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New Thinking on Psychological Health: Finding Purpose and Meaning in Life

DOI: 10.31038/PSYJ.2023583

 

Purpose and meaning in life are now vibrant topics in multiple domains of science and practice. We recently published a collection of articles on purpose and meaning in life to showcase the rich content of this emergent work [1]. Contributors brought differing samples, measures, and contexts to the collective inquiry. Most visible in prior studies have been those linking purpose in life to specific aspects of health, such as risk for various disease outcomes and length of life (mortality). Here the special issue carved new territory. Links between purpose in life and waist circumference, an indicator of abdominal obesity, was shown to be mediated by healthy eating [2]. Activist purpose, defined as commitment to engage in social activism, was associated with good health behaviors [3]. Links between religiousness/spirituality and mortality were mediated by purpose in life and social support [4]. Purpose in life moderated relationships between self-rated health and mortality [5]. And low levels of purpose, personal growth, and social connection were linked with increased risk for deaths of despair (due to suicide, addiction, and alcoholism) compared to risk of death due to heart disease [6].

Importantly, other contributors to the special issue probed what precedes these later life outcomes-that is, what early life influences contribute to the emergence of meaning and purpose. Childhood relationships with significant others were examined. Supportive and loving parents in early childhood development had strong impact on the sense of meaning for those in university, with effects mediated by experiences of loneliness [7]. Early influences that shape beliefs such as rigid interpersonal schemas were seen to compromise adult meaning and purpose, however clinical intervention can help with restructuring such schemas to foster improved mental health in adults [8]. Other early-life inquiries focused on educational interventions to promote multiple aspects of eudaimonic well-being in elementary and high school students. Findings showed that preexisting levels of depressive symptoms and anxiety can be obstacles to the development of well-being [9]. Another study examined at-risk adolescents who participated in a sailing experience designed to nurture meaning, identity, commitment, social well-being, and self-acceptance [10]. Together, these contributions offered new insights about child and adolescent experiences that nurture or undermine meaning and purpose.

Another section examined the psychosocial correlates of meaning and purpose. Longitudinal analyses showed how aspects of hedonic and eudaimonic well-being were linked with depressive symptoms, with findings showing reciprocal relationships across time [11]. The connections between meaning in life and character strengths were examined showing that hope, spirituality, zest, curiosity, and gratitude were the strongest predictors [12]. A separate study linked meaning and purpose with sociodemographic factors (age, educational status, work status) as well as with stress, spirituality, optimism, depressive symptoms, social support, and quality of life [13]. Together, these new findings continue mapping of the nomological network of meaning and purpose.

A section on distinct contexts such as work, major public stressors, and the natural environment were considered for understanding what nurtures or undermines meaning in life. The double edge of meaningful work was examined-which can both enhance motivation and performance in organizations, while eroding well-being and increasing the chances of burnout for individuals-with calls to elevate decency as a critical antecedent of meaningful work [14]. A further context pertained to how meaning in life mediates or moderates negative emotion in the face of social unrest and the pandemic [15]. The natural environment was examined as another context wherein nature may play important roles in helping humans find coherence, significance, and purpose [16].

Two final contributions focused on translational science and community action. One called for a stronger reciprocal relationship between research and application with primary emphasis on justice, equity and a commitment to influence public policy [17]. A final article laid out a transdisciplinary approach to meaning-making by describing a set of community-based, context-sensitive and socially responsible interventions designed to be applicable to everyday life including discourse in the public square, intergenerational life stories, and the use of literature, art, and museums to educate for meaning [18].

The special issue concluded with advocacy on two fronts. The first called for new thinking that weaves topics of purpose and meaning together with concern about human virtues and ethics. So doing is necessary to address some of the world’s gravest woes, such as widening inequality, systemic racism, and climate change. A second call is for greater collaboration between researchers and practitioners, so that scientific advances are not sequestered in scholarly journals but are rapidly applied and refined in the real world. Together, we believe that a moral foundation to meaning and purpose research combined with greater collaboration with practitioners will set the stage for the pursuit of meaning and purpose in directions that strive to create a more just, fair, and sustainable world.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ryff CD, Soren A (2023) Introduction to this Special Issue on New Thinking on Psychological Health: Find Purpose and Meaning in Life Int J Environ Res Public Health 20:7168
  2. Berkowitz L, Mateo C, Salazar C, Samith B, Sara D, et al. (2023) Healthy Eating as Potential Mediator of Inverse Association Between Purpose in Life and Waist Circumference: Emerging Evidence from US and Chilean cohorts. Int J Environ Res Public Health 20: 7099.
  3. Hill PL, Rule PD, Wilson ME (2023) Does Activism Mean Being Active? Considering the Health Correlates of Activist Purpose. Soc Sci 12: 425.
  4. Boylan J, Biggane C, Shaffer J, Wilson C, Vagnini K, et al. (2023) Do Purpose in Life and Social Support Mediate the Association between Religiousness/Spirituality and Mortality? Evidence from the MIDUS National Sample. Int J Environ Res Public Health 20: 6112.
  5. Friedman E, Teas E (2023) Self-Rated Health and Mortality: Moderation by Purpose in Life. Int J Environ Res Public Health 20: 6171.
  6. Song J, Kang S, Ryff C (2023) Unpacking Psychological Vulnerabilities in Deaths of Despair. Int J Environ Res Public Health 20: 6480.
  7. Dameron E, Goeke-Morey M (2023) The Relationship between Meaning in Life and the Childhood Family Environment among Emerging Adults. Int J Environ Res Public Health 20: 5945.
  8. André N, Baumeister R (2023) Dysfunctional Schemas from Preadolescence as One Major Avenue by Which Meaning Has Impact on Mental Health. Int J Environ Res Public Health 20: 6225.
  9. Ruini C, Albieri E, Ottolini F, Vescovelli F (2023) Improving Purpose in Life in School Settings. Int J Environ Res Public Health 20: 6772.
  10. Dyrdal G, Løvoll H (2023) Windjammer: Finding Purpose and Meaning on a Tall Ship Adventure. Soc Sci 12: 459.
  11. Joshanloo M, Blasco-Belled A (2023) Reciprocal Associations between Depressive Symptoms, Life Satisfaction, and Eudaimonic Well-Being in Older Adults over a 16-Year. Int J Environ Res Public Health 20: 2374.
  12. Russo-Netzer P, Tarrasch R, Niemiec RA (2023) Meaningful Synergy: The Integration of Character Strengths and the Three Types of Meaning in Life. Soc Sci 12: 494.
  13. Coelho A, Lopes M, Barata M, Sousa S, Goes M et al. (2023) Biopsychosocial Factors That Influence the Purpose in Life among Working Adults and Retirees. Int J Environ Res Public Health 20: 5456.
  14. Soren A, Ryff C (2023) Meaningful Work, Well-Being, and Health: Enacting a Eudaimonic Vision. Int J Environ Res Public Health 20: 6570.
  15. Sun R, Lau E, Cheung S, Chan C (2023) Meaning in Life, Social Axioms, and Emotional Outcomes during the First Outbreak of COVID-19 in Hong Kong. Int J Environ Res Public Health 20: 6224.
  16. Passmore H, Krause A (2023) The Beyond-Human Natural World: Providing Meaning and Making Meaning. Int J Environ Res Public Health 20: 6170.
  17. Burrow A (2023) Beyond Finding Purpose: Motivating a Translational Science of Purpose Acquisition. Int J Environ Res Public Health 20, 6091.
  18. Russo-Netzer P (2023) Building Bridges, Forging New Frontiers: Meaning-Making in Action. Soc Sci 12: 574.

Success in Solving Riddles and Psychometric Intelligence of Students

DOI: 10.31038/PSYJ.2023582

Abstract

The results of performing the intelligence test were compared with the successfulness of solving Russian folk riddles. Significant correlations between the level of intellectual ability and the number of riddles solved have been discovered. Linear regression relationships between the number of riddles solved and the successfulness of performing the intelligence test have been built. An assumption that riddles may serve as a cognitive model for the investigation of human intelligence was made.

Keywords

Riddle, Intelligence, Metaphor, Language game

Introduction

In the classical psychology of ability, intelligence is understood as the ability to solve well-defined problems, which, as a rule, have a single solution. The limitations of this interpretation have long been the subject of criticism by both domestic and foreign psychologists J. Guilford, E. Torrance and others). In this situation, a number of leading researchers of the problem of cognitive abilities criticize the academicism of the modern psychology of intelligence. For example, R. Sternberg calls the system of diagnosing intelligence a “vicious circle of testing”, in the process of which one test is compared with another [1]. The need to rely on common sense in the study of intellectual phenomena is also emphasized by representatives of the so-called “contextual interactionism.” Referring to IQ as “the kingdom of hobbits,” M. Andersen criticizes the mechanism of the psychometric approach to intelligence [2]. For example, a large number of studies within the framework of cross-cultural psychology are devoted to the so-called “everyday cognition”. In particular, A. D. Schliemann and D. W. Karracher “… call into question cognitive analysis, which is based solely on laboratory research” [3].

Basic Assumptions

Metaphorical ability-the ability to create metaphors-was spoken of as a creative ability by Aristotle [4]. As you know, metaphorization is based on the vagueness, inaccuracy and ambiguity of everyday concepts that a person operates. The assumption that the understanding and construction of metaphor is closely related to the intellectual abilities of a person is expressed by a number of Russian and foreign scientists (E. Cassirer, D. Lakoff, P. Ricoeur, M. A. and others). In particular, J. Ortega y Gasset points out that “… metaphor serves not only to change but also to think”, and that it “… lengthens the arm of the intellect” [5].

In our study, the criterion of metaphor was the level of success in solving Russian folk riddles. The simplest and most succinct definition of the riddle is given by Aristotel, who understood the latter as “a well-constructed metaphor”. I. I. Revzin, assuming the importance of riddles as an integral element of folk pedagogy, defined a riddle as “… a minimal coherent text that stimulates direct activity (finding a denotation)” [6]. Y. I. Levin, interpreting riddles as “… intentionally transformed description of reality”, emphasized the fundamental difficulties of algorithmization and formalization of the semantic procedure for solving a riddle [7].

The important role of riddles as an integral attribute of archaic cultures is noted by J. Huizinga. Calling the riddle a “sacred game” that is on the verge of “serious and non-serious,” this philosopher emphasizes the fundamental role of the play principle in the structure of social consciousness [8]. L. Wittgenstein, illustrating the understanding of the “language game”, along with such classical examples of intellectual activity as the formulation and testing of hypotheses, the solution of arithmetic problems, also cites the solution of riddles as an example [9]. Although the experimental psychology of thought and intellect has a history of more than a century, only a few studies have been devoted to riddles. Such tasks are considered to be the prerogative of developmental psychology, and the object of study is most often children. It should be noted that the riddles are included in the set of test tasks of the popular in the United States test of intellectual achievements by A. S. Kaufman-Kaufman Assessment Battery for Children (KABCsampler.pdf) [10]. When planning the experiment, we proceeded from the hypothesis that the success of solving riddles, which are an example of problem situations used by people for the “spontaneous diagnosis” of ingenuity and ingenuity, is somehow related to the level of psychometric intelligence of adult subjects.

Research Methodology

In the experiments conducted with 2nd year students of the Faculty of Romance and Germanic Philology of the Bashkortostan State University, three selections of Russian folk riddles taken from an academic collection prepared by V. V. Mitrofanova [11] were used. Each experimental series consisted of thematically homogeneous riddles: in the first series (12 tasks) the subjects solved riddles dedicated to natural phenomena (rain, snow, snowdrift, etc.), in the second (10 tasks) riddles related to a person and parts of his body (teeth, tongue, mouth, etc.), and in the last series (10 tasks) students were offered riddles from the animal world. The experiment involved 130 people, 122 girls and 8 boys, aged 17 to 20 years. The experiments were carried out with training groups during laboratory classes on psychology, and no more than 30 minutes were given to solve each cycle. A separate lesson was devoted to the diagnosis of intelligence according to the test of R. Amthauer modified by V. N. Druzhinin [12].

Results of the Study

Describing the results obtained, it should be noted that a number of tasks turned out to be quite difficult for this contingent of subjects. For example, such riddles as “The little horse drank the whole lake”, or “The mother-in-law stands on the current and threatens the daughter-in-law” in general, no one has solved. Although the experimental tasks were organized into thematically homogeneous cycles, only one subject suggested that the riddles were thematic. The average success rate of solving the riddles of the first series was 1.2, the second-3.3, and the third-0. 6 riddles. The results of R. Amthauer’s test are much more stable: the average indices of success in solving the verbal, arithmetic, and geometric subtests of this method are 4.8, 4.1, and 5.8, respectively. A comparison of the success of solving riddles with indices with indicators of intellectual competence according to R. Amthauer using Kendall’s nonparametric correlation coefficient revealed a large number of significant relationships (p<0.05), highlighted in bold in Table 1.

Table 1: Intercorrelation matrix of intellectual competence according to R. Amthauer with the success of solving riddles.

 

Verbal subtest

Arithmetic subtest Geometric Subtest Total intelligence Success in solving riddles

Verbal subtest

1,00 0,10 0,19 0,36

0,20

Arithmetic subtest

0,10

1,00 0,40 0,65 0,21

Geometric Subtest

0,19 0,40 1,00 0,74

0,35

Total intelligence

0,36

0,65 0,74 1,00 0,34

Success in solving riddles

0,20 0,21 0,35 0,34

1,00

The normality of the distribution of the total results of R. Amthauer test, checked with the help of the Chi-square test, made it possible to apply the apparatus of linear regression analysis, in which the predictor was the number of solved riddles and the regressor is the success of the R. Amthauer test.

In general, the linear relationship between the overall success of solving riddles (X) and the number of correctly solved tasks according to the R. Amthauer test (Y) is described by the following equation (p<0.01):

Y = 9,6 + X (1)

In the process of analyzing the answers of the subjects, it turned out that for a number of tasks the students managed to find answers that did not literally coincide with the correct ones, but in general were no less successful. For example, the answer to the riddle “A black cat licks the window” is “night.” The answer “wipers on the car window” was assessed as no less successful and was counted as a solution. This circumstance, already described in the literature (Levin, 1973), increased the variability of the subjects’ answers and made it possible to compare the “conditional success” of solving individual riddles with the psychometric intelligence.

With the help of step-by-step regression analysis, it was possible to obtain a linear dependence between the total success of solving individual riddles (X) and intelligence (Y) for only seven experimental tasks: (p<0.01):

Y = 9,5 + 1,6X (2)

Conclusions and Prospects of the Study

  1. A comparison of the productivity of R. Amthauer test with the success of students in solving Russian folk riddles revealed a close relationship between R. Amthauer intellectual competence and the number of correctly solved riddles.
  2. The analysis of the success of solving individual riddles made it possible to construct a number of reliable linear regression dependencies between the total success of individual puzzle selections and the indices of intellectual competence according to R. Amthauer.
  3. As a result of the experiments, it can be concluded that the riddle is a prototype of an intellectual test that was part of the system of folk psychology and pedagogy. The intellectual abilities of students, revealed with the help of riddles, are comparable to the indicators of the classical test of cognitive abilities, and riddles can be used as one of the methods of “non-classical” diagnosis of intelligence.
  4. We believe that on the basis of the results obtained, it is possible to make an assumption about the fundamental importance of a comprehensive psychological and linguistic study of such forms of spontaneous intellectual activity as riddles, proverbs, humor, etc., for understanding such a complex phenomenon as human intelligence. Following L. Wittgenstein (Wittgenstein, 2003), the above phenomena can be considered as a kind of “transcendental normative language games” that play an important role in the process of socialization of the individual. In this regard, such a complex and ambiguous phenomenon as a riddle can be considered as a kind of complex lingvo-psychological model for the study of human intelligence.

References

  1. Sternberg RJ, Kaufman JC, Grigorenko EL (2008) Applied Intelligence. Cambridge:
    Cambridge University Press.
  2. Andersen ML (1994) The many and varied social constructions of intelligence. In:
    TR Sarbin, JI Kitsuse (Eds.) Constructing the social (pp. 119-138). London: Sage.
  3. Psychology and Culture / Ed. by D. Matsumoto. St. Petersburg, Piter Publ, 2003.
  4. Ortega y Gasset J (1990) Two great metaphors. In: ND Arutyunova, MA Zhurinskaya
    (Ed.) Theory of metaphor. Moscow: Progress, pp. 68-81.
  5. Kirby JT (1997) Aristotle on metaphor. American Journal of Philology 118: 517-554.
  6. Revzin I. I. K (1975) obshchesemioticheskogo izpretatsii treh postulatov Proppa
    (analiz skazki i teorii svyaznosti teksta) [On the general semiotic interpretation of
    Propp’s three postulates (analysis of fairy tales and the theory of text connectivity)].
    Collection of articles in memory of V. Y. Propp (1895-1970) Moscow. pp. 77-91.
  7. Levin YI (1973)Semantic Structure of the Russian Riddle // Works on Sign Systems,
    Vol. VI. Scientific Articles in Honor of M. I. Bakhtin (To the 75th Anniversary of His
    Birth), 166-190.
  8. Huizinga J (2014) Homo ludens ils 86. Routledge.
  9. Wittgenstein L (1953) Philosophical Investigations. New York, NY, USA: Wiley-
    Blackwell.
  10. Kaufman AS, Kaufman NL (2004) Kaufman Assessment Battery for Children Second
    Edition. Circle Pines, MN: American Guidance Service.
  11. Puzzles. Preparation. Ed. Leningrad: Nauka Publishing House, 1968.
  12. Druzhinin VN (1999) Psikhologiya obshchego sposobnosti [Psychology of general
    abilities]. St. Petersburg, Piter Publ.

AI Simulations of What a Doctor Might Want to Hear from a Patient: Mind Genomics, Synthetic Respondents, and New Vistas for Personalizing Medicine

DOI: 10.31038/MGSPE.2023315

Abstract

The study reported here deals with the creation of questions about what a doctor wants to hear when interacting with a patient, and the evaluation of that question to those questions. The questions, answers and respondents (survey takers) were all generated through artificial intelligence. The results revealed the possibility of AI support in all three areas, and revealed meaningful results when the study was run using the procedure of Mind Genomics. Systematic combinations of messages (elements) according to an experimental design revealed clearly different patterns of responses to the messages based upon who the response personas were designated to be. Three clearly different mind-sets emerged, groups of synthesized respondents whose pattern of coefficients were similar to each other within a mind-set, with the centroids of the mind-sets differing in a way which made intuitive sense.

Introduction

The introduction of artificial intelligence (AI) has created a level of interest perhaps unrivalled in the history of technology, but also spilling over into all areas of human endeavor as well as issues of philosophy [1]. As the use of AI has become easier, more widespread, various uses have emerged, almost beyond counting.

At the same time that technology and society has focused on AI, the author and colleagues have been working with a different, somewhat new way of fathering data about the world of the everyday. The science is called Mind Genomics. The notion is that everyday experience is worth studying for the way it allows us to understand people. Furthermore, rather than studying people by asking them about topics using questionnaires, or by talking directly to them as do qualitative researchers, an intermediate way is to present people with different descriptions, or vignettes, really combinations of phrases to paint a word picture, and then ask the people to rate the vignettes on a scale. The results generate a database of impressions of these vignettes, with the impressions able to be deconstructed into the driving power of each of the element or phrases. The respondent, or survey taker doing this task, cannot ‘game the system’ because the combinations change from person to person, based upon an underlying set of planned combinations, the so-called experimental design [2].

Up to now the test takers in these Mind Genomics studies have been real people, whether of school age or older. The extensive data which has emerged from these studies range from evaluation of descriptions of foods [3,4] and onto education [5] the law [6], social issues [7], and beyond. The Mind Genomics approach has proved fruitful in its ability to allow different ideas to emerge from these studies, as well as uncover new to the world groups of people who think of the world differently. These groups are called mind-sets.

