordinarily requires multiple interventions spanning the time of birth to adulthood [18]. This process includes a multidisciplinary evaluation, involving pediatric dentists, oral and maxillofacial surgeons, orthodontists, prosthodontists, speech therapists, and psychological consultation teachers. In this study, our patient only underwent the necessary surgery because of financial difficulty, we sincerely advise he achieve serial therapy shortly.
Conclusion
We preferred the modified palatoplasty for all cleft variations. The use of modified palatoplasty in the cleft palate seems to contribute to a reduction of hemorrhage, uvula varies, and postoperative swelling.
Acknowledgement
Cailing Jiang contributed to the conception, design, analysis and interpretation of data, and drafting of this article. Chong Jiang, Zijun Guo and Haiyou Wang contributed to data collection and analysis. Sui Jiang contributed to the conception and design, critical review of the article, and final approval.
Declaration
The authors declare no conflict of interest.
Funding
The preparation of this manuscript was not supported by any funding or grants.
Ethics Approval
Ethics approval was received from the ethics committee of Guangdong Provincial People’s Hospital (KY2023-827-03).
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