The Mind Genomics Platform and AI as a Generator of Ideas

In the Mind Genomics platform AI has already been used to create questions, and from those questions create sets of answers. It is these ‘answer’s or elements, that Mind Genomics combines into small, easy to read combinations called vignettes. These vignettes, comprise a maximum of four elements and a minimum of two elements, created by an underlying experimental design The vignettes are created in a rigorous fashion, so that:

  1. Each vignette has at most one element or answer from a question, never two or more answers from a question, but occasionally no answer from a question. It is this property of incompleteness that will allow the researcher to use statistical (regression analysis) to show how the elements or answers ‘drive’ ratings
  2. Each vignette is different from every other vignette. The vignettes a systematically changed by a permutation program [8].
  3. Each respondent evaluates a specific set of 24 vignettes, with each element appearing four times. Each set of 24 vignettes is reserved for a specific respondent

During the early part of 2023 the Mind Genomics platform was enhanced by AI, first to provide questions, and then to provide answer to the questions. The enhancement used ChatGPT3.5 [9,10]. The researcher was presented with a screen which requested four questions, and afterwards four screens, each of which requested four separate answers to each question that the research would provide. Though one might not think that the request to provide four questions is particularly daunting, the reality is that it is quite daunting. As a consequence, many nascent uses of Mind Genomics simply abandoned the task. The reality began to become apparent, viz., that people may be good at answering questions, but they are not good at formulating a story in terms of a set of questions to ask which will get at the answer(s). Some may call this a deficit in so-called critical thinking, but for the purposes of this paper it is simply a stumbling block in usability of Mind Genomics.

The creation of a series of built in prompts, provided to the researcher in a non-threatening, rather easy way, ended up producing Idea Coach. We will show the use of Idea Coach in this paper, as part of the specific treatment of the topic, ‘what doctors want in patients’,. We will use Idea Coach to show how the questions are generated, and how data from synthetic respondents are created and analyzed. This paper shows the method, and the nature of the answers that one might get.

The underlying motivation is to see what might emerge from these initial trials with AI acting as a synthetic respondent. The important issue is do the data ‘make sense’ to the reader. The issue about whether the data matches external results must be addressed later, when the approach of creating synthetic respondent has been well worked out. This study is only the first step I that process, not the external validation step. To summarize, the validity considered here is the simplest one of all, namely ‘face validity.’ Do the data generated by AI ‘make sense’

Running the Mind Genomics AI Experiment from Start to Finish

The Mind Genomics process is templated from start to finish. The study presented here deals with what a doctor wants from a patient. The synthesized respondents are going to be medical professionals. The actual study can be found in the website. Much of the set-up of the study has been taken from the senior author’s previous Mind Genomics website, www.BimiLeap.com. The synthetic respondents are created within a new website, Socrates as a Service ™, which uses many of the feature of the Mind Genomics platform, but adds the ability to synthesize respondents simply by describing the way they think, what they do, etc.

Step 1

Give the study a name, select a language for the prompts, and accept the terms for privacy.

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

As noted above, it is at this point in the process that many researchers are stymied, and where the researcher can use AI to help formulate questions. The instructions to ‘tell a story’ are simply meant as a help to the research. The underlying idea is that the questions should deal with different aspects of the topic.

Figure 1 shows the screen requesting the four questions, and the next screen invoked when the request is made to use AI in the form of Idea Coach. The ‘box’ in Panel B of Figure 1 is filled out by the researcher. Typically, the request should comprise an introduction (e.g., explain in detail), the issue (the specific request), and then prompts asking the Idea Coach to produce a question of no more than 15 words, and a question understandable to a person of younger age. For this project the age was ‘12’ years, but in other projects the age has been higher (e.g., around 21 years old). Finally, Panel C shows the return of a subset of the 15 questions produced by AI, with the remaining questions requiring the researcher to scroll down. Panel D shows the final set of questions, edited, and in preparation for the next step in AI empower Idea Coach.

fig 1

Figure 1: Four questions and Idea Coach

The researcher can repeat the request to Idea Coach as many times as desired, with each return by Idea Coach comprising 15 questions, some new, some repeats. At the end of the process, the researcher will have selected four questions, and inserted them into the template, and, if necessary, editing these questions to ensure the proper format of answers to be produced by Idea Coach in the next step. Table 1 presents one set of questions, along with an AI based ‘summarization’ of the questions as well as further extension of the questions into new opportunities. Note that Table 1 is created for every set of 15 questions developed through Idea Coach, as well as for every set of 15 answers to a question produced by Idea Coach (see below). Excel booklet from which Table 1 is extracted is called the project ‘Idea Book.’ Each separate iteration, either to generate questions or answers, generates 15 results. The full Idea Book is available after the project passes the stage of creating questions and answers.

Table 1: The Idea Coach prompt, the first set of 15 questions, and the AI elaboration of those 15

tab 1(1)

tab 1(2)

tab 1(3)

Step 3: Create the Answers

Once the questions have been created and edited (polished to increase the quality of the AI output), it is time to create answers. The same process occurs, with the researcher presented Idea Coach with the edited question, and then 15 answers returned. Again, the researcher has the task of selecting up to four answers and re-running the Idea Coach again for new answers to address the now polished/edited question. During the process it is always possible to revise the question. Figure 2 shows the different steps for the creation of answers. Once again, the Idea Coach can be invoked as many times as desired. Table 2 shows an example of the 15 answers to the first question.

fig 2

Figure 2: Screenshots showing the process for creating the four answers to a question. Panel A shows the partial output from Idea Coach. Panel 2 shows the four answers actually selected, and then slightly edited for use in the study.

Table 2: First set of answers to question #1

tab 2(1)

tab 2(2)

tab 2(3)

Step 4: Select the Final Set of Questions and Answers

Table 3 shows this selection. All text comes from Idea Coach, but with edits at each step of the way to make sure that the elements can be understood in a meaningful way by people, and presumably in that case by AI as well.

Table 3: The final set of questions and answers

tab 3

Step 5: Create the Self-profiling Questions

The personas of the synthetic respondents are created from combinations of the self-profiling questions. The underlying process is systematically one randomly selected answer from each question to create the persona. The personas were created by “Socrates as a Service ™,” the next generation of program in the Mind Genomics platform. Figure 3 shows an example of a classification question, with Panel A having no information, and Panel B showing the same template, but filled out to define the respondent. Note that the self-profiling classification allows the researcher to specify anything desired about the to-be-synthesized respondent. Table 4 shows the actual set of self-profiling questions.

fig 3

Figure 3: Example of one question filled out for the self-profiling classification. Panel A shows the empty placeholder. Panel B shows the first self-profiling classification as filled out by the research. There are up to eight of these questions, each with a possible 2-8 alternative answers.

Table 4: The set of self-profiling questions and answers. The personas were created from combinations of the answers

tab 4

Step 5: Create an Open-ended Question

As part of the Mind Genomics effort, the platform allows the respondent to complete two open ended questions, one before doing the evaluation of the vignettes the other after doing the evaluation of the vignette. Figure 4 shows the request for the open-ended question to be done after the synthetic respondent has ‘evaluated’ the 24 vignettes comprising combinations of elements or ‘messages’.. The normal human respondent generally has a lackadaisical attitude towards filling out these open-ended questions, unless the topic is deeply emotional, such as breast cancer. The inclusion of the open-ended question was done to explore what might emerge from AI. Those results are discussed below.

fig 4

Figure 4: Templated screen for the open-ended question, with the question filled out

Step 6: Create the Respondent Orientation and Rating Scale

Figure 5, Panel A shows the very short respondent orientation. The Mind Genomics process has been set up with the guiding vision that the information needed to rate the vignette would be presented in the combinations of the elements or test messages, as well as influenced by who the respondent ‘IS’ and how the respondent ‘THINKS’. Consequently, the very short introduction simply instructs the respondent to read the vignette. Figure 5, Panel B shows the two-sided scale as presented to the researcher during the set-up. Table 5 shows the actual text of the scale, emphasizing the two sides or dimensions embedded in each scale point.

fig 5

Figure 5: The respondent orientation (Panel 5A), and the rating scale (Panel 5B)

Table 5: The text of the 5-point binary scale used by the synthetic respondent to rate the vignette

tab 5

Step 7: Select the Source of Respondents

The new Mind Genomics platform, now named SaaS (Socrates as a Service™) has expanded the options to include synthetic respondents using AI. Figure 6 shows the choices. The newest choice is at the bottom, ‘I want to use simulated respondents.’ By making the synthetic respondent simply become another choice, the new Mind Genomics platform has created the opportunity for SaaS to become a simple, affordable teaching tool. The researcher can set up the study in the manner previously done, but ‘explore’ the response using AI, in order to learn. Research now becomes a tool to learn both through the combination of Idea Coach + Question Book at the start of the project, and through iterative explorations using AI in the middle or end of the project.

fig 6

Figure 6: Screen shot showing how the researcher can source ‘respondents’.

Step 8: Define Respondent

For studies run with people the first step in the actual evaluation consists of a very short ‘hello’ followed by the pull-down menu for the self-profiling classification. Figure 7 shows this pull-down menu, showing the three answers for the question ‘How do you feel about the insurance companies and the medical health holding companies?’ Human respondents find this way of answering the self-profiling questions to be easy and not intimidating. When it comes to the synthetic respondents, there is no need for Step 8. The program automatically creates the personas, the synthesized combinations of the different answers, with each question contributing exactly one answer to the persona being developed.

fig 7

Figure 7: The pull-down menu for self-profiling classification, used for human respondents, but not for synthetic respondents.

Step 9: Create Test Vignettes by Experimental Design

A hallmark of Mind Genomics is the creation of combinations of messages, these creations being called vignettes. Rather than instructing a respondent to evaluate each of the 16 elements, the Mind Genomics strategy is to combine these elements into small, easy-to-read combinations. There is no effort to link the elements together, an effort which would backfire because the ensuing paragraph of linked elements would contain too much connective material, verbal plaque, as it were.

The actual vignettes are created by an underlying experiment design which ensures that the 16 elements appear equally often (5x in a set of 24 vignettes). Furthermore, no vignette ever has more than one element or answer from a question, but many vignettes have only two or three answers, with other questions failing to contribute to the vignette. Finally, each respondent evaluates a mathematically equivalent set of vignettes by the permutation process, but the actual combinations evaluated by the individual respondents differ from one respondent to another [8].

The foregoing strategy lies at the basis of Mind Genomics. It becomes virtually impossible to game the system because the combinations are overwhelming. The real focus is on the performance of the individual elements. The vignettes are only the way to get the elements in front of the respondent in a way which resembles the seemingly discordant nature of everyday experience. Quite often exit interviews with respondents as well as discussions with professionals end up with the ‘complaint’ that it was simply impossible to figure out the; right answer’, an effect which mildly irritates people, but all too often infuriates academics.

Step 10: Present the 24 Vignettes for a Respondent to the AI and Obtain a Rating on the Five-point Scale

The AI system proceeds by creating a prompt for each vignette. The first part of the prompt defines WHO the respondent is. The respondent is some randomized combinations of answers from the six self-profiling classifications, with each answer appearing approximately equally often across the 801 respondents. This first part of Step 10 will produce a constant persona across the 24 vignettes.

The second part of Step 10 presents the rating question and scale to the AI. This second part of step 10 will produce a constant rating question and set of answers across the 24 vignettes for the synthesized persona.

The third part of Step 10 presents the AI with the vignette. The AI is instructed to assume the persona, to read the scale, and then to rate the vignette on the scale by choosing one of the five answers.

The actual study is now run, the total time for 801 respondents lasting 15-30 minutes. The time may be substantially shorter, but there is extensive back and forth with the AI modules and provider.

Step 11: Uncover the Distribution of the Five-point Scale Ratings across the Set of All Self-profiling Scales

At the end of the process, we can look at the distribution of the ratings across the groups synthetic respondents, these groups defined by the how the synthetic respondent ‘identifies itself’. Table 6 shows a remarkable consistency across the different self-profiling groups. If we were to stop here, we would conclude that there is no discernible difference across the different self-profiling groups, and thus the effort to create synthetic respondents at this stage of AI development has failed. We would, however, be quite wrong in that conclusion, as the further tables will show.

Table 6: Distribution of the five rating scale points for each selection in the self-profiling classificaiton questionnaire

tab 6

Step 12: Transform the Ratings into Binary Variables

A now-standard practice in Mind Genomics is to transform the rating scale. The rating scale created here provides two dimensions. Our focus here is on simulating the positive response ‘My style’, corresponding to the combination or union of ratings 5 and 4, respectively. The transformation makes ratings of 5 or 4 equal to 100, and in turn ratings of 1,2 or 3 equal to 0. To each newly transformed variable, now called R54x, is added a vanishingly small number (<10-4). This prophylactic step ensures some minimum level of variation in R54x, which will become a dependent variable in OLS (ordinary least-squares regression), discussed in Step 11. For other analyses, the system or the researcher can create different binary variables, such as R52X, a positive gut feel.

Step 13: Relate the Presence/Absence of the 16 Elements to the Newly Developed Binary Variables

Table 7 shows the coefficients for the equations relating the presence/absence of each element to the following dependent variables, which have been coded 0 or 100.

R1x-Rating of 1 coded 100, ratings of 2, 3, 4 or 5 coded 0
R2x-Rating of 2 coded 100, ratings of 1, 3, 4 or 5 coded 0
R3x-Rating of 3 coded 100, ratings of 1, 2, 4 or 5 coded 0
R4x-Rating of 4 coded 100, ratings of 1, 2, 3, or 5 coded 0
R5x-Rating of 5 coded 100, rating of 1, 2, 3 or 4 coded 0
R54x-Ratings of 5 or 4 coded 100, ratings of 1, 2 or 3 coded 0
R52x-Ratings of 5 or 2 coded 100, ratings of 4, 3 or 1 coded 0
R21z-Ratings of 2 or 1 coded 100, ratings of 5, 4 or 3 coded 0
R41x-Ratings of 4 or 1 coded 100, ratings of 5, 3, or 2 coded 0

RT-Response time-with human being defined as the number of seconds elapsing between the presentation of the vignette and the response. Not definable for AI, although measurable.

It is clear from Table 7 that the coefficients within a column are quite similar to each other. There are some variations, but remarkably little. Furthermore, the answers seem to make intuitive sense. It does not pay to analyze each set of numbers, however, because within a column the numbers are simply too close. Finally, there is a response time emerging, although it is not clear what that means. The RT, response time, is measured in terms of seconds between the presentation of the vignette and the respondent’s rating. All response times are low, around 0.6, but do not know what is occurring.

Table 7: Coefficients for the Total Panel (801 respondents x 24 vignettes each)

tab 7

Step 14: Show the Linkage between Elements and R54 for Different Levels of Each Persona Variable

A slightly more nuanced picture emerges when the total panel results are broken up into separate persona ‘levels.’ Table 4 shows the six different self-profiling questions, and the answers to each. Tables 8A-8F show the strong performing elements for each persona ‘level’. Each table, Tables 8A-Table 8F, corresponds to one of the six self-profiling questions. The columns correspond to the answers. The coefficients are strong performing values for element, with ‘strong performing’ operationally defined as a coefficient of +14 or higher.

Table 8A: Strong performing elements for persona Q1: How many years have you been a medical professional

tab 8a

Table 8B: Strong performing elements for persona Q2: What makes you dislike a patient?

tab 8b

Table 8C: Strong performing elements for persona Q3: How long is a reasonable time with a patient?

tab 8c

Table 8D: Strong performing elements for persona Q4: What is your feeing about telehealth. No strong performing elements emerged

tab 8d

Table 8E: Strong performing elements for persona Q5: How do you feel about the insurance companies and the medical holding companies

tab 8e

Table 8F: Strong performing elements for persona Q6: How do you feel when you start the day

tab 8f

The development of Tables 8A-8F is straightforward, consisting of the isolation of the vignettes showing the specified persona option, and then running the OLS (ordinary least-squares) regression for all the cases having the appropriate self-profiling answer. Each table has a base size, referring to the number of respondents in the simulated set of 801 who are assigned the particular answer. Thus, in Table 8A, for example, 86 respondents were assigned to the answer 1 of question 1, namely: How many years have you been a medical professional, with the answer 1, ‘I’m a student, planning to start my career.’

The OLS regression [9] returns return with coefficients for each cell, based upon the rating: R54 = k1A1 k2A2… k16A16. The result is a wall of numbers. Table 7 suggests that the highest coefficient for R54 for the total panel is 10. Therefore, Tables 8A-AF shows only those coefficients of 14 or higher. Furthermore, Tables 8A-8F shows only those elements which have at least one coefficient of 14 in a row. This stringent criterion substantially reduces the number of data points that need to be considered.

Our initial results here suggest that there are coefficients higher than others, although not many of them. Nor is the underlying story particularly clear. Finally, the highest coefficient is 17, hardly as strong as the results obtained with human beings, but yet suggesting that AI can differentiate among elements based upon the persona created.

Step 15: Create Mind-sets from Synthesized Respondent Data

Our final analysis for this study considers the existence of mind-sets, different ways of looking at the data. When Mind Genomics is executed with human respondents there is an almost universal emergence of mind-sets, with perhaps the exception of ‘murder [6].

When Mind Genomics data are clustered together on the basis of the coefficients, generally the meaning of the mind-sets becomes exceptionally clear, even though the process of creating mind-sets does not use any interpretation of the data. Rather, the process to create mind-sets is clustering, with the process easy to do with conventional data, and now just as easy to do with synthesized data. The process uses k-means clusters [11,12], and a measure of ‘distance’ between two objects (e.g., between two synthesized persons) defined as (1-Pearson R). The Pearson R, the correlation coefficient, shows the degree to which two sets of numbers co-vary. When the 16 coefficients of the two synthesized people co-vary perfectly, they are considered to be in the same mind-set, the Pearson R is 1.00, and the distance is 0. When they 16 coefficients vary perfectly inversely with each other, they are considered to be in different mind-sets, the Pearson R is-1, and the distance is 2.0.

Moving now to the results from the k-means clustering at the top of Table 9, we see coefficients around 9-11 for the total panel, coefficients 0-22 for two clusters or mindsets but not many high coefficients of 21+, and three mind-sets emerging from three clusters, two of the mind-sets being strong, with a number of coefficients 21 or higher. The value 21 has been chosen for simplicity, based upon observations over a two-year period working with human respondents in different topics.

Table 9: Coefficients for the total panel, and for the two and three mind-set groupings. Strong performing coefficients (21 or higher) are shown in shaded cells. Coefficients with negative or 0 values are not shown.

tab 9

When we apply the criterion of 21 or higher we end up with three mind-sets, two of which show the requisite value of coefficients 21 or higher (Mind-Sets 2 of 3 and 3 of 3, respectively).

Mind-Set 1 of 3-Focus on the process of the visit (but no truly strong elements)
Mind-Set 2 of 3-“Intervention-focused Patients”
Mind-Set 3 of 3-Engaged and collaborative patients.

The three mind-sets can be interpreted more deeply through AI, using the same set of prompts as we used to summarize the ideas on each page of questions (see Table 1) and each page of answers (see Table 2). Table 10 shows the summarization for Mind-Sets 2 of 3 and 3 of 3, respectively. The summarization is based on the commonalities of all elements with coefficients of 21 or higher. Mind-Set 1 of 3 fails to meet that minimum level, and therefore the Idea Coach Summarizer was not applied.

Table 10: Summarization by Idea Coach (AI) of the strong performing elements for Mind-Sets 2 of 3 and 3 of 3. The names of the mind-sets were also suggested by AI.

tab 10(1)

tab 10(2)

tab 10(3)

Step 16: How well does the AI Perform When Synthesizing Respondents?

A continuing effort in Mind Genomics is the attempt to increase critical thinking. How does one measure critical thinking, however, and more importantly, how can one set up criteria to assess the development of critical thinking. One way to assess such thinking is by looking at the set of positive coefficients for the total panel, for the two mind-set solutions, and for the three mind-set solutions. The objective is to create elements with high coefficients, but also elements which are very high in one mind-set, but low in the in other mind-sets. Thus, higher may not be better because the elements do not score differently across the mind-sets. Some preliminary simulation suggest that strong performance occurs with an IDT value of 68-72. The IDT is the Index of Divergent Thought, shown in Table 11. The table shows the relevant parameters to compute the IDT.

Table 11: Computation specifics of the IDT, Index of Divergent Thought. A value between 68 and 72 may be optimal.

tab 11

The results from this study and from several other parallel studies of the same type (doctor-patient) suggest that the IDT values for synthesized data are lower than what are obtained from people. That is, the synthetic respondents do generate easy-to-interpret mind-sets, but the inner structure is not as strong, based upon the IDT of 47 rather than the IDT’s of 70 often observed in the simplest of these Mind Genomics studies. In other words, synthetic respondents give answers, but the ‘deep structure’ is somehow not quite ‘human’.

Discussion and Conclusions

The appetite for AI as synthetic ‘people’ is increasing daily. Whether the topic be social issues [1], health [13], or politics [14] there appears to be a one-way push towards more sophistication in the application. We no longer question the utility or even the ‘validity’ of synthetic people. Rather, the focus is on the improvement of the application. Towards that goal of improvement, the study reported here suggests a new application, namely the use of AI to explore medical issues involving stated specifics of the doctor-patient interaction. The potential for Mind Genomics is this area is as yet unknown, but one might imagine doctors using Mind Genomics with synthetic patients to learn how to interact with patients. It may well be that the years of experience of a doctor in the so-called ‘bedside manner’ might be quickly learned with AI. Only time will tell, but fortunately the use of Socrates as a Service ™ may well shorten that time for learning.

References

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Biotechnology for Increased Resource Utilization of Fish Side-Streams

DOI: 10.31038/NRFSJ.2023623

Abstract

Biotechnology can open new possibilities to recover important nutrients from the large amount of residual raw materials that are lost during the value chain from fish in the sea until it reach consumers. These resources may make up around 60% of the round weight of fish and usually comprise viscera, head, trimmings, frames, skin, and processing water. Technologies like enzyme-assisted hydrolysis and esterification, autolysis, and fermentation are all examples of biotechnologies that can upcycle valuable biomolecules. Furthermore, these biological processes can be combined with fractionation processes to tailor-make new biomolecules with valuable functional, bioactive, sensory, and nutritional properties of these perishable bioresources.

Keywords

Biotechnology, Fish side-streams, Valorization, Enzymatic hydrolysis, Co-products, Fermentation

Introduction

The Ocean provides essential food and nutrients to human health and diets; however, the resources are not utilised to their full potential. Resources get lost along the value chain from catch to consumer or result in non-food products. Simultaneously, more nutrients are needed to feed the growing population in a world with limited natural resources. Food production contributes significantly to greenhouse gas emissions, and global estimates show that 1/3 of the food is lost or wasted [1]. Consequently, about 6 % of the world’s greenhouse gas emissions come from potential food that is never eaten [2,3]. It is a global aim to reduce fish loss and waste by 50% by the year 2030 prioritizing actions like maximizing the usage of co-products and by-catch [4]. Minimizing losses and waste takes precedence in the waste hierarchy, as emphasized by Usmani et al. [5] and Teigiserova et al. [6]. Despite the significance of prevention, it may not always be adequate for maximizing the utilization of intricate and nutrient-rich bioresources. In such cases, food processing comes into play, offering the potential to repurpose, recover, or convert lost biomolecules into valuable products, aligning with a circular economy approach, as highlighted by Usmani et al. [5] and Archad et al. [7]. Biotechnology emerges as a valuable tool in the effort to upcycle these resources, reintegrating them into food value chains for human health and nutrition or other high-value applications.

This short review highlights important residual marine bioresources and a selection of potential biotechnologies to improve their utilization.

Marine Bioresources and Their Biotechnological Potential

Nearly half of the global biological production originates from the Ocean; however, it contributes only 2% of the calorie intake and 15% of the protein intake today [8]. The Ocean is assumed to provide a larger portion of our future food supply due to e.g., high pressure on agricultural land and climatic vulnerability. The global production of fish and seafood has reached around 200 million tons annually [9]. Of the 179,6 million tons of fish globally produced in 2020, 87% were used for human consumption and 12,9% for non-food purposes [10]. Not usually included in these bioresource and food loss calculations are the side-streams that comprise bioresources that were not intended for food in the first place but may contribute significantly to food and nutrition security for a growing population.

These bioresources can also be classified as residual raw materials, co-products, plus products, surplus, leftovers, or potential resources for upgrading or upcycling. Some authors also use the term by-products or waste. Since the term by-product is referred to as resources not intended for food use in the Animal By-product Regulation (Regulation (EC) No 1069/2009 [11], we choose not to use that term in our communication. Moreover, we also avoid using the term waste since it is not associated with high-value applications such as food or feed but should be composted or destroyed. Fish side streams may contain important nutrients such as proteins and lipids and their derivatives can have potential use in food, feed, or pharmaceuticals. Defining these resources as waste or by-products will complicate further use as food ingredients or other products intended for human consumption.

Seafood represents a broad and heterogeneous group of organisms comprising phytoplankton, zooplankton, microorganisms, plants, invertebrates, fish, and mammals. The global marine database reports catch data of more than 1700 different species [12], and many of these species are not being fully exploited for their potential as dietary sources. The main fish species from global fisheries are anchovies, sardines, herring, cod, tuna, salmon, and mackerel, which account for about 30% of the global wild fish catch [13]. Besides these species, many species are still undiscovered or poorly known. The same is the case for organs from commercial fish that are not usually defined as the main food products. Fish catch and fish consumption have increased. So has the generation of residual raw materials. Since more food will be sourced from the Ocean in the future, more side streams will be generated and consequently more knowledge is needed to find high-value applications for these resources.

Marine resources comprise essential macronutrients like proteins, peptides, and lipids and minor nutrients like carbohydrates vitamins, minerals, and antioxidants. The proximate composition of fish and shellfish is primarily water, proteins, and lipids which in the fish muscle usually make up about 98% of the total mass [14]. Such proximate data for different fish species are collected in databases such as the uFiSh [15]; however, the nutritional and chemical composition of fish varies with species, seasons, geographical locations, stages of maturity, and size, and varies particularly among different organs of the fish. The degree of processing decides what residual raw materials become available. For example, when filleting whitefish or fatty fish, half of the biomass is left as residual raw materials or waste [16-18]. Gutting makes the viscera available, de-heading adds the heads to the residual raw materials and filleting also adds the cut-offs, frames, bones, and sometimes skins. These side streams are illustrated in Figure 1 and may vary due to species, seasons, age, degree of spawning etc.

FIG 1

Figure 1: Illustration of residual raw materials from fish handling and processing. Copyright: E. Falch/M. Gilbu

A particularly important nutrient is the marine lipids and the long-chain omega-3 fatty acids. The lipid content and composition are reflected by the diet and are unequally distributed in the fish. For the fatty fish, these lipids are found in the muscle, cut-offs, and visceral organs, while in the lean fish species, the lipids are mainly found in the liver and visceral organs. For wild-caught fish, the gut usually demonstrates a large variation of proximate composition and lipids due to differences in feed composition and content. On the other hand, for the farmed species the composition does not vary much due to the same feed given to the fish, and usually a period with no feeding and emptying of the stomach before slaughtering. The fisheries of cod fish (Gadidae family; cod, saithe, tusk, ling, haddock) lose much biomass on the sea. We therefore calculated the potential content of long-chain n-3 fatty acids from these side-streams and found that filleting generated 2/3 as residual raw materials and a production of 10,000 kg fillet made 1000 kg lipids comprising ca 30% long-chain n-3 fatty acids [16,17]. This is a significant contribution to a healthy diet and could provide heart health to a large population since 250 mg per day of these fatty acids is the recommended level for using health claims for maintaining a healthy heart [19,20]. The lipid content in the liver varied between 45 and 60% and the lipid content in the visceral fraction varied between 2 and 9% [16,17]. So, these lost resources could contribute to health particularly these marine lipids that are part of a scarce resource from the oceans and waters. Residual raw materials from most species are generally rich in marine fatty acids, which are in high demand globally.

However, it is important to shed light on the highly perishable nature of the marine residual raw materials. It usually degrades fast due to high water content and thereby perfect conditions for microbes, autolysis by endogenous enzymes and lipid oxidation due to the high content of unsaturated lipids [21,22]. This fast biochemical degradation requires correct handling and processing [21]. Additionally, assuring consumers’ acceptance requires not only controlling and preventing unwanted reactions but also choosing the right processing to make stable and palatable products with texture and physico-chemical properties as ingredient [23]. Here, biotechnologies and fractionation can play a major role.

Biotechnology for Increased Utilization

Among the common and accepted definitions of biotechnology is “the integration of natural science and organisms, cells, parts thereof, and molecular analogues for products and services” [24]. The use of enzymes and microbes (including microalgae) in combination with fish side streams all fall within the biotechnology category and may help increase the utilization of these resources. These processes are illustrated in Figure 2. The potential uses of these selected biotechnologies are further discussed below.

FIG 2

Figure 2: Illustration of biotechnological processes for utilizing residual raw materials from fish. Copyrights: E. Falch/M. Gilbu

Enzyme Assisted Bioconversion

First, enzyme-assisted bioconversion is widely used in research but not adapted to its full potential industrially. Enzymes, as natural catalysts, offer the ability to break down molecules without resorting to high temperatures or harmful chemicals. This way, they can facilitate nutrient extraction from processing side streams transforming them into diverse molecules, including peptides and lipids. A notable advantage of employing commercial enzymes as processing aids lies in their precision. Specific enzymes can selectively target specific molecules, allowing for tailored modifications based on desired properties like functionality, taste, or health effects. For instance, enzymes can hydrolyze proteins into peptides with specific characteristics or convert fatty components into specific omega-3 fatty acids. Another benefit of using hydrolytic enzymes is their role in releasing lipids from the protein-rich matrices before the centrifugal separation of lipids instead of using harsh chemical extraction or high-temperature treatment [25-27]. Given that most side-streams from fish are protein-rich, proteases can effectively hydrolyse proteins into smaller peptides, serving as a valuable pretreatment step for releasing valuable compounds. Among industrial enzymes, hydrolases, particularly proteases and lipases, are extensively employed, with proteases being among the best characterized [28]. Enzymatic hydrolysis exhibits significant potential in recovering proteins and peptides from protein-rich side streams [25,29]. The primary outcome from these processes is fish protein hydrolysates serving as both a protein source and a source of bioactive peptides. Numerous studies have highlighted the bioactive properties of fish protein hydrolysates, including antihypertensive, antimicrobial and antioxidative properties alongside preservative, functional and flavour-enhancing properties [30-32]. A well-known reported taste challenge of fish protein hydrolysates is the formation of bitter peptides [31].

While specific proteases are often required to achieve these properties, the enzymatic hydrolysis process typically benefits from complementary fractionation techniques such as membrane filtration. This combined approach holds promise for unlocking the full potential of enzyme-assisted bioconversion in industrial applications.

Enzymes can also be used to design new bioactive molecules. One example is up-concentrating marine omega-3 fatty acids (concentrates). Fish oil can be separated after thermal treatment or after enzymatic hydrolysis with proteases. With fish oil as the basis, lipases can catalyze the hydrolyzation of fatty acids or ethyl esters from the glycerol backbone, making it possible to fractionate into molecules with higher concentrations of omega-3 fatty acids. This can be conducted with either specific or non-specific lipases, meaning that the lipases can hydrolyse fatty acids randomly, on specific positions in the molecules or on specific fatty acids. After obtaining the targeted fatty acids, lipases can again be used to esterify into new acylglycerols with higher concentrations of omega-3 fatty acids [33].

Enzymatic hydrolysis may also be conducted using endogenous enzymes, which means those enzymes that are already present in the raw materials. In such autolysis, the conditions are adjusted to facilitate the activities of enzymes and prevent microbial growth [34]. These fish hydrolysates are generally a basis for feed and pet food. During autolysis, the biomass becomes liquid which makes it possible to separate proteins from lipids and further fractionation. The most common process is silage where formic acid is added to reduce the pH, but there are also examples of processes with no use of formic acid together with the endogenous enzymes. In Norway, recirculation companies such as Scanbio (www.scanbio.com) provide a logistic solution to the aquaculture industry and with several vessels, they move around to collect silage for further processing into feed or pet food.

While the use of industrial enzymes can help control the reaction and conduct planned reaction products, the reactions in autolysis are more challenging to control since there will be many different types of enzymes in play. One example of this is from the work with cod viscera where cholesterol unexpectable was reduced on behalf of the cholesteryl esters [35]. With no inactivation of endogenous enzymes, a range of different enzymes will be present to hydrolyse, esterify and degrade the different molecules. Consequently, this leads to an uncontrolled range of reactions and reaction products.

The examples above have focused on the enzymes in their pure form (industrial enzymes) or as a component in the raw material. Industrial enzymes are generally a result of precision fermentation where microbes act as enzyme producers. The large enzyme producer Novozymes, searches for enzymes with specific activities in nature for then using bacteria or fungi as enzyme factories (https: //www.novozymes.com/). Their strain database comprises > 50,000 microstrains. The leftovers (nutrients, water, and microorganisms) from Novozymes enzyme production are used as farm fertilizer.

Fermentation for Upcycling

Fermentation has become important for a wider range of applications and is expected to play an even more important role in future ingredients and products [36,37]. The technology is environmentally friendly with low use of energy [38,39] and no use of harmful chemicals or harsh temperatures. Fermentation has long traditions for use in fish preservation and to improve the sensory attributes of fish products [40]. Longer shelf life can directly prevent fish loss and waste, but fermentation can also offer possibilities to improve the utilization of the fish side streams [38-40]. Common microorganisms used in fermentation are certain bacteria strains, yeast, mould, and microalgae.

Biomass fermentation is usually run in bioreactors with microorganisms grown under specific conditions that facilitate controlled growth with a substrate. This is an efficient way to produce biomolecules with fast-growing cells with doubling times in hours compared to months or years for animal cells [36]. Examples of potential biomolecules produced during the fermentation of fish side streams are proteins, peptides, gelatine, oils, enzymes, antioxidants, nutrients, flavours, speciality minor nutrients, biofuel, and fertilizers [38-42]. There are also some good recent examples of the use of bacterial fermentation to improve the application of oil from fish silage [41] and the nutritional quality of fish meal [42] for use in food and feed ingredients.

Fish side streams are also a good substrate for the cultivation of microalgae to produce valuable nutrients [39,43] with applications in food, feed or as biofuel. Cultivation of microalgae is particularly suggested as an important future protein source. These side streams usually contain organic matter, nitrogen, phosphorus, and other nutrients that are beneficial for microalgae growth. However, depending on the nutrient requirements of the different microalgae strains it might be necessary to adjust with additional nutrients or dilute to get the right nutrient balance. Microalgae can utilize the nutrients present in fish side streams, promoting their development and offering a promising solution for both the management of waste and the production of microalgae biomass. There are several examples of cultivation of microalgae from fish-side streams. Venugopal and Sashidharan [43] discussed microalgae cultivation from fish by-catch and side streams as a future protein source, while Vidya et al. [44] combined processing side streams from dairy and fish as substrates for microalgae with the production of lipids and high-value pigments and Tropea et al. [45] combined fermented fish side streams with lemon peel for production of aquafeed. These are just a few examples and researchers claim that we only see the start of the full potential of using fermentation for new food ingredients [46].

Future Perspectives

Using these food resources more effectively is crucial for food security, environmental impact reduction, and economic benefits in food production. While preventing waste is the best option in the waste hierarchy [5,6,47], food processing and biotechnology can allow the reuse, recovery, or conversion of lost biomolecules into valuable products [5,7]. There is a wide variety of potential new applications to improve the utilization of food and fish resources and new overviews of possibilities [48-50] and some studies also explore the new ingredients used in food products such as the use of microalgae in burgers [51]. This area is expected to continue to grow so nutrients in the future are not lost but becoming a part of a circular economy system.

Acknowledgement

I acknowledge all motivated and innovative students and PhDs who contribute to the better utilization of our precious global food resources and enrich our jobs as supervisors. I also acknowledge NTNU Grafisk with Mariane Gilbu for the illustrations. Funding from Norwegian Research Council grant 294539 (SUPREME), grant 303497 (OMEGA) and JPI A Healthy Diet for a Healthy Life for the new Up4Food project helps us keep on researching for the best solutions.

References

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The Tragedy of Smoking, Alcohol, and Multiple Substance use during Pregnancy

DOI: 10.31038/AWHC.2023644

Abstract

Background: Antenatal substance use is a significant public health concern in South Africa. Information on smoking, drinking and drug use during pregnancy was collected prospectively for the Safe Passage Study of the Prenatal Alcohol in Sudden infant death syndrome and Stillbirth Network.

Objectives: Data from 4 926 pregnant women in a local community near Tygerberg Academic Hospital, were examined to determine whether associations between different substance use groups and postnatal infant outcomes at birth and 1 year were significant.

Methods: Gestational age (GA) was determined by earliest ultrasound. Maternal data were collected at enrolment or first antenatal visit. Substance use data were obtained at up to four occasions. Birthweight data were derived from medical records, and birthweight z-scores (BWZs) were specifically calculated using INTERGROWTH-21st study data. Statistical analyses were done with Statistica version 13.

Results: Women who used more substances enrolled later, were younger, and had smaller mid-upper arm circumferences (MUACs), less education and lower monthly income than women who used no substances (control group). Infants born to women who used more substances had lower GA at delivery, birthweight and BWZ than infants from the control group. At 1 year, infants born to women who used more substances had a lower weight, shorter length and smaller head circumference. Education was positively associated with all infant outcomes at birth and 1 year. MUAC was positively associated with infant BWZ, and weight and length at 1 year. Income was negatively associated with BWZ, but positively associated with all 1-year outcomes.

Conclusion: Substance use during pregnancy affects infant outcomes at birth and 1 year of age. The addictive properties of substance use make cessation difficult, so prevention strategies should be implemented long before pregnancy. Higher maternal education, associated with better infant outcomes at birth and 1 year and acting as a countermeasure to substance use, is of paramount importance.

Keywords

Smoking, Drinking, Marijuana, Methamphetamine, Pregnancy

Introduction

Substance use during pregnancy is on the increase worldwide [1-4] and is a significant public health concern [5,6]. In South Africa (SA), use of multiple substances during pregnancy is common. In a survey of 5 232 pregnant women visiting midwife obstetric units in Cape Town, it was found that 36.9% used alcohol and drugs, 34.9% alcohol only, and 1.6% drugs only [7]. Also in Cape Town, a substudy of the Safe Passage Study (SPS), on the value of maternal serum alpha-fetoprotein measurements, found that 61% of pregnant women smoked, 55% drank alcohol, and 9% and 5% used marijuana and methamphetamine, respectively [8]. Methamphetamine use in pregnancy is associated with poorer neonatal outcomes, especially decreased birthweight, head circumference and body length [9,10]. The effects of marijuana use during pregnancy are less clear, with reports ranging from no adverse effect with regard to the likelihood of prematurity or LBW [11-13] to a reduction in birthweight, length and head circumference [3] and an increase in preterm births and Growth Restriction (GR) [14,15].

The association of marijuana use with poor perinatal outcome seems to be attributable to concomitant use of tobacco and other confounding factors [12]. Perinatal outcome is particularly susceptible to socioeconomic conditions affecting lifestyle choices and behaviour [16]. Low socioeconomic status and lower educational attainment increase the risk of smoking during pregnancy significantly [17,18]. Smoking is not only associated with complications such as preterm birth, GR and stillbirth [17,19,20], but has long-term maternal implications such as lung cancer, cardiovascular and chronic respiratory disease, oral diseases and strokes, and long-term infant implications such as respiratory problems (e.g. childhood asthma), infections, obesity, cleft lip/palate, and neurodevelopmental and behavioural problems [21-24].

Interestingly, only the effect of cocaine on birthweight remained significant after adjusting for confounding variables [5]. It is important to note that very few pregnant women use methamphetamine or marijuana on their own; most of them also use nicotine or alcohol, or both. In a study of 12 069 pregnant women, it was found that 45% of marijuana users also smoked [13]. The same applied to users of methamphetamine, of whom 78.6%, 42.9% and 39.3% used tobacco, alcohol, and marijuana, respectively [2].

Of all three health-compromising behaviours, smoking, alcohol consumption and recreational drug use, cigarette smoking has been most studied and strongly implicated in reduced fetal growth [25]. Our previous finding that significantly more pregnant smokers than pregnant non-smokers engaged in heavy alcohol consumption [26] is supported by Okah et al. [27]. They found that pregnant smokers were seven times more likely than non-smokers to use alcohol and/or drugs, and that the rate of heavy smoking and moderate/heavy drinking increased with the number of health-compromising behaviours. Infants antenatally exposed to both alcohol and cigarettes had a substantially higher risk of sudden infant death syndrome compared with those who were unexposed, or exposed to alcohol or cigarettes alone [28].

As the information on smoking, drinking and drug use for the SPS was collected prospectively, this database was ideal to examine the interactions of substance use during pregnancy on infant outcome [29].

Methods

The SPS of the Prenatal Alcohol in Sudden infant death syndrome and Stillbirth (PASS) Network.was designed to investigate the role of prenatal alcohol exposure in the outcome of 12 000 pregnancies in SA (Cape Town) and the USA (Northern Plains). Women recruited included those with low-and high-risk pregnancies, with a wide range of exposures to alcohol, nicotine, marijuana and methamphetamine [29]. The present study was limited to the SA arm of the SPS, where participants were recruited at a community health centre close to Tygerberg Academic Hospital (TAH), Cape Town. Participants were enrolled between August 2007 and January 2015 and infants were followed up until the end of August 2016. Gestational Age (GA) was determined by earliest ultrasound before the second antenatal visit. Depending on the GA at enrolment, women had up to three further antenatal visits at TAH, at 20-24, 28-32 and 34-38 weeks. The revised Timeline Followback method was used at up to four occasions to obtain detailed information on drinking, cigarette smoking, and the use of marijuana, amphetamines and other substances during pregnancy [30]. Anaemia was based on laboratory results of a haemoglobin value <11 g/dL during pregnancy and obtained from Medical Chart Abstraction (MCA). Demographic and anthropometric information was obtained at enrolment or the first antenatal visit. Maternal weight was measured twice, using a regularly calibrated high-quality scale. For the Mid-Upper Arm Circumference (MUAC), the midpoint of the upper arm was first determined and then the circumference measured twice. If any two measurements differed by >1 kg (weight) or 2 mm (MUAC), a third measurement was taken and the mean of the closest two measurements used.

A pregnancy loss or fetal demise before 20 weeks, according to the US definition for SPS, was defined as a miscarriage, whereas a non-live birth at ≥20 weeks was regarded as a stillbirth [31-33]. Terminations of pregnancies after 20 weeks were done for medical reasons. Death of a liveborn infant before the age of 1 year was defined as an infant death. A social worker, employed for the SPS, was available to all women for counselling if necessary or requested.

Newborns were weighed immediately after birth and the information was entered in the maternal chart, from where it was obtained by MCA after delivery. The GA at delivery, obtained from the Electronic Data Capturing (EDC) system, together with fetal sex was used to determine birthweight z-scores (BWZs) and centiles specifically for us upon request, from the international standards of the INTERGROWTH-21st study (available for GAs from 168 to 299 days, excluding twins) [34].

The infants were seen at 1 year of age and the assessment date was adjusted for prematurity, e.g. an infant born 10 weeks (70 days) early had a required 1-year age of birth date + 365 + 70 days to birth date + 365 + 70 + 30 days at 1-year assessment. At the beginning of our study, infants born at term were required to have an age of 365-30 to 365 + 30 days at their 1-year examination, but this was soon changed to between birth date + 365 days and birth date + 365 + 30 days. Infants were weighed (1YW), and their length (1YL) and head circumference (1YHC) were measured by trained research workers according to a specific protocol. For weighing the infants, a Charder digital baby scale was used (Charder Electronic Co. Ltd, Taiwan). The child, dressed in a clean, dry diaper, with a vest during winter, was weighed to the nearest 0.1 kg. The process was repeated, and if the measurements differed by >0.2 kg, a third measurement was taken. A Seca 416 infantometer (Seca Deutchland, Germany) was used to measure the length to the nearest millimetre. The full procedure was repeated for a second measurement and if it differed by >2 mm, a third measurement was taken. A flexible tape measure was used to measure the head circumference to the nearest millimetre while the child was sitting on the mother’s lap or lying down. The tape measure was placed over the occipital protuberance at the back of the head and around to just over the supraorbital ridge and the forehead in front. The procedure was repeated, and done a third time if the first two measurements differed by >2 mm. All the measurements were entered on a specific case report form, and later on the EDC system.

To examine the effects of various combinations of exposure to nicotine, alcohol, marijuana and methamphetamine, 11 different combinations were used, namely no exposure (Control), NoDrugsDrink, NoDrugsSmoke, NoDrugsDrinkSmoke, MarSmoke, MarDrink, MarDrinkSmoke, MetDrink, MetSmoke, MetDrinkSmoke, and All (used all four substances). Since only 12 and 2 participants used only marijuana or only methamphetamine, respectively, separate groups for these drugs were not developed and they were excluded from the cohort. Outcome variables studied were BWZ, 1YW, 1YL and 1YHC. Since we, and others, have shown that MUAC, maternal education and household income play important roles in newborn and 1-year outcomes, these were used as confounders [35,36].

Statistical analyses were performed using the Statistica data analysis software system, version 13 (TIBCO Software Inc., USA). Descriptive statistics were used to describe continuous variables, which were compared between groups with analysis of variance (ANOVA). Bonferroni or least significant difference multiple comparisons identified significant differences between the means in the ANOVA. Non-parametric tests such as the Mann-Whitney U-test or the Kruskal-Wallis test compared differences between groups where responses were not normally distributed. Two-way ANOVAs were used to compare the influence of two factors on continuous response variables. The maximum likelihood χ2 test determined significance in categorical data and was used to compare the substance use groups with the Control group. Spearman correlations measured correlations between ordinal/continuous response variables. A p-value <0.05 indicated statistical significance. The three prespecified confounding variables were used in multiple regression analyses with 11 groups of smoking, drinking, marijuana and methamphetamine combinations for each of the four outcome variables to determine their association and the underlying effect of substance use.

Ethics approval for the study was obtained from the Health Research Ethics Committee of Stellenbosch University (ref. nos N06/10/210 and S19/07/119), as well as from the Western Cape Department of Health. Participants were able to withdraw at any time during the study.

Results

The full cohort consisted of 4 926 pregnant women, of whom 877 (17.8%) used no drugs, cigarettes or alcohol (Control), 825 (16.7%) used no drugs but drank (NoDrugsDrink), 862 (17.5%) used no drugs but smoked (NoDrugsSmoke), 1 801 (36.6%) used cigarettes and alcohol (NoDrugsDrinkSmoke), 64 (1.3%) used marijuana and cigarettes (MarSmoke), 27 (0.5%) used methamphetamine and cigarettes (MetSmoke), 20 (0.4%) used marijuana and alcohol (MarDrink), 11 (0.2%) used methamphetamine and alcohol (MetDrink), 274 (5.6%) used marijuana, alcohol and cigarettes (MarDrinkSmoke), 88 (1.8%) used methamphetamine, alcohol and cigarettes (MetDrinkSmoke), and 77 (1.6%) used all four substances (All). This equated to 65% of women who smoked, 63% of women who drank, 9% of women who used marijuana and 4% of women who used methamphetamine. Excluded from this cohort were twin pregnancies, withdrawals, participants lost to follow-up, women who used marijuana or methamphetamine alone or had missing substance use data, and multiple enrolments. Only the first enrolment of a participant was included in this cohort. Preterm birth (<37 weeks) and very preterm birth (<32 weeks) occurred in 598 (12.1%) and 85 (1.7%) women, respectively. Of the total cohort (4 926 women) 65 women (1.3%) were HIV positive, 1 979 (40.2%) were anaemic, 8 (0.2%) had a miscarriage, 7 (0.1%) had a termination of pregnancy, 657 (13.3%) had low-birthweight (LBW) infants who weighed <2 500 g, 840 (17.1%) had small-for-gestational-age (SGA) infants who fell below the 10th birthweight centile, 44 (0.9%) had a stillbirth, and 45 (0.9%) had an infant death.

Information on the biometric measurements and socioeconomic conditions is provided in Table 1.

Table 1: Basic descriptive statistics of all participants

Variables

Valid N Mean Median Minimum Maximum Lower quartile Upper quartile

SD

Gestational age at enrolment (days)

4 926

142 141 38* 276 105 177 49

Maternal age (years)

4 926 24.4 23 16 45 20 28

6.0

Maternal arm circumference (mm)

4 838

276 267 175 535 241 303 46

Maternal body mass index (kg/m2)

4 787 25.6 24.2 13.7 55.9 21.2 28.9

5.8

Gravidity

4 916

2.1 2 1 10 1 3 1.3

Education (years)

4 919 10.1 10 2 13 9 12

1.7

Household income (ZAR/month)

3 500

886 750 45 6 000 500 1 200 607

GA at delivery (days)

4 926 272 275 61 313 267 282

18

Birthweight (g)

4 862

3 016 3 030 190 5 740 2 700 3 380 574

Birthweight z-score

4 847 -0.34 -0.37 -6.34 4.12 -1.04 0.33

1.03

Infant age at 1 year (days)

4 500

372 369 330 475§ 366 377 17

Infant weight at 1 year (kg)

4 490 9.4 9.3 5.3 16.9 8.5 10.3

1.4

Infant length at 1 year (cm)

4 408

73.7 73.7 60.7 88.0 71.8 75.6 3.0

Infant head circumference at 1 year (cm)

4 479 46.1 46.0 41.1 54.7 45.1 47.0

1.5

SD: Standard Deviation.
*Single case that deviated from required 6 weeks, but permission obtained to keep included.
Miscarriages included.
Initial time window minimum that was corrected later.
§Time window maximum adjusted for prematurity
The only excessively large value, not removed.

Table 2 summarises the maternal biometric measurements and socioeconomic conditions that were compared for the different substance use groups. Women in the Control group enrolled the earliest for antenatal care, had the largest MUAC and BMI, and also earned the highest mean income per month. Women in the MetSmoke group enrolled the latest, had the highest gravidity without being the oldest women, had the smallest mean MUAC, had the lowest average monthly income, and had the joint lowest education together with the MarSmoke and All groups. Women in the MarDrink group had the joint lowest gravidity and the highest education. Women in the MetDrink group were the oldest and had the joint highest gravidity. Women in the MarDrinkSmoke group were the youngest, had the joint lowest gravidity, had the lowest BMI, and were significantly the most anaemic.

Table 2: Biometric measurements and socioeconomic conditions compared in different substance use groups.

Variables F

p-value

Measure Substance use group
Control

(n=877)

NoDrugsDrink

(n=825)

NoDrugs Smoke

(n=862)

NoDrugsDrinkSmoke

(n=1 801)

MarSmoke

(n=64)

MetSmoke

(n=27)

MarDrink

(n=20)

MetDrink

(n=11)

MarDrinkSmoke

(n=274)

MetDrinkSmoke

(n=88)

All

(n=77)

Gestational age at enrolment (days) <0.01* Letters d cd cd c bcd a bcd abc cd b b
Mean 137 142 142 142 145 184 151 170 141 154 157
SD 49 48 50 49 52 47 49 42 45 53 46
Maternal age (years) <0.01* Letters a bc ab cd de abcd bcde abcd e bcd bcd
Mean 25.7 24.6 24.9 24.1 22.1 24.7 21.3 26.6 20.4 23.6 23.0
SD 6.2 5.8 6.2 5.8 5.8 4.7 4.8 4.7 4.4 4.4 4.7
Maternal arm circumference (mm) <0.01* Letters a ab bc c d d abcd abcd d cd cd
Mean 286 283 276 273 253 250 265 271 253 265 260
SD 51 49 47 43 32 26 39 31 37 39 42
Body mass index (kg/m2) <0.01* Letters a a b bc d bcd abcd abcd d cd cd
Mean 26.8 26.6 25.6 25.3 23.0 23.1 24.2 25.0 22.7 23.8 23.4
SD 6.2 6.1 5.8 5.5 4.0 3.8 4.6 3.1 4.1 4.3 5.0
Gravidity <0.01* Letters abce df ab cdef cdefg acd befg abcdefg g abcdef abcdef
Mean 2.2 1.9 2.4 2.1 1.8 2.8 1.5 2.8 1.5* 2.2 2.1
SD 1.2 1.2 1.4 1.2 1.1 1.4 1.1 1.6 0.9 1.4 1.2
Education (years) <0.01* Letters b a c c d d ab abcd d d d
Mean 10.5 10.7 9.9 9.9 9.1 9.1 10.8 9.9 9.4 9.4 9.1
SD 1.7 1.6 1.7 1.7 1.6 2.0 1.4 1.7 1.5 1.5 1.6
Household income (ZAR) <0.01* Letters a ab bc c cd abcd abcd abcd d cd d
Mean 997 987 880 844 639 566 902 699 636 720 573
SD 667 597 601 586 514 483 525 296 460 515 539
Anaemia with haemoglobin <11 g/dL Compared with Control N 345 319 350 725 26 12 12 5 127 30 28
% 39.3 38.7 40.6 40.3 40.6 44.4 60.0 45.5 46.4 34.1 36.4
χ2 p-value 0.776 0.590 0.649 0.839 0.593 0.062 0.680 0.039* 0.336 0.608

Mar: Marijuana; Met: Methamphetamine; SD: Standard Deviation;
Letters=significance lettering. If the significance lettering between 2 groups have common letters (e.g. b and bcd), the groups do not differ significantly.
*Significant at p<0.05 (F or χ2).
Smallest mean value.
Largest mean value.

Infant outcomes at birth and 1 year were compared in the different substance use groups and are summarised in Table 3. Infants from the Control group were heaviest at birth, had the largest BWZ, and were joint heaviest at 1 year. Infants from the NoDrugsSmoke group were significantly more premature, with more LBW and GR (SGA), and had more deaths compared with the Control group. Infants from the NoDrugsDrink group had the highest GA at birth and were joint heaviest at 1 year, whereas infants from the MarDrink group had the largest mean length and head circumference at 1 year. Infants from the MetDrink group had the lowest mean GA (<37 weeks) and more were premature; they had the lowest birthweight, and more were stillborn. Those alive at 1 year also had the lowest mean weight, lowest mean length and lowest mean head circumference, despite their adjusted age at 1 year. The MetSmoke group had the highest significant rate of infant deaths. Infants from the MarDrinkSmoke group had the lowest BWZ and compared with the Control group had the highest proportion who had LBW and were SGA.

Table 3: Infant outcome at birth and 1 year compared in different substance use groups

Variables F p-value Continuous data measure Substance use group
Control

(n=877)

NoDrugsDrink

(n=825)

NoDrugsSmoke

(n=862)

NoDrugsDrink

Smoke

(n=1 801)

MarSmoke

(n=64)

MetSmoke

(n=27)

MarDrink

(n=20)

MetDrink

(n=11)

MarDrinkSmoke

(n=274)

MetDrinkSmoke

(n=88)

All

(n=77)

Gestational age at delivery <0.01* Letters b a cd bc cd de abcd e bcd cd d
Mean 273 275 271 272 268 265 269 255 271 269 268
SD 20 15 18 18 19 12 29 23 16 11 15
Birthweight <0.01* Letters a a b b cd abcd abc d cd bcd cd
Mean 3 131 3 111 2 994 2 976 2 818 2 913 3 029 2 564 2 851 2 932 2 812
SD 585 536 596 567 536 453 503 772 566 463 531
Birthweight z-score <0.01* Letters a b b c cd abc abcd abcd d abc cd
Mean -0.14 -0.24 -0.32 -0.43 -0.64 -0.20 -0.44 -0.21 -0.66 -0.32 -0.61
SD 1.1 1.0 1.0 1.0 0.9 1.0 1.0 1.0 0.9 0.9 0.9
Infant age at 1 year 0.02* Letters c c ab abc b b abc abc ab abc ac
Mean 371 371 373 372 376 379 375 382 373 373 369*
SD 16 15 18 17 17 16 11 35 19 16 17
Infant weight at 1 year <0.01* Letters ab a cd c bcde e abcde e e de e
Mean 9.6 9.6 9.4 9.4 9.2 8.6 9.3 8.3 9.2 9.1 8.9
SD 1.4 1.4 1.3 1.4 1.2 1.1 1.3 0.9 1.3 1.5 1.2
Infant length at 1 year <0.01* Letters a a b b bc c ab abc c c c
Mean 74.2 74.2 73.7 73.6 73.2 72.3 74.6 72.1 73.0 72.7 72.5
SD 2.9 3.0 3.0 3.0 2.6 2.6 2.8 2.4 3.1 2.7 3.3
Infant head circumference at 1 year <0.01* Letters ab b ac c cd cd abc d cd abc cd
Mean 46.2 46.2 46.0 46.0 45.7 45.6 46.4 44.9 45.9 46.0 45.7
SD 1.5 1.5 1.4 1.5 1.3 1.4 1.5 1.4 1.5 1.4 1.5
Variables χ2 pvalue Categorical data measure Substance use group
Control

(n=877)

NoDrugsDrink

(n=825)

NoDrugsSmoke

(n=862)

NoDrugsDrinkSmoke

(n=1 801)

MarSmoke

(n=64)

MetSmoke

(n=27)

MarDrink

(n=20)

MetDrink

(n=11)

MarDrinkSmoke

(n=274)

MetDrinkSmoke

(n=88)

All

(n=77)

Preterm birth <37 weeks Compared with Control N 93 68 129 224 10 4 3 5 34 15 13
% 10.6 8.2 15.0 12.4 15.6 14.8 15.0 45.5§ 12.4 17.0 16.9
χ2 p-value 0.096 0.006* 0.168 0.214 0.486 0.530 <0.001* 0.405 0.068 0.093
Very preterm birth <32 weeks Compared with Control N 16 8 18 32 2 0 1 1 6 0 1
% 1.8 1.0 2.1 1.8 3.1 0.0 5.0 9.1 2.2 0.0 1.3
χ2 p-value 0.135 0.691 0.931 0.463 0.479 0.303 0.080 0.700 0.201 0.738
Low birthweight <2 500 g Compared with Control N 87 75 123 267 10 4 4 3 56 13 15
% 9.9 9.1 14.3 14.8 15.6 14.8 20.0 27.3 20.4§ 14.8 19.5
χ2 p-value 0.560 0.005* <0.001* 0.147 0.405 0.140 0.058 <0.001* 0.154 0.009*
Growth-restricted infant <10th centile Compared with Control N 116 106 146 350 13 3 4 1 69 13 19
% 13.2 12.8 16.9 19.4 20.3 11.1 20.0 9.1 25.2§ 14.8 24.7
χ2 p-value 0.817 0.031* <0.001* 0.112 0.749 0.379 0.687 <0.001* 0.685 0.006*
Miscarriage <20 weeks Compared with Control N 3 0 1 3 0 0 1 0 0 0 0
% 0.3 0.0 0.1 0.2 0.0 0.0 5.0 0.0 0.0 0.0 0.0
χ2 p-value 0.093 0.325 0.367 0.639 0.761 0.002* 0.846 0.332 0.583 0.607
Termination of pregnancy Compared with Control N 2 2 0 3 0 0 0 0 0 0 0
% 0.2 0.2 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
χ2 p-value 0.951 0.161 0.729 0.702 0.804 0.831 0.874 0.429 0.654 0.675
Stillbirth Compared with Control N 8 4 6 17 1 0 0 1 2 2 3
% 0.9 0.5 0.7 0.9 1.6 0.0 0.0 9.1§ 0.7 2.3 3.9
χ2 p-value 0.292 0.614 0.936 0.606 0.618 0.668 0.007* 0.777 0.230 0.019*
Infant death Compared with Control N 3 1 14 20 0 2 0 0 2 2 1
% 0.3 0.1 1.6 1.1 0.0 7.4§ 0.0 0.0 0.7 2.3 1.3
χ2 p-value 0.347 0.007* 0.043* 0.639 <0.001* 0.793 0.846 0.394 0.016* 0.213

Mar: Marijuana; Met: Methamphetamine; SD: Standard Deviation;
Letters=significance lettering. If the significance lettering between 2 groups have common letters (e.g. b and bcd), the groups do not differ significantly.
*Significant at p<0.05 (F or χ2).
Smallest mean value.
Largest mean value.
§Highest significant rate.

The maternal measures of GA at enrolment, age, MUAC and education as found in 11 substance use groups are presented in Figures 1, 2, 3 and 4, respectively. The birth outcomes of GA at delivery, birthweight and BWZ in the different substance use groups are shown in Figures 5, 6 and 7, respectively. The 1-year visit outcomes of 1YW, 1YL and 1YHC in the different substance use groups are shown in Figures 8, 9 and 10, respectively.

Table 4 summarises the multiple regression results for BWZ. There was a positive association between BWZ and MUAC for all the groups that did not use drugs. The strongest associations were in the Control and the NoDrugsDrink groups, which also had the largest MUACs. BWZ was positively associated with education in only two groups, NoDrugsDrink and MarDrink, and these two groups also had the highest education. BWZ was negatively associated with income in the MetDrink group only. In this group, a higher income was associated with a lower BWZ, whereas a lower income was associated with a higher BWZ.

Table 4: Multiple regression summary for birth outcome variable birthweight z-score

Substance use group

n MUAC Education Income
 bz p-value  bz p-value  bz

p-value

Control

646

0.31 <0.001* -0.00 0.929 0.04 0.272

NoDrugsDrink

596 0.26 <0.001* 0.10 0.021* -0.06

0.151

NoDrugsSmoke

590

0.18 <0.001* 0.01 0.898 0.02 0.664

NoDrugsDrinkSmoke

1 208 0.16 <0.001* 0.05 0.072 0.04

0.152

MarSmoke

41

0.29 0.090 -0.07 0.683 0.15 0.392

MetSmoke

16 0.45 0.244 -0.30 0.354 -0.28

0.445

MarDrink

11

-0.17 0.614 1.04 0.046* -0.66 0.214

MetDrink

7 -0.05 0.865 0.62 0.058 -0.98

0.043*

MarDrinkSmoke

154

0.10 0.221 -0.02 0.788 0.04 0.683

MetDrinkSmoke

56 0.18 0.182 0.09 0.505 0.16

0.239

All

50

0.16 0.291 0.03 0.856 -0.04

0.830

MUAC: Mid-Upper Arm Circumference; Mar: Marijuana; Met: Methamphetamine.
*Significant at p<0.05; bz: Standardized Regression Coefficient.

Table 5 summarises the multiple regression results for 1YW. There was a positive association between infant weight at 1 year and MUAC for all the groups that did not use drugs, apart from the All group. Mothers in the All group had 4th-lowest MUAC, that was associated with the 3rd-lowest weight at 1 year. There was also a positive association between 1-year weight of infant and education of mother for the Control, NoDrugsDrink, NoDrugsSmoke, NoDrugsDrinkSmoke, MarDrinkSmoke and MetDrinkSmoke groups. There was a positive association between 1-year weight of infant and income of mother in the NoDrugsDrinkSmoke group. These mothers earned the 5th-highest income and had infants with the 3rd-largest weights at 1 year.

Table 5: Multiple regression summary for outcome variable infant weight at 1 year

Substance use group

n MUAC Education Income
bz p-value bz p-value bz

p-value

Control

608

0.14 <0.001* 0.12 0.003* 0.02 0.668

NoDrugsDrink

550 0.13 0.003* 0.15 0.001* -0.00

0.916

NoDrugsSmoke

538

0.11 0.013* 0.09 0.047* 0.09 0.060

NoDrugsDrinkSmoke

1 109 0.14 <0.001* 0.12 <0.001* 0.11

<0.001*

MarSmoke

36

0.21 0.236 0.13 0.489 0.23 0.225

MetSmoke

14 0.56 0.178 -0.34 0.313 0.15

0.726

MarDrink

10

-0.40 0.347 0.73 0.184 -0.47 0.426

MetDrink

6 0.42 0.692 0.15 0.837 0.66

0.564

MarDrinkSmoke

145

0.08 0.319 0.21 0.015* 0.12 0.180

MetDrinkSmoke

48 0.08 0.573 0.42 0.003* 0.09

0.515

ALL

43

0.35 0.029* -0.07 0.707 0.06

0.718

MUAC: Mid-Upper Arm Circumference; Mar: marijuana; Met: Methamphetamine.
*Significant at p<0.05; bz: Standardized Regression Coefficient.

Table 6 summarises the multiple regression results for 1YL. A positive association between infant length at 1 year and MUAC was only found for the Control and NoDrugsSmoke groups. The Control group had the largest MUACs, which was associated with the tallest infants at 1 year, whereas the NoDrugsSmoke group had significantly smaller MUACs and significantly shorter infants at 1 year when compared with the Control group. Infant length at 1 year was also positively associated with education of mothers in the Control, NoDrugsDrink, NoDrugsSmoke, NoDrugsDrinkSmoke and MetDrinkSmoke groups. Education was highest in the Control and NoDrugsDrink groups, with the tallest infants at 1 year, and lowest in smoking plus drug use groups, and these infants were also significantly shorter at 1 year, as seen in the MetDrinkSmoke group. There was a positive association between 1-year length of infant and income of mother for the NoDrugsSmoke and NoDrugsDrinkSmoke groups. Those who had a higher income in these groups had taller infants at 1 year.

Table 6: Multiple regression summary of outcome variable infant length at 1 year

Substance use group

n MUAC Education Income
bz p-value bz p-value bz

p-value

Control

592

0.13 0.001* 0.10 0.014* 0.02 0.557

NoDrugsDrink

545

0.06

0.144 0.18 <0.001* 0.07

0.095

NoDrugsSmoke

522

0.09 0.045* 0.10 0.035* 0.12 0.011*

NoDrugsDrinkSmoke

1 089 0.05 0.104 0.14 <0.001* 0.14

<0.001*

MarSmoke

35

-0.05 0.765 0.33 0.073 0.24 0.193

MetSmoke

14 0.44 0.313 -0.44 0.223 0.16

0.721

MarDrink

10

-0.24 0.582 0.78 0.181 -0.51 0.424

MetDrink

6 0.37 0.742 0.34 0.670 0.08

0.943

MarDrinkSmoke

143

0.08 0.362 0.16 0.058 0.16 0.075

MetDrinkSmoke

47 0.21 0.096 0.45 0.001* 0.18

0.159

All

42

0.19 0.233 0.29 0.113 -0.05

0.786

MUAC: Mid-Upper Arm Circumference; Mar: Marijuana; Met: Methamphetamine.
*Significant at p<0.05; bz: Standardized Regression Coefficient.

Table 7 summarises the multiple regression results for 1YHC. Infant head circumference at 1 year was not associated with MUAC, but was positively associated with maternal education for the Control, NoDrugsDrink, NoDrugsDrinkSmoke, MarDrinkSmoke and MetDrinkSmoke groups. Higher education was associated with larger head circumferences and lower education was associated with smaller head circumferences in these groups. In the NoDrugsDrinkSmoke group, head circumference of the infant at 1 year was positively associated with income. Those who had a higher income in this group also had infants with larger head circumference at 1 year.

Table 7: Multiple regression summary for infant head circumference outcome at 1 year

Substance use group

n

MUAC Education Income

bz

p-value

bz

p-value

bz

p-value

Control

604

0.07 0.077 0.10 0.013* 0.04 0.378

NoDrugsDrink

549 0.01 0.730 0.13 0.003* -0.01

0.823

NoDrugsSmoke

536

0.00 0.930 0.08 0.090 0.09 0.060

NoDrugsDrinkSmoke

1 105 0.03 0.391 0.11 0.001* 0.08

0.015*

MarSmoke

37

0.12 0.519 0.15 0.438 -0.11 0.576

MetSmoke

15 0.45 0.304 -0.30 0.391 -0.25

0.580

MarDrink

10

-0.01 0.977 0.80 0.181 -0.44 0.498

MetDrink

6 0.75 0.278 0.50 0.303 0.93

0.223

MarDrinkSmoke

145

-0.02 0.775 0.23 0.007* 0.07 0.404

MetDrinkSmoke

47 0.03 0.823 0.66 <0.001* 0.14

0.201

All

43

0.28 0.089 -0.05 0.801 -0.11

0.563

MUAC: Mid-Upper Arm Circumference; Mar: Marijuana; Met: Methamphetamine.
*Significant at p<0.05; bz: Standardized Regression Coefficient.

Discussion

Maternal Measures and Trends

We found a significant trend in the GA at enrolment, when women booked for antenatal care, from the earliest GA in women who took no substances to a later GA in those who used all substances, but the MetSmoke and MetDrink groups enrolled even later (Figure 1). The finding of McCalla et al. [36] that, although recreational drug users had a wide range of social problems that compromised fetal growth and development and were in greater need of prenatal care, they were less likely to make use of antenatal care services, supports our finding.

FIG 1

Figure 1: Gestational age at enrolment compared among different substance groups

There was also a trend in maternal age (Figure 2), with the oldest women in the Control group to the youngest in the All group, except for the MarSmoke, MarDrink and MarDrinkSmoke groups. Women who used marijuana were the youngest. Our finding that marijuana users are young is in agreement with other researchers [3,13,14].

FIG 2

Figure 2: Maternal age compared among different substance groups

The trend in MUAC (Figure 3), from no substance users to users of all substances, was significantly smaller MUACs, but MUACs were even smaller in the MarSmoke, MetSmoke and MarDrinkSmoke groups. Our finding that women who smoked, whether combined with drugs, alcohol or not, had significantly smaller MUACs, has been confirmed by two previous studies [26,35]. The reduced MUAC, associated with cigarette smoking and indicating poorer nutritional status, was associated with an increased risk of spontaneous preterm birth as well as a lower infant BWZ [26,35].

FIG 3

Figure 3: Maternal arm circumference compared among different substance groups

The trend in education (Figure 4) and income (Table 2) from Control to All was lower education and lower income with more substances used. Women who smoked, in any combination, all had significantly lower education when compared with the Control group or drinkers only. Numerous studies that have reported on the association of cigarette smoking with a lower level of education [37-41] and income [39-43] support our finding. Compared with the women in the Control group, women in the NoDrugsDrink group had a higher education, and women who drank combined with marijuana or methamphetamine, but did not smoke, did not differ significantly. Woman in the NoDrugsDrink, MarDrink and Control groups had the highest mean education, ranging from 10.5 to 10.8 years. This finding is validated by research by Patrick et al., [40] who reported that young adults with the highest family education and income were most prone to alcohol and marijuana use, and by Rees [44], who found little evidence that drinking affected educational attainment.

FIG 4

Figure 4: Education compared among different substance groups

Birth Outcomes and Trends

Gestation at delivery declined as the number of substances increased, although this did not apply to alcohol use alone. Compared with the Control group, GA at delivery was significantly lower for methamphetamine users and for smoking on its own or in combination with marijuana, while it was significantly higher for the NoDrugsDrink group (Figure 5), with the highest mean GA of 39 weeks and 2 days. There was no significant difference between the Control and NoDrugsDrinkSmoke, MarDrink or MarDrinkSmoke groups. Our previous study also found that alcohol use alone was associated with a higher GA, while alcohol seemed to counteract the negative association of smoking with GA [26], and lends support to our findings. The highest significant difference in GA was found when we compared the NoDrugsDrink group (highest GA) with the MetDrink group (lowest GA). This suggests a combined effect of methamphetamine and alcohol on GA. Not only did the MetDrink group have the most preterm births, but it also had the highest significant rate of stillbirths, despite being such a small group. Our results endorse the findings by other researchers that methamphetamine was associated with a lower GA at birth [9,45-47] and with preterm birth [46-48]. However, according to England et al., [49] little is known about the co-use of other substances by women who drink during pregnancy. It appears that the combined effect of methamphetamine and alcohol on GA has not been reported previously. It is interesting that Sowell et al. [50] found that brain morphology was affected in children with prenatal methamphetamine and alcohol exposure above and beyond the effects of alcohol exposure alone, suggesting a synergistic effect between methamphetamine and alcohol.

FIG 5

Figure 5: Gestational age at delivery compared among different substance groups

The trend in birthweight from Control to All was lower birthweight with more substances used (Figure 6). Okah et al. [27] reported that women with alcohol and/or drug use during pregnancy did not appear to be at greater risk of giving birth to a term LBW infant than women who reported abstinence. However, the addition of smoking to either behaviour produces placental vasoconstriction that will decrease oxygen delivery to the fetus, limit fetal growth [51], and increase the risk of LBW by 2-to 4-fold. Gibson et al. [52] found that infants born to smokers had lower birthweights and were more prone to GR. These reports support our findings of significantly more infants with LBW in the smoking groups (NoDrugsSmoke, NoDrugsDrinkSmoke, MarDrinkSmoke and All) and of the non-smoking groups (all but one) being the only groups with a mean birthweight >3 000 g (Table 3). The MetDrink group, being the exception, had the lowest mean birthweight and also the lowest mean GA at delivery (<37 weeks), with 45.5% of infants being preterm. Many researchers have found that methamphetamine was associated with lower birthweight [10,45,53,54], and Black et al. [55] found antenatal drug use to increase the risk of LBW infants above that related to cigarette smoking. Odendaal et al. [56] and Jackson et al. [57] reported that the combined use of cigarettes and alcohol during pregnancy had a synergistic effect for LBW and GR, which also concurs with our findings.

FIG 6

Figure 6: Birthweight compared among different substance groups

The trend in BWZ from Control to All was lower BWZs with more substances used. The lowest BWZs were associated with marijuana and smoking, but not methamphetamine (Figure 7). Significant GR was detected in the infants from the smoking groups (NoDrugsSmoke, NoDrugsDrinkSmoke, MarDrinkSmoke, and All), with >25% of the MarDrinkSmoke group being affected. El Marroun et al. [58] reported that marijuana use during pregnancy resulted in more pronounced GR than tobacco use, while Sturrock et al. [59] also found that cigarette smoking was associated with a lower BWZ, but that women who both smoked and used marijuana during pregnancy had infants with a lower BWZ than those who used cigarettes alone. Spinillo et al. [60] reported on fetal GR among women who smoked throughout pregnancy, while Hayatbakhsh et al. [61], after controlling for smoking, alcohol consumption and other drugs, showed that marijuana use in pregnancy was associated with SGA infants with lower BWZs. The abovementioned researchers all validate our findings.

FIG 7

Figure 7: Birthweight Z-score compared among different substance groups

One-year Outcomes and Trends

The trend in infant weight from Control to All was lower infant weight at 1 year with more substances used (Figure 8). The lowest weights were in the methamphetamine-using groups, especially the MetDrink group, which had the lowest mean weight, with the most preterm births and infant ages adjusted for prematurity, and the MetSmoke group. In previous studies, weight and growth were reported as significantly decreased in methamphetamine-exposed children at ages 1-4 years [54,62,63], which endorses our results.

FIG 8

Figure 8: Infant one-year weight compared among different substance groups

The trend in infant length from Control to All was shorter infant length at 1 year with more substances used (Figure 9). Smoking only, or smoking combined with drugs and/or alcohol, was associated with significantly shorter infants at 1 year. Many studies have shown a long-term negative effect of maternal smoking during pregnancy on height of infants, from birth to adolescence [64-70], which supports our finding. Zabaneh et al. [71], Smith et al. [63] and Eriksson et al. [62] reported decreased height velocity throughout the first 3 years of life in methamphetamine-exposed children, corroborating our findings that infants from the MetDrink and MetSmoke groups, although adjusted for prematurity, had the shortest and second-shortest mean length at 1 year, respectively (Table 3).

FIG 9

Figure 9: Infant one-year length compared among different substance groups

The trend in infant head circumference from Control to All was a smaller infant head circumference at 1 year with more substances used. The smallest head circumferences were in the MetDrink group, despite adjustment for prematurity (Figure 10). Other researchers have found that infants prenatally exposed to methamphetamine tended to show a significantly smaller head circumference at birth or 1 year [54,62,72,73], supporting our findings.

FIG 10

Figure 10: Infant one-year head circumference compared among different substance groups

Effects of Combined Drug Use, Smoking and Drinking on Maternal Measures, Birth and 1-year Outcome

Many significant differences were found when the MarDrinkSmoke and MetDrinkSmoke groups, who used three substances, were compared with the Control group. Women using three substances (methamphetamine or marijuana with smoking and drinking) were younger, had a smaller MUAC, lower education and smaller income, and had infants with lower birthweight, 1-year weight and 1-year height than those from the Control group. These results are supported by the findings of other researchers [2,13]. Although polysubstance use in pregnancy is common [74], there is little information available, and the full range of substance combinations and their health impacts remain incompletely understood [75]. Alcohol, tobacco and drug co-use during pregnancy is particularly problematic and compounds the adverse effects on fetal growth [55,75,76].

Women in the methamphetamine three-substance (MetDrinkSmoke) group enrolled much later and had a lower GA at birth than Controls. They were also older than marijuana users but younger than abstainers. Smith et al. [2] found that infants exposed to methamphetamine or tobacco during pregnancy were 3.5 times or 2 times more likely, respectively, to be SGA compared with unexposed infants, suggesting more GR if the infant was exposed to methamphetamine and smoking. GR together with our finding of lower GA in the MetDrinkSmoke group (17% preterm births, which was second highest after the 45.5% in the MetDrink group) supports the association of methamphetamine with preterm birth.

Women in the marijuana three-substance (MarDrinkSmoke) group were much younger (also younger than methamphetamine users), had lower gravidity, were significantly more anaemic, had infants with a lower BWZ and smaller head circumference, and had more LBW and SGA infants when compared with the Control group. Interestingly, Chabarria et al. [13] and Grzeskowiak et al. [77] reported decreased head circumference at birth to be associated with maternal marijuana use combined with smoking, or independent of tobacco use, respectively. This may help explain the association found between MarDrinkSmoke and smaller head circumference of infants at 1 year in our study. Although we agree with others that marijuana use in pregnancy is harmful to the fetus in that it was associated with low infant birthweight [3,13,77] and SGA infants [14,78,79], our findings support those of Conner et al. [12] and Forray et al. [74], who reported that the association between maternal marijuana use and adverse outcomes appears to be attributable to comorbid substance use. Our findings are consistent with many reports of marijuana users being younger [75], of lower parity, better educated, and more likely to use alcohol, cigarettes and hard drugs [3,13,14]. However, we found no direct association between marijuana use and spontaneous preterm birth, as others have reported [13,14].

Confounders

Our finding that a larger MUAC, indicative of better nutritional status, was associated with a higher BWZ was supported by Smith et al. [2], who found that lower maternal weight gain during pregnancy was more likely to result in an SGA infant. A larger MUAC was also associated with a taller, heavier infant at 1 year.

Higher education was positively associated with outcomes at birth (BWZ) and all outcomes at 1 year, resulting in a larger infant who weighed more, was taller and had a larger head circumference. Numerous researchers have reported a strong inverse relationship between education and cigarette smoking [37-41,80] and drug use [81,82]. By decreasing substance use, academic outcomes may improve, and therefore also birth and 1-year outcomes.

Higher income was associated with a lower BWZ, perhaps suggesting more methamphetamine and alcohol use while pregnant, but was also associated with a larger infant at 1 year who weighed more, was taller and had a larger head circumference.

Study Strengths and Limitations

SPS was a unique, large study performed in population groups with similar socioeconomic circumstances and known to have a high incidence of antenatal substance use. A wealth of maternal, fetal and infant data were collected prospectively over a 9-year period. Substance use exposure data were collected on up to four occasions throughout pregnancy, and infant assessments were done at up to three time points throughout the first year of life. All measurements were taken twice, and we used validated recognised instruments and adjusted 1-year infant age for prematurity.

Limitations include that despite this being a large study with a high incidence of substance use, the small numbers in certain substance use groups limit the strength of the findings. Substance use was self-reported and may therefore be under-reported. Although we have detailed smoking and drinking exposure continuous data, drug information was not quantified, limiting us to nominal (yes or no) data for the various substances used.

Conclusion

The tragedy of substance use during pregnancy not only affects maternal and fetal health during pregnancy, but also infant growth and wellbeing at 1 year of age. Given that these substances are modifiable risk factors [28], and that detailed information on the preventable adverse effects of smoking and drinking during pregnancy was not effective in the population studied [83], it is clearly a major public health problem. The co-use of methamphetamine and alcohol (smallest group) seemed to have a confounding negative association with infant birth and 1-year outcomes, but reasons for this remain unknown. The addictive properties of substance use make cessation difficult, so prevention strategies should rather be addressed. As the prevalence of tobacco use among 13-15-year-old females in SA was 20% in 2002 [21], prevention strategies should be implemented long before pregnancy in order to limit the uptake of addictive substance use among young women. Higher maternal education, associated with better infant outcomes at birth and 1 year and acting as a countermeasure to substance use, is of paramount importance.

Acknowledgements

We wish to thank the South African Journal for permission to publish this manuscript (S Afr Med J 2022; 112(8) 526-538). We thank the personnel of the SA arm of the SPS for their outstanding work, which included the recruitment of 7 060 pregnant women and the collection and capturing of valuable information at up to seven assessment time points per participant.

Author Contributions

Concept and Design: LB, HO; Acquisition, Statistical analysis, or Interpretation of data: LB, PS, DN, MP, HO; Drafting of the manuscript: LB, HO; Editing, Revising, or Proofreading of manuscript: LB, PS, DN, MP, HO. Authors have nothing to declare.

Funding

The study was funded by the National Institute on Alcohol Abuse and Alcoholism, Eunice Kennedy Shriver National Institute of Child Health and Human Development, and National Institute on Deafness and Other Communication Disorders (ref. nos U01 HD055154, U01 HD045935, U01 HD055155, U01 HD045991 and U01 AA016501. The funding body had no role in conducting the research or writing the article.

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The Function of Supercritical Fluids for the Solvus Formation and Enrichment of Critical Elements

DOI: 10.31038/GEMS.2023582

Abstract

The opening of a solvus curve around their critical point starts with a singularity. At this singularity, an element distribution forms, described by a Lorentzian-type curve, showing the highest concentration of some trace and primary elements at the critical point, corresponding to the most elevated temperatures, and with cooling, this Lorentzian curve opens and snuggles up to the solvus curve. This combination is strong proof of supercritical transition to critical and under-critical conditions. Furthermore, we show here that remnants of an older mineralization are present, demonstrating that the current picture must not be correct. Supercritical fluids can have a significant influence on mineralization as a whole.

Keywords

Supercritical fluids, Element enrichment, Solvus- and Lorentzian-curves, Tin deposit Ehrenfriedersdorf

Introduction

Since the beginning of the studies on melt inclusion in pegmatite quartz from the tin deposit Ehrenfriedersdorf, particularly on the pegmatites from the Sauberg mine, in the years around the turn of the century, we often found very water-rich melt inclusions. Water content and temperature appear to be a characteristic relationship from the beginning: a characteristic solvus curve [1]. After that, such curves were also found for many other pegmatites and evolved granites worldwide (Figure 1) [2].

fig 1

Figure 1: Pseudo-binary solvus curve for 19 different evolved granites and pegmatites worldwide. Note: each point represents the arithmetic mean of measurement on up to 100 melt inclusions. Values at T/TC (in °C)=1.0 correspond to the solvus curve’s critical point. The abscissa (analytically determined water content) first approximates the melt density. Note here that the point scattering is, in a first approximation, the result of the complex interaction of volatiles (H2O, F, H3BO3), which sometimes work additively.

For the origin of such curves, a clear answer could not given. At this time, the necessary analytical technique was still in it’s infancy-however, the evidence of the characteristic relationship between water content and temperature increases significantly year to year. Applying reduced parameters (T/TC) displays relatively good comparability for different granite and pegmatite systems (Figure 1) see also Figure 2 in Thomas and Davidson, 2015) [3]. So, a more universal relationship is probably. However, demonstrating such curves was the first step to solving this puzzle because a deeper origin is behind the solvus curves (temperature versus water concentration). Here, we explicitly use the water concentration because the density decreases and is not steady with the increase in the melt’s water content (especially at or near the solvus crest). We have shown [4] that at the solvus crest, the concentration of volatiles can obtain extreme values far away from the solvus crest water concentration (including F, B, C, P, and high concentrations of alkali elements, Be, Sn, and others.

fig 2

Figure 2: Lorentzian distribution of P versus H2O concentration of water-rich melt inclusions in pegmatite quartz from the Sauberg mine, Ehrenfriedersdorf. Note: each point represents the data mean on 5 to 10 melt inclusions.

Key Observations

Starting with the work on the enrichment of Be in granite-pegmatite systems in 2011 [5], it would, from case to case, be evident that the highest element enrichment is related to the solvus crest-the start point for the opening the solvus with the drop in temperature. According to our findings of diagnostic HP-HT minerals from mantle depths transported by supercritical fluids, it would be more evident that the solvus crest is the locus where the supercritical fluid changes into the critical/under critical state. Therefore, here, we mainly found the highest enrichment of the most scarce elements (Be, Cs, Zn, Sn, and many others). To our surprise, the strongly enriched trace and main elements show a characteristic Lorentzian distribution related to the water concentration, with the maximum corresponding to the solvus crest [2]. Some elements show a Gaussian distribution caused by overlapping two or more Lorentzian curves of different anion species. Mathematically speaking, the critical point here is a physicochemical singularity. During the transition from supercritical to critical and hydrothermal conditions, unusual processes are far from equilibrium, such as moissanite and beryl’s synchronous growth [6]. Typically, for technical processes, moissanite grows at temperatures above 1000°C. Also, typical rare elements in granitic systems show extreme enrichment in part. One such process is the excessive element enrichment, such as zinc [7]: 75.000 to 85.000 ppm Zn in a fluid inclusion trapped near the solvus crest with a water concentration of about 30%. Table 1 shows the extraordinarily enriched elements and their relationship to the Clarke values.

Table 1: Extreme trace element enrichment in syngenetic fluid inclusions trapped at or near the solvus crest [7].

Element (in ppm)

Melt inclusion 1 Melt inclusion 2 Clarke (ppm) Enrichment

Rb

12.300 15.700 200

61.5-78.5

Cs

22.700

20.000 5 4540-4000

Zn

80.000 45.000 50

1600-900

Cd

200

580 0.1 2000-5800

Sn

240 2.200 3

80-733

Sb

900

740 0.26 3462-2846

Pb

1.790 1.430 20

89.5-71.5

Clarke, according to Rösler and Lange (1975) [8]

At room temperature, Zn is, according to Raman spectroscopy, present as a well-transportable potassium tetrachlorocomplex: K2[ZnCl4]. Another unusual observation at the beginning of our research was the finding of extreme P-rich melt inclusions in pegmatite quartz Qu8 with 50.7 ± 3.5% P2O5 [8,9]. Because the phosphorus here was easily water-soluble, we were cautious during sample cleaning before any analytical studies. Later, we found melt inclusion in pegmatite quartz from the Sauberg mine with high P2O5 concentrations. Near the solvus crest, phosphorus shows strong enrichment in the form of the Lorentzian distribution (Figure 2). That is significantly higher than London (1998) [10] showed for P-rich peraluminous granites. This author interpreted higher P values as local build-up in boundary layers. Here, we tell another story: Enrichment of P by the transition of supercritical fluids into undercritical fluids during the interaction of the first one with the granites. Similar results arise also for the sulfate-anion and other elements.

In different publications, the authors [2,4,11] have shown that many trace and main elements are Lorentzian distributed. The proof was not straightforward because many pieces of evidence are nearly invisible because of the extreme postmagmatic hydrothermal intensity in the region, which blurs the primary processes. The remnants of strongly peralkaline mineralization prove such changes. The first author found strong peralkaline melts in the Ehrenfriedersdorf pegmatite Qu8, indicated by nepheline in quartz (Figure 3) [12]. That is an extraordinary observation because, usually, nepheline and quartz exclude themselves.

fig 3

Figure 3: Nepheline in pegmatite quartz (Qu8) from Ehrenfriedersdorf, Sauberg mine, Germany. The nepheline is composed of nepheline [(Na, K)AlSiO4] and kalsilite [KAlSiO4]. Between the nepheline and quartz is a tiny K-feldspar rim.

Table 2 gives the microprobe results on nepheline and feldspar. The nepheline aggregate (Figure 3) resembles a corroded crystal according to the form and feldspar rim. However, most nepheline crystals in this sample are in K-feldspar. That means that the nepheline points to very different rock chemistry before the reworking of the whole deposit by intensive hydrothermal activity. Non-consideration can lead to entirely wrong conclusions. Figure 4 shows another nepheline crystal in K-feldspar.

Table 2: Shows the composition of nepheline and K-feldspar by EMP analyses

Nepheline

K-feldspar-rim

SiO2

45.8 ± 1.4

68.5

Al2O3

31.8 ± 0.4

16.6

FeO

 0.3 ± 0.1

 0.4

Na2O

17.1 ± 0.1

 3.2

K2O

 3.8 ± 0.3

 9.1

Rb2O

 0.2 ± 0.04

 0.2

P2O5

~0.02

 0.02

Sum

99.02

98.0

n

10

1

XNe

0.86

XKls

0.14

XOr

0.65

XAb

0.35

fig 4

Figure 4: Nepheline (Ne) and Kalsilite (Kls) in K-feldspar (Kfs) in pegmatite Qu8. Quartz (Qtz) is secondary.

After re-homogenization, in the same sample are larger aggregates of silicate glass (~ 5mm) with a very low ASI (aluminum saturation index): ASI = 0.418 ± 0.009. The origin of this rock (now glass) is unclear. In the glass are small remnants of albite, K-feldspar, and nepheline. Table 3 shows the composition of this glass, determined with the microprobe SX50.

Maybe this rock (glass) is the reaction product of the supercritical fluid with the granitic rock in the reaction room (in the crust). At this time, maybe till 2012, the sporadically found high concentration of some elements (also tin) was explained more by chance (see also the discussion by London and Evensen 2002) [13]. We have had no explanation for the nepheline and glass with the low ASI values. However, from then on, we found indications of regularities in the appearance of many trace elements. A prerequisite for this systematic study was the water determination of the melt inclusion in question. Surprising was the Lorentzian distribution of about 20 trace elements with the water content of the corresponding melt inclusions [2].

Table 3: Composition of the re-homogenized silicate glass (700°C, 3.0 kbar) in the sample with nepheline in quartz and K-feldspar.

 

Mean

± 1σ

SiO2

71.39

0.25

TiO2

~0.01

<0.02

Al2O3

7.17

0.13

P2O5

0.05

0.02

FeO

0.21

0.04

MnO

<0.03

CaO

0.00

MgO

<0.01

Na2O

7.64

0.10

K2O

4.14

0.04

Rb2O

0.15

0.01

Cs2O

<0.04

F

0.00

Cl

<0.01

H2O

9.00

0.25

Sum

99.85

ASI

0.42

0.02

Mean from 72 determinations, σ: Standard deviation, ASI: Aluminum Saturation Index, water is the difference to 100% and corresponds to the Raman spectrometric determined water content.

Interpretation

The combination of the pseudo-binary melt-water solvus with the extreme element enrichment in the shape of the typical Lorentzian distribution is, together with the HP-HT mineral relicts (diamond, graphite, moissanite, reidite, coesite, and others), a solid-proof of the interaction of supercritical fluids coming from mantle depths with rocks in the upper crust. Furthermore, the extraordinary element parageneses in the Ehrenfriedersdorf case imply an older subducted deposit in the mantle region already postulated by Schütze et al. (1983) [14] according to isotope, element-geochemical and radio-geochronological studies.

Our latest studies, which brought unambiguous proofs of the interaction between supercritical fluids coming from mantle depths and granitic rocks in the upper crust, the following points are essential:

  1. The most spherical crystals, often tiny, of diamond, graphite, moissanite, and others in crustal minerals prove that a supercritical fluid transported these crystals very fast from mantle depths to the crust.
  2. All such minerals are spherical, which means that during transport from the mantle to the crust, elements of these crystals go into the supercritical fluid by partial solution.
  3. The “chemical way” from mantle to crust differs from point to point. By that, the composition of the supercritical fluid is also not uniform.
  4. The transition from the supercritical state into the critical and under-critical state is related to the processes far from equilibrium.
  5. The fast-moving supercritical fluids are also not in an energetic equilibrium with the surroundings, bringing much water and energy into the crustal region.
  6. In this highly excited state, processes happen that are conventionally not feasible (moissanite whiskers grow in beryl at significantly lower temperatures and pressures.
  7. The decisive element enrichment in the Lorentzian distribution shows impressively that enrichment processes occur, which, under hydrothermal conditions, are impossible.

Discussion

Apart from our work, other colleagues have not found such correlations (a combination of natural solvus curves and Lorentzian element distributions). Here, we see a beautiful field of activity that can bring us completely new views into geological processes related to supercritical fluids. An excellent experimental starting point demonstrates [15]. For example, Zn is highly soluble in high-temperature fluids. Therefore, a longer transportway is possible. That means, not far from the Ehrenfriedersdorf tin deposit, there is also a Zn deposit possible. In reverse, a larger Sn deposit is also possible under the famous Pb-Zn deposit in the Freiberg area because we have found there spherical remnants of REE-rich tveitite-Y crystals [Ca14Y5F43], typical for evolved tin-granites from Zinnwald, E-Erzgebirge.

Acknowledgment

We thank the many people who contributed to the supercritical fluids work over the last 25 years. A special thanks go to James (Jim) D. Webster (1955-2019), who initiated the intense work on evolved granites and pegmatites.

References

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How does the Language Prediction Model, the ChatGPT Evaluate Negative Emotions?

DOI: 10.31038/JCRM.2023634

Summary

The Generative AI (Artificial Intelligence: AI), the ChatGPT (Generative Pretrained Transformer: ChatGPT), is a language prediction model that generates sentences based on word frequencies and interrelationships. In this study, we evaluated how the ChatGPT, a generative AI, performs in cognitive conflicts (conflicts) between healthcare professionals and patients/families encountered in healthcare settings, using dialogue transcripts of licensed medical mediations (with joint decision making), in which the ChatGPT is said to convey limitations and misinformation regarding negative emotions. We report the results of our study of the ChatGPT’s negative emotion evaluation, comparing it with human evaluations.

Abstract

We investigated that how the ChatGPT (Version 3.5), a generative AI, evaluated negative emotions in narratives of cognitive discrepancies (conflicts) between medical professionals and patients/families encountered in the medical field. As a result, negative emotion evaluation by the ChatGPT did not reach the level that people do. It can be inferred that there are limitations to negative emotion evaluation by the ChatGPT at this time.

Introduction

ChatGPT (Generative Pretrained Transformer: ChatGPT) is an artificial intelligence (AI), neural network-based language prediction. It is one of the models that generate sentences based on word frequencies and their interrelationships. This is said to cause so-called hallucination (hallucination), which is the conveyance of incorrect information, due to the limitations of human emotion processing that depends on context and situation [1,2]. Conflicts between medical professionals and patients/families encountered in the medical field are always accompanied by negative emotions. How does the ChatGPT, a generative AI, evaluate such negative emotions? There do not seem to be any evaluation reports on this issue. Therefore, we conducted a human evaluation of the ChatGPT’s verbal assessment of negative emotions using recorded dialogue data from past medical mediations (a concept with a dialogue process involving collaborative decision making [3] and investigated the rate of agreement.

Case Presentation

The purpose of the study was to determine whether “The ChatGPT (Version3.5), a generative AI, can capture negative emotions from dialogue narratives.”

The overall flow of the research methodology is shown in Figure 1. The evaluation period was from August 31, 2023 to September 30, 2023. The subjects were the Ethics Committee and the record language of the medical mediator of the first complaint claim submitted with the permission and consent of the patient’s family, among the previously resolved complaint cases, as shown in Table 1.

fig 1

Figure 1: Research Methods

Table 1: Complaint status of subject cases

Case

Patient narratives of situation content

1

The patient’s daughter and the outpatient attending physician talk about the delay in seeing the patient for inappropriate medical care.

2

Patient speaks up to health care provider about his dissatisfaction with the treating technician.

3

The head technician and nurse talk about the policy for dealing with problems between patients and technicians in the department in charge.

4

The patient’s son and daughter-in-law have doubts about the medical personnel’s handling of the sudden change.

5

This is a scene in which a patient and a medical professional are discussing a treatment plan for a nerve palsy that has appeared since the surgery.

6

A patient who was seen for abdominal pain is misdiagnosed initially and speaks with the corresponding physician.

7

A family member of a patient who has accidentally swallowed a partial denture is talking with a medical professional, a nurse.

8

Patient speaks of how nurses treated him during his hospitalization.

9

The patient is just after surgery. He discusses the response of the resident in charge of the patient.

10

A bereaved family member who lost a patient tells the story.

11

A bereaved family member who lost a patient suddenly told the story.

For the content of the ChatGPT instructions for the generated AI, prompts were created based on the Fukatsu-style questioning technique created by Fukatsu [4], as shown in Figure 2. For the constraints and emotions in the prompts, nine emotions [5] were used, including “joy,” “expectation,” “anger,” “disgust,” “sadness,” “surprise,” “fear,” “trust,” and “anxiety,” referring to Pulchick’s Emotion Circle, and responses were also required to provide reasons for their emotion evaluations. Responses were also asked for reasons for their own emotion ratings. The ChatGPT used for the evaluation was Version 3.5, which was set to not perform the learning function. The prompts were then entered and implemented on two PCs, and the interpersonal emotions of the narratives were estimated for three participants (an ethics mediator and two male and two female medical students in their 20s each, before they left for clinical practice).

fig 2

Figure 2: Instructions to the generating AI (ChatGPT)

For the evaluation of the generated AI ChatGPT emotion output, examples of output results are shown in Table 2; the output results of the 11 cases were individually evaluated by the three aforementioned prompt implementers using the published rating criteria [5,6] scale. The respective emotional evaluation items and evaluation methods are shown in Figure 3. The two evaluations were then aggregated to conduct an overall evaluation of ChatGPT’s emotion evaluation status [7].

Table 2: Generative AI (ChatGPT) answers to the narrative of Case 1

Narrative of a patient’s family

Answer 1

Answer 2

 

My father had been suffering from severe constipation for a long time. It took days from the time he was admitted to the hospital until he was

diagnosed, and in the end, the disease was found to be in such an advanced stage, but my father trusted the doctor like a god and had been coming to the hospital for a long time, but he was betrayed. I’m sure my father must be holding a grudge against him now.

#Emotions Emotions felt from the patient’s family narrative are “anger” and “sadness”.

#ReasonThe family of the patient is angry because their father was suffering from severe constipation. They express anger that the attending physician instructed them that over-the-counter laxatives were fine and did not understand their father’s suffering and did not take appropriate action. They are also surprised and saddened that the attending physician did not know that the patient’s wife was accompanying him. He also expresses disappointment and sadness that it took days to diagnose the disease and that it was discovered in an advanced state. He also feels disgust and sadness at the loss of trust in the doctor, as he did not receive proper medical care, despite the fact that his father had trusted the doctor and had visited him for a long time.

#Emotion: Anger Reason: The patient’s daughter is angry that her father has been suffering from severe constipation for a long time and her doctor did not provide proper medical care, only recommending over-the-counter laxatives.

She is also angry that the attending physician did not know that the patient’s wife was accompanying her and did not provide an

appropriate explanation, and regrets that she should have been more proactive.

He also attributes his anger to the fact that it took so long from the time he was admitted to the hospital until he was diagnosed, and that his disease had progressed.

My father trusted his doctor like a god, and the fact that his trust was

betrayed suggests that he may also be angry with his doctor.

fig 3

Figure 3: Evaluation items and methods for the emotional results of the generated AI (ChatGPT)

Results

Table 3 shows the aggregate results of the emotion judgments of the ChatGPT by the generated AI; the total percentage of 100% agreement and 75% agreement ranged from 63.6% to 75.0% for questions 1 through 5, respectively. The reason statements evaluated in Question 2 were quotes from the narrative and interpreted feelings. All of the responses that were interpreted as emotions were inconclusive. Next, the context of the emotional discrepancy was indicated as inadequate or excessive. The most common emotion perceived as excessive was anger. There were also many responses that listed only one emotion.

Table 3: Rater ratings of generated AI (ChatGPT) emotional output results

Question

1 2 3 4

5

100%

7(15.9) 6(13.6) 23(52.3) 22(55.0) 19(43.2)
75% 21(47.7) 22(50.0) 10(22.7) 6(13.6)

14(31.8)

50%

11(25.0) 11(25.0) 10(22.7) 10(22.7) 8(18.2)
25% 1(2.3) 1(2.3) 1(2.3) 5(11.4)

3(6.8)

0%

4(9.1) 4(9.1) 0(0.0) 1(2.3) 0(0.0)

Total number of responses

44(100.0) 44(100.0) 44(100.0) 44(100.0)

44(100.0)

1.       Is the sentiment consistent with the response regarding “feelings”?

2.       Is the reason for the response regarding “emotion” appropriate?

3.       Is the rationale vague in response to the “emotion” response?

4.       In response to the “emotion” response, is the emotion expressed in the supporting reasons consistent with the “emotion”?

5.       For the response regarding “emotion,” is the emotion expressed in the supporting reasons appropriate?

Next, the following characteristics of the responses were noted. First, (a) In the case of simple structures such as a single emotion, the emotion was appropriately captured. Second, (b) when emotions were mixed in a complex way, the rating of agreement decreased. Also, (c) in the case where the patient died, mixed responses were generated without distinguishing between past emotions and emotions at the time of the narrative. Furthermore, in the case of (d) where the interest (expression of interest, desire, and values: hereafter, interest) changed during the course of the narrative, the respondent responded to the emotion by addressing only the first half of the interest and ignoring the interest after the change.

Discussion

Table 3 shows that the total percentage of 100% agreement and 75% agreement ranged from 63.6% to 75.0%. 75% agreement was adopted because the three raters were two men and two women in their 20s with limited emotional and life experience and a medical mediator who had gained the patients’ trust and supported collaborative decision making during actual interviews with the patients. Since the hypothesis was that the results would “accurately capture negative emotions,” the 100% agreement between the emotions responded to by the ChatGPT and the emotions responded to by the raters was low (15.9% and 13.6%), if 100% agreement is considered “accurate” in the hypothesis, the 100% agreement between the emotions (Question 1) and their rationale (Question 2) was low (15.9% and 13.6%). The results showed that the AI was unable to accurately assess negative emotions in the items that must be given the most weight in human emotion assessment. This indicates that emotion evaluation based on language alone is limited or impossible, considering that humans evaluate the emotions of others by synthesizing the situation, context, and nonverbal messages and matching them with their own interests. The definition of accurate should have been clarified in order to refine the evaluation.

As shown in Figure 3, “anger” was frequently over-rated. We considered that this was caused by grasping only the final emotion, “anger,” and ignoring the primary emotion that caused the anger.

Next, for results (a)-(d), we considered that the emotions in the language of narration can be accurately taken, but not in the area of judging by context.

The following points are necessary to improve the agreement of ChatGPT’s emotion judgments with human evaluations. For example, parameters such as environment, atmosphere, facial expressions, and tone of voice, which are quasi-linguistic and non-linguistic. It is necessary to add these elements as linguistic information. The emotional evaluation of ChatGPT, a generative AI, was limited to the age of the evaluator and the number of evaluators. For more accurate evaluation, it is necessary to add parameters such as the age of the evaluators, the number of evaluators, and their expertise. It is also important to clarify the type of linguistic information.

Conclusion

The negative sentiment evaluation of the generative AI was only partially affirmed. The emotion evaluation of the ChatGPT of the generated AI based solely on linguistic information at the time of this study is limited. At present, it is difficult to accurately identify emotions in detail.

Conflicts of Interest

There are no corporate or other COI relationships that should be disclosed.

References

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Impact of the COVID-19 Pandemic on Breast Incidence and Stage during the COVID-19 Pandemic in the Netherlands, and a Comparison with Other Countries

DOI: 10.31038/AWHC.2023643

Short Report

In the Netherlands, the first COVID-19 patient was confirmed on February 27th, 2020. Thereafter, the number of infected patients quickly increased, just as the number of hospitalized COVID-19 patients. In March, 2020, the first policy measures were taken to prevent the spread of the virus. Those measures included, amongst others, the recommendation to keep 1.5 meters distances and for elderly and vulnerable people to stay at home. Additionally, the Dutch national breast cancer screening program was suspended in week 12 of 2020. In week 26 of 2020 it was resumed at 40% capacity, which was slowly increased to a capacity of 100% in the spring of 2021. These policy measures and decisions in healthcare were made without, or with little, prior knowledge of the consequences on breast cancer detection and care. Therefore, this short report aimed to give an overview of the effect of the COVID-19 pandemic on breast cancer incidence and stage in the Netherlands, and compared those results with other countries.

First results of the effect of the COVID-19 pandemic on breast cancer detection showed a decrease in breast cancer incidence (expressed as the number of breast cancer diagnoses per 100,000 women) in women diagnosed with breast cancer in weeks 2-17 of 2020, compared with women diagnosed in weeks 2-17 of 2018 or 2019. This effect was seen in all age groups and tumor stages, except stage IV [1-3]. As expected, the incidence of DCIS and stage I tumors decreased to the largest extent, as these are the tumors mainly detected by the breast cancer screening program.

When we focus on women in the screening age, 50-74 years, 67% fewer screen-detected tumors were diagnosed in weeks 9-35 of 2020, compared to week 9-35 of 2018/2019 [4]. The incidence of screen-detected tumors was significantly lower in all age groups during weeks 14-35 of 2020, and the incidence of all tumor stages, except stage IV, was significantly lower during weeks 14-25. During weeks 26-35 the incidence of DCIS and stage I-II tumors stayed significantly lower. Less pronounced effects were observed for the incidence of clinically-detected breast cancer. Compared to weeks 9-35 of 2018/2019, 7% fewer clinically-detected breast tumors were diagnosed in weeks 9-35 of 2020. The incidence decreased in all age groups in weeks 12-16. Incidence of stage I-II tumors was significantly lower in weeks 12-13 and incidence of DCIS and stage I-III tumors was significantly lower during weeks 14-16.

Follow-up research of our group investigated the effect of the pandemic on breast cancer incidence and stage during January 2020 till December 2021 [5]. This study showed that the incidence was significantly lower in women eligible as well as not eligible for screening (i.e., those aged <50 and >74 years). This suggests that the decrease in incidence was caused by both the suspension of the screening program and the reluctance of patients to visit the general practitioner. During the second wave, i.e., October 2020-April 2021, the incidence of clinically-detected tumors was significantly higher in Dutch women aged 50-74 years [5]. This suggests that the method of detection in some women changed from screen-detected to clinically-detected. Additionally, a small and temporary increase in the incidence of stage IV tumors was seen in Dutch women aged 50-69 years. However, it is unclear whether this increase is due to the COVID-19 pandemic or other factors. The increased incidence could also be a result of the increase in the usage of improved diagnostic methods, such as the PET-CT scan which is highly accurate in detecting distant metastases compared to conventional methods [6].

Comparable to our studies, studies from other countries also showed a decrease in both the absolute number of breast cancer patients [7-14] and in the crude breast cancer incidence rate [15,16] at the beginning of the pandemic. The largest decrease in breast cancer incidence was seen in women in the screening age groups [3,4,8,16]. Part of the decrease in breast cancer incidence can be explained by the suspension of the national breast cancer screening program. Many countries had to suspend their screening program to reduce the pressure on healthcare [17]. A previous meta-analysis showed a 41% decline in mammogram rates between 2019 to 2020 according to data of three registry-based studies, and a 53% decline based on data of ten non-registry-based studies [18]. Another part of the decrease in breast cancer incidence could be explained by a decline in the number of women visiting the General Practitioner (GP) due to fear of contracting the virus or overburding the healthcare system [1,2]. The decrease in the number of women visiting the GP in the Netherlands did not differ by age group [2].

Studies from Norway and New Zealand only showed a minimal decrease in the number of breast cancer diagnoses during the start of the pandemic [5,19]. Both Norway and New Zealand had a low COVID-19 infection rate and low COVID-19 death rates compared to other countries [20-25]. This indicates that the stable incidence in Norway and New Zealand could be due to the low severity of the pandemic, resulting in a minimal decrease of breast cancer diagnoses.

Comparable to our results, some studies from other countries showed that breast cancer incidence quite quickly reached pre-COVID levels after the first wave [7-9,14-16]. However, a couple of countries/regions had more difficulties in reaching pre-COVID incidence levels. These include Italy [10], Hungary [11], the United States [12], and Bavaria [13]. The level of political regulation and the number of COVID-19 infections or deaths were comparable between those four countries/regions and other countries [26-28]. Hence, this probably does not explain the difference in incidence. One reason for the decreased incidence in Italy could be that Italian women were still hesitant to visit screening after the end of the first wave [29]. A Italian study showed a 20% decrease in the number of women attending screening between October-December 2020, compared to the same period in 2019, while the number of women invited reached pre-COVID levels [29]. The decrease in incidence in Hungary might be explained by the relatively high number of COVID-19 patients in the hospitals, compared to other countries [30]. Also, the breast cancer screening program was suspended a second time in April 2021. The decrease in Bavaria (Germany) could have been caused by a relatively high number of patients at the Intensive Care Units (ICU) in Germany during the second wave, compared to other countries [31]. A negative association between the number of patients at the ICU and the diagnostic capacity at the oncological care was found in Germany [32]. The potential cause for the decrease in incidence found in the study from the United States is unknown. These cross-country comparisons show that the cause for the decline in incidence varies from country to country.

In the Netherlands, the maximum allowed screening interval between two invitations increased from two to three years in November 2020. The increase in the screening interval was both due to the COVID-19 pandemic and due to a shortage in mammography technologists. As a result, the mean screening interval was 32.2 months in 2021 [33]. This increased screening interval probably caused the method of detection in some women to change from screen-detected to clinically-detected, as a significant higher number of women were diagnosed with a clinically-detected cancer during October 2020-April 2021 compared to the same period in 2017-2019 [5]. A Dutch modelling study showed that a three-months suspension of the screening program, without catch-up, might already cause a 19% increase in the number of interval tumors detected between the last and first screening after interruption, compared to no suspension [34].

The majority of studies on tumor stage investigated whether the proportion of women diagnosed with a certain stage tumor changed during the pandemic [10,21-25]. However, as the suspension of the breast cancer screening program mainly led to a decrease in the incidence of DCIS and stage I tumors it was expected that a lower proportion of women would be diagnosed with these tumors, and that a higher proportion would be diagnosed with late-stage tumors. It would have given more insight if these studies investigated the effect of the pandemic on the incidence of breast cancer by tumor stage, as we did in our studies.

In our studies we did not adjust for the aging of the population or the increase in risk factors associated with breast cancer, while those factors might have led to an increase in the number of cancer patients. However, as the crude breast cancer incidence rate stayed rather constant in the Netherlands during the seven years before the pandemic (2013-2019) [35], and the study period used in our studies is relatively small, it is not expected that this influenced the results.

This report showed the effect of the COVID-19 pandemic on breast cancer incidence and tumor stage in the Netherlands, and a comparison with other countries. More studies on the effect of the COVID-19 pandemic on breast cancer incidence, both in total and per tumor stage, are needed to determine the association between delays in diagnosis and tumor stage.

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Impact of BRCA1 and BRCA2 Gene Mutations in Prostate Cancer in Thies

DOI: 10.31038/MGJ.2023611

Abstract

Prostate cancer (CaP) is a public health problem among men worldwide, particularly those over the age of 50, and its incidence continues to rise. Despite improvements in early detection methods, a large proportion of patients succumb to the disease. Studies have shown that men with BRCA1/BRCA2 gene mutations in prostate cancer are likely to have more severe disease and a poorer prognosis. A BRCA2 gene mutation is known to confer the highest risk of prostate cancer (8.6 times in men ≤ 65 years of age) while BRCA1 presents an increased risk, albeit to a lesser extent (3.5 times); making BRCA genes a conceivable genomic biomarker for prostate cancer risk. It is in this context that we will examine the impact of BRCA1/BRCA2 gene mutations in prostate cancer in Thiès. Our study is conducted between January 2020 and December 2022 with 59 patients diagnosed with a prostate tumor in the urology department of the Thiès regional hospital and the Saint Jean de Dieu hospital in Thiès. The variables studied were age, PSA levels, Gleason score and histological grades. Total DNA from prostate tissue was extracted using the Qiagen protocol (Qiagen Dneasy Tissue Kit) and the three primers for the BRCA1-185delAG, BRCA1-5382insC and BRCA2-6174delT genes were amplified. The results indicate a frequency of 62.71% of patients diagnosed with prostate cancer versus 37.29% with the lesion of benign prostatic hyperplasia. BRCA1-5382insC and BRCA2-6174delT mutations showed higher frequencies (2-3 fold) in patients with CaP than in those with the BPH lesion, with 62.7% vs. 37.3% and 65.1% vs. 34.9% respectively. Gleason score 8 was more represented with a rate of 44% corresponding to grade IV according to WHO-ISUP 2016. However, individuals carrying mutations (BRCA1-5382insC; BRCA2-6174DelT) could be associated with a higher risk of prostate cancer, and are also likely to have a poor survival rate.

Keywords

Prostate cancer; Mutations; BRCA1, BRCA2

Introduction

Prostate cancer (CaP) is the second most common cancer diagnosis in men (14.1%) and the fifth leading cause of death (6.8%) worldwide in 2020 [1]. Every year, Africa records around 1.1 million new cases of cancer and up to 700000 deaths from the disease [2]. Many men with prostate cancer are diagnosed by a biopsy and analysis of the prostate, a prostate specific antigen (PSA) test and a digital rectal examination. Risk factors for prostate cancer include family risk, ethnicity, age, obesity and other environmental factors [3]. Demographic expansion and improved life expectancy worldwide are expected to contribute to an increase in the number of cases of CaP [4]. making it a major global health problem. Prostate cancer is a heterogeneous disease, both epidemiologically and genetically. The interaction between genetics, environmental and social influences results in lower estimates of prostate cancer survival rates by race, which explains the differences observed in the epidemiology of prostate cancer in different countries [3]. There is documented evidence of a genetic contribution to prostate cancer. Hereditary prostate cancer and genetic predisposition to prostate cancer have been studied for years. One of the most predisposing genetic risk factors for prostate cancer is family inheritance. Twin studies and epidemiological studies have both demonstrated the role of heredity in CaP [5]. Many researchers have investigated the possible role of genetic variations in androgen biosynthesis and metabolism, as well as the role of androgens [6,7]. Genomics research has identified molecular processes that lead to certain cancerous developments, such as chromosomal rearrangements [3]. Although new treatments have emerged in the last decade, prostate cancer is still a major source of cancer deaths in men [8]. Advanced age is the main risk factor, with more than three-quarters of CaP detections made in men over 65 [9]. Prostate cancer susceptibility genes are genes involved in the androgen pathway and testosterone metabolism. The development of the prostate epithelium and prostate cancer cells depends on the androgen receptor and testosterone signalling pathway [10]. The identification of cancer biomarkers and the targeting of specific genetic mutations can be used for the targeted treatment of prostate cancer. Biomarkers that can be used for targeted therapy include tumour biomarkers, DNA biomarkers and general biomarkers [11]. Family history and genetic predisposition such as BRCA1/BRCA2 pathogenic variants have also been identified as important risk factors [12,13]. It is known that a mutation in the BRCA2 gene confers the highest risk of prostate cancer in men (8.6 times higher in men aged 65 years, while BRCA1 shows increased risk, although to a lesser extent (3.5 times) [14]. These genes have attracted much attention from researchers, but their role in the clinical assessment and treatment of prostate cancer remains complex. The aim of this study is to examine the impact of BRCA1/BRCA2 gene mutations in prostate cancer in Thiès.

Materials and Methods

This study covers 59 patients with prostate tumours. These patients were recruited from the urology department of the Thiès regional hospital and the Saint Jean de Dieu hospital in Thiès between January 2021 and December 2022. Inclusion criteria were a suspicious digital rectal examination (DRE) with a PSA level greater than 4 ng/ml, followed by biopsies for histopathological diagnosis. After review in accordance with the rules laid down by Senegal’s National Health Research Ethics Committee (SNHREC) and in compliance with the procedures established by Cheikh Anta Diop University in Dakar (UCAD) for all research involving human participants, ethical approval was obtained for this study. The objectives of the study, the protocol, the benefits and the confidentiality criteria were explained to each patient to give them the opportunity to accept or refuse to take part. In the case of acceptance, a duly completed and signed informed consent form was required for admission to the study. For data collection, we collected demographic data (surname, first name, age, ethnicity, reason for consultation), PSA levels and medical history from routine family files.

DNA Extraction and Amplification of the BRCA1 and BRCA2 Genes

Total DNA from each sample was extracted using the Qiagen protocol (Qiagen Dneasy Tissue Kit). DNA quality was checked by electrophoretic migration on a 1.5% agarose gel. For a given gene, the conditions for DNA amplification are the same whatever the pathology and for both tumour tissues and controls. PCR amplification conditions included a 1st step of a 12 minute of initial denaturation at a temperature of 95°C, followed by a 2nd step consisting of 35 cycles of 15 seconds of denaturation and hybridization at 94°C and 57°C respectively, primer elongation at 72°C/1 minute, and a 3rd step: final elongation or polymerization at 72°C for 5 min. PCR products were checked by electrophoretic migration on 1.5% agarose gel from 5 μl of amplicons. The size of each amplified gene was estimated using a 500 bp SmartLadder size marker.

The primer sequences and corresponding amplicon sizes are shown in Table 1.

Three founder mutations in the BRCA1 and BRCA2 genes were identified for PCR: 185delAG in exon 2 and 5382insC in exon 20 of the BRCA1 gene, and 6174delT in exon 11 of the BRCA2 gene [15-17]. Germline mutations in the BRCA1 and BRCA2 genes have been reported in several studies of different ethnic populations [16,18,19]. For each mutation, three primers (one common, one specific for the mutant and one specific for the wild-type allele) were used. The competing mutant and wild-type primers were designed to differ in size by 20 bp, allowing easy detection of the PCR products by routine electrophoresis. Both the mutant (long) and wild-type (short) primers contain a mismatched base sequence near the 3′ end. The long (mutant) primer also incorporates two additional mismatched bases at two contiguous positions corresponding to the 5′ end of the short (wild-type) primer. During the final cycles of the PCR reaction, heteroduplexes can be formed from the short and long products, but the contiguous mutagenic sequences in the long product prevent the short product from being filled in using the long strand as a template. If a mutation is present in one of the alleles, two bands will be present. PCR conditions were optimised for each primer pair and applied uniformly to all samples. Amplifications were performed in a reaction volume of 25 μl. The composition of the reaction mixture is given in Table 2.

Table 1: Primers used

Primers

Primers sequences Amplicon size

BRCA1-del185AG

Foward  5′ggttggcagcaatatgtgaa 3′
Reverse wild 5′gctgacttaccagatgggactctc 3′ 335pb
Reverse mutant 5′cccaaattaatacactcttgtcgtgacttaccagatgggacagta 3 ′ 354pb

BRCA1-5382insC

Foward wild 5′aaagcgagcaagagaatcgca 3′ 271pb
Foward mutant 5′aatcgaagaaaccaccaaagtccttagcgagcaagagaatcacc3′ 295pb
Reverse 5′gacgggaatccaaattacacag 3′

BRCA2-6174delT

Foward wild 5′gtgggatttttagcacagctagt 3′ 151pb
Foward mutant 5′cagtctcatctgcaaatacttcagggatttttagcacagcatgg 3′ 171pb
Reverse 5′agctggtctgaatgttcgttact 3′

Table 2: Composition of the PCR reaction medium for each gene

Volume to be sampled for a PCR with a reaction volume of 25 μl.

Reagents

Gènes amplifiés
BRCA1-185delAG BRCA1-5382insC

BRCA2-6174delT

Water

8,75 μl

8,75 μl 8,25 μl

Master mix

12,5 μl 12,5 μl

12,5 μl

Fw

0,25 μl

0,25 μl 0,25 μl

Fm

0,25 μl 0,25 μl

0,25 μl

R

0,25 μl

0,25 μl 0,25 μl

Mgcl2

1 μl 1 μl

1,5 μl

Results and Discussion

Results

For 59 patients recruited, 37 (62.71%) were diagnosed with prostate cancer (CaP) and 22 (37.29%) with benign prostatic hyperplasia (BPH). With regard to the BRCA1 (185delAG and 5382insC) and BRCA2-6174delT mutations, the frequency of BRCA1-185delAG mutations in patients with CaP was 40% compared with 60% in those with a BPH lesion, indicating that this mutation shows no significant difference in men with CaP and probably does not contribute to the incidence of this cancer. However, the other two BRCA1-5382insC and BRCA2-6174delT mutations showed higher frequencies (2 to 3 times) in patients with CaP than in those with the BPH lesion, with respectively 62.7% versus 37.3% and 65.1% versus 34.9%. For individuals with adenocarcinoma of the prostate, most cases had a Gleason score greater than or equal to 7 (87%); with 13% of individuals having a Gleason score equal to 6. Gleason scores for prostate tumours were classified into subgroups <7 and ≥7. This threshold was chosen based on clinical experience and previous literature suggesting that the clinical outcome for prostate cancer of Gleason score 7 is more similar to that of Gleason score 8 to 10 than for Gleason score <7 disease2 [20]. Table 3 shows the association between Gleason scores and BRCA1/BRCA2 mutations. Individuals with cape with BRCA1/2 germline mutations were more frequently associated with Gleason score ≥ 8, at stage T3/T4. BRCA1-5382insC and BRCA2-6174delT mutation carriers conferred a 2 to 3-fold increased risk of high-grade prostate cancer. Although the BRCA1-185delAG mutation has not been associated with prostate cancer, it may be associated with high Gleason score tumours. These results must be carefully taken into account in genetic counselling.

Table 3: Association between Gleason scores and BRCA1 /BRCA2 mutations

Gleason score/BRCA mutations

BRCA1-185delAG BRCA1-5382insC BRCA2-6174delT
N individuals N individuals

N individuals

Gleason score 6

0

5 4

Gleason score 7

4 12

9

Gleason score 8

6

13 11

Gleason score 9

0 2

2

Discussion

When analysing the genetics of CaP, it is essential to distinguish between localised, high-risk and metastatic disease. Firstly, due to the widespread adoption of PSA, the majority of new CaP diagnoses are low-grade localised disease with an excellent prognosis. These diagnoses are clinically distinct from the comparatively fewer diagnoses of advanced metastatic CaP [21] which are known to have the potential for a poor outcome. Several studies have shown that the genomic/genetic landscape of metastatic castration-resistant CaP (mCRPC) is different from that of localised [22,23]. It is difficult to obtain meaningful clinical predictions by examining CaP as a whole, given the great clinicopathological heterogeneity of the disease. This can be illustrated by germline mutations in BRCA2 which have been underestimated as a driver of hereditary prostate cancers. Genomic profiling of CaP was initially extrapolated from material acquired during unselected prostatectomies and genetic abnormalities were therefore considered rare [24]. As a result, verification bias prevented reporting the true prevalence of pathogenic genetic mutations in advanced metastatic cape. This work was designed to assess the impact of BRCA 1 and BRCA 2 mutations in prostate cancer in the Thiès region with the association of the three founder mutations BRCA1-185delAG and BRCA1-5382insC and BRCA2-6174delT. This study revealed that the highest frequency of BRCA1 mutations in CaP patients was BRCA1-5382insC (62%) followed by BRCA1 185-delAG (40%). The frequency of BRCA1 mutations in patients with a BPH lesion was 60% for BRCA1-185delAG and 38% for BRCA1-5382insC. In addition, the global BRCA2-6174delT mutation was identified in 65.1% of patients with cape versus 34.9% of those with a BPH lesion. These results suggest that the BRCA2-6174delT and BRCA1-5382insC mutations are likely to contribute to the incidence of prostate cancer in the Thiès region, which is not the case for the BRCA185-delAG mutation, which shows no significant difference in patients with CaP. Our results are comparable to those of Gallagher et al. in 2010 [24] and Agalliu et al. in 2009 [25] where they found mutation frequencies for the BRCA1-5382insC and BRCA2-6174delT genes to be largely predominant in individuals with CaP. Studies of breast cancer by Abou El Naga et al. reported contradictory results, with the two mutations (BRCA1-5382insC and BRCA2-6174delT) showing higher frequencies in healthy controls than in breast cancer patients [26]. In addition, we found that the risk of prostate cancer associated with carrying these mutations was higher in men diagnosed at an older age (65 or over) and in particular in those with the BRCA2-6174delT and BRCA1-5382insC mutations.

A number of previous studies have examined the associations between these BRCA1/BRCA2 mutations and prostate cancer [17,28-30]. Struewing et al. [27] estimated a lifetime risk of CaP of 16% for BRCA1/BRCA2 mutation carriers and 3.8% for non-carriers. Our results reported that BRCA2-6174delT and BRCA1-5382insC mutation carriers had two to three times the risk of prostate cancer, and as indicated here, the BRCA1-185delAG mutation was not associated with prostate cancer. Our results contradict those of Giusti et al. [30] who found the BRCA1-5382insC mutation not to be associated with prostate cancer. The absence of a detectable effect for the BRCA1-185delAG mutation could be linked to its low prevalence in the population, or to the effects of allelic heterogeneity. In support of a role for prostate cancer-associated BRCA2 mutations, studies of breast and/or ovarian cancer families harbouring disease-associated BRCA2 mutations have reported that male family members carrying such mutations have an increased risk of prostate cancer [31-33]. A Finnish study [34] of breast and/or ovarian cancer families also reported a 5-fold increase in the risk of prostate cancer in men carrying BRCA2 protein-truncating mutations. First-degree male relatives of breast cancer patients with protein-truncating BRCA2 mutations had a 4.8% risk of prostate cancer [35]. In 2012, studies by Castro et al. [36] reported that BRCA2 mutation status was found to be an independent predictor of median cause-specific survival. Interestingly, the non-carrier group also had a poorer outcome than other sporadic CaP series, suggesting that a family history of breast cancer could somehow affect the prognosis of prostate cancer patients.

In the present study, a major proportion of mutation carriers had a Gleason score ≥ 7 (87%); our results are similar to those of Gallagher et al. in 2010 [24] where 85% of mutation carriers had Gleason disease ≥7. Our results were striking, with 22 of 26 (84.6%) BRCA2-6174delT mutation carriers and 27 of 32 BRCA1-5382insC mutation carriers (84.3%) showing Gleason disease ≥7, representing a group with an aggressive phenotype and confirming this association reported by Agalliu et al. [25]. However, individuals carrying these two mutations may be associated with a higher risk of prostate cancer and are also likely to have poor survival as reported by Edwards et al. in 2010 [37]. This is also reflected in the study by Kote-Jarai et al. where the proportion of high grade CaP (Gleason score ≥ 8) was 63% significantly elevated [14].The study by Gallagher et al. reported that BRCA2 mutation carriers had an increased risk of CAP and a higher histological grade and that BRCA1 or BRCA2 mutations were associated with a more aggressive clinical course [24] results confirmed by studies by Castro et al. in 2013 in a large retrospective cohort [38].

Conclusion

Prostate cancer is the second most common cancer in men worldwide and is a complex heterogeneous disease with high heritability. Our results showed that BRCA2-6174delT and BRCA1-5382insC mutations are strongly associated with a very aggressive form of prostate cancer. Molecular characterisation of CaP patients should be systematically integrated into healthcare structures in order to select patients who are more likely to respond to targeted agents. In addition, in the event of a family history of hereditary breast cancer (± hereditary ovarian cancer), it is recommended that the patient be referred to an oncogenetic consultation to look for a mutation in the BRCA1 and BRCA2 genes. In the case of aggressive prostate cancer (high Gleason score or locally advanced or metastatic stage) in a patient under the age of 50, it is recommended that the patient be referred to an oncogenetic consultation to look for a mutation in the BRCA2 and HOXB13 genes (level of evidence 2a) [39]. Further clinical trials would be needed to assess the impact of genomic nuances in reducing the morbidity and mortality prevalent with prostate cancer.

Acknowledgement

The authors are very grateful to the patients who participated in this study. We are extremely grateful to Dr Modou Faye who helped for sample collection. Also Pr SEMBENE, head of the genomics laboratory for all the molecular studies carried out and Pr Tonleu Linda Bentefouet, head of the cytological and pathological anatomy unit for the histopathological diagnoses.

Conflict of Interest

The authors have declared no conflicts of interest.

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Function Inspired Structures of Proto-Ribosome and the First Aminoacyl-tRNA Synthetase: Origin of life in the water of the Earth (III)

DOI: 10.31038/GEMS.2023581

Abstract

The first protein must produce at random processes. However, it is difficult to replicate correct protein without any control. The activities of control must be different from activities to be control. It is known that organisms replicate proteins via ribosomes by using genetic information. The mechanism that had replicated proteins naturally is a bridge between living organism and nonliving organism. We can make assumptions about structure of proto-ribosome on the base of its functions. That is, a proto-ribosome would have sandwiched L-type mRNA and D-type tRNA in part of a phospholipid bilayer. This structure can be used to estimate the initial processes of replicated proteins and the initial formation of aminoacyl-tRNA synthetases.

Keywords

Enzyme world, Ribosome, DNA, mRNA, tRNA, Aminoacyl-tRNA synthetase

Introduction

The first life had formed in non-extreme environment on the Earth because the first cell with gene system must have naturally formed. However, many of traditional studies of the origin life have been carried out on extremophiles [1], because most of professional researchers must acquire budgets, they have been studied by the acquired budget. The organisms had been born, then evolved by adaptations in its environment. By the results of evolution, extremophiles can live only in extreme environment [2]. On the other hand, there are numerous descriptions on molecular biology [3]. Especially ongoing progress in the structural biology is giving a physico-chemical basis that explains facets about tRNA [4]. Replication of protein is controlled via the informational media of double helix of DNA discovered by Watson and Click [5]. Karasawa reported that the replication processes of DNA are possible to reveal based on the structure of DNA [6,7]. The initial process of protein replication can be revealed based on the structure of the proto-ribosome which includes mRNA and tRNA in a part of a phospholipid bilayer. Here, the mRNA is left-handed (L-type) chirality, while the tRNA is right-handed (D-type). Although these two strands with different chirality enter a plathome of processing of central part of the double-layer, those strands never coalesce. The double-layered helical structures are indispensable in the ribosome-translation machinery.

Preparations

Formation of mRNA, tRNA and DNA

Organic molecules such as hydrocarbons were accumulated on the surface of water, and macroscopic boundary conditions formed a membrane [6]. When amino acid molecules adhered to the membrane, those molecules were formed molecular structures possessing with the function of enzymes. The first organization of life had formed in the world of enzymes. Current protein is replicated in a ribosome via short-lived mRNA and tRNA. Those mRNA and tRNA are produced from a replicated DNA [7]. Since RNA is a tool to deal with genetic information, the first life should be discussed in the real world of enzymes instead of the informational world of RNA.

Matching Processes between mRNA and tRNA

When amino acids adhere a membrane, conformation of a part of membrane is modified. The changed conformation of the membrane includes information on the amino acid sequence of a protein. Even though a proto-DNA is formed by simplifications from the membrane by exclusion of the protein, it possesses information of size and segmentation on the amino acid. Such information is used for the first matching processes between template of mRNA and matching objects of tRNA. Subsequent evolutions, pairing relationships for the matching was established by complementary base pairs of codons and anticodons. So, intermittently fixing of a segment of mRNA for a specified amino acid in a protein, each tRNA is shifted along the mRNA in order to looking for the partner of hydrogen bonds.

Leading Strand and Lagging Strand of DNA

Since chirality of mRNA is L-type but tRNA is D-type, two kinds of RNA do not merge. It is known that the chirality of biomolecule in the Earth, amino acids are L-type, and sugars are D-type. So, the membrane adhered with protein and bases has L-type chirality, but D-type of helical structure will be formed due to antagonistic and organized movements of interconnected helix structures [7]. That is, alternate rotations around X axis through the center of tetrahedron units changes the shape of tetrahedral unit projected in the X-Y plane from square to trapezoidal. When a pair of atoms of tetrahedron located at the end of the long and short site vibrate up-and-down movements, inner and outer in the tetrahedron’s vertices vibrate opposite directions. Under an assumption of such antagonistic movements, leading strand and lagging strand of DNA are synthesized simultaneously at each segmentation of the amino acid. Since each amino acid has individual size, the segment of constituent of ribosome for an amino acid is the same. tRNA is formed by a single D-type of lagging strand with base due to chirality difference between amino acid and lagging strand. So, the leading strand replicates continuously, whereas the lagging strand replicates discontinuously forming short fragments. Since bases of two RNAs touch via hydrogen bonds, tRNA is possible to move independently from mRNA. Here, the complementary anticodon of tRNA is vertical flip symmetry of corresponding amino acid of mRNA.

Results

Formation of Proto-ribosome in a Phospholipid Bilayer

Phospholipid contains a chiral center at C2 position of a glyceryl moiety [7]. Twisted phospholipids laterally interlock, and the interlock induces systematic motions due to systematic thermal vibrations of atoms [8]. A double layer sandwiched between two layers of hydrophilic heads spontaneously forms. If one of the layers in part of the bilayer is rotated by 180°, the progress of the helix is changed from the output side to the input side at the center of the bilayer. This bilayer, despite having two chirality centers, provides a one-directional screw movement by the one-directional rotation. When only mRNA enters the bilayer, it passes through the bilayer. However, if tRNA enters from the other side of the mRNA, both strands come into conflict at the central portion owing to chirality [7]. mRNA and the series of tRNA’s are sandwiched between a protein and a series of amino acids with base pairs facing each other at the center. Figure 1 is an illustration of a structure of proto-ribosome and its constituents proposed in this paper.

FIG 1

Figure 1: A structure of proto-ribosome and with its constituents

Prospect of the Protein Replication: Evolutions of Gene System on Chain Reactions

When a biological reaction is performed, a new reaction occurs due to change of the situation caused by the reaction. A chain reaction will continue to circulate if it forms a loop. A relationship of “from demand to the supply” will be included in those chain reactions. In various chain reactions, protein molecules that express repeated chain reactions will be formed and the enzymes will be formed. The chain reactions those support the survival of life will be incorporated into genes system in the form of long DNA. Then, Prokaryote have evolved to Eukaryote by formation of a nucleus of the cell in order to memorize very long DNA.

Discussions

Molecular Mechanisms Underlying Ribosome Dynamics

A step of protein replication is proceeded by amino acid unit at platform of a ribosome. The triplet base pairs in a DNA for each amino acid are formed via pattern matching on hydrogen bond between codon of mRNA and anticodon of tRNA. The complementally base pairs are adenine with thymine (A-T) and cytosine with guanine (C-G) for each base step. Incidentally, an aminoacyl-tRNA synthetase makes linkage between the triplet code and an amino acid by the direct attachment of an amino acid and corresponding tRNA [9]. Tamura, Schimmel reported about non-enzymatic aminoacylation of an RNA minihelix [10]. Karasawa proposes a functional model of aminoacyl-tRNA synthetase that comes from a cover around the functional model of aminoacyl-tRNA as shown in Figure 1. The linkage between amino acid and the triplet is carried out by an aminoacyl-tRNA synthetase. The proto-DNA forms a unique conformation when interacting with amino acids. The proto-DNA must possess information on amino acid. The enzyme binds with specific molecules, resulting in a conformational change, and carries out function of the catalyst. However, even the base sequence of tRNA has been revealed, understanding the molecular mechanisms underlying tRNA dynamics is yet challenging [11].

Conclusions

The author proposes that the first life should be discussed in the real world of enzymes instead of the informational world of RNA. Over the course of evolution, if a new mechanism is added alongside conventional mechanisms that functioning during life activities, the new mechanism must coexist with the conventional system. Eventually, the new system that successes to survive will remain, and unnecessary system will disappear. The research based on the current system is difficult to reveal the disappeared structures. The proto-ribosome was estimated based on the necessity that shifts tRNA reversely for mRNA and confirms the matched amino acid sequences. We can describe fundamental functions of ribosome by assuming such simple initial structure and its environments. The bottom-up approaches based on acceptable assumptions are useful to reveal initial processes of protein replications. However, there is a gap between the proposed model and current nucleotide sequence models in the molecular biology. It is known that the evolution of living organisms has influenced the Earth’s atmosphere and geology. It is another desire of the author that the proposed functional models will become bridges between living organisms and the field of geology of the Earth.

References

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