Biostatistical Analysis Research Articles

Unveiling Emerging Opportunistic Fish Pathogens in Aquaculture: A Comprehensive Seasonal Study of Microbial Composition in Mediterranean Fish Hatcheries

The importance of microbial communities in fish hatcheries for fish health and welfare has been recognized, with several studies mapping these communities during healthy rearing conditions and disease outbreaks. In this study, we analyzed the bacteriome of the live feeds, such as microalgae, rotifers, and Artemia, used in fish hatcheries that produce Mediterranean species. Our goal was to provide baseline information about their structure, emphasizing in environmental putative fish pathogenic bacteria. We conducted 16S rRNA amplicon Novaseq sequencing for our analysis, and we inferred 46,745 taxonomically annotated ASVs. Results showed that incoming environmental water plays a significant role in the presence of important taxa that constitute presumptive pathogens. Bio-statistical analyses revealed a relatively stable bacteriome among seasonal samplings for every hatchery but a diverse bacteriome between sampling stations and a distinct core bacteriome for each hatchery. Analysis of putative opportunistic fish pathogenic genera revealed some co-occurrence correlation events and a high average relative abundance of Vibrio, Tenacibaculum, and Photobacterium genera in live feeds, reaching a grand mean average of up to 7.3% for the hatchery of the Hellenic Center of Marine Research (HCMR), 12% for Hatchery A, and 11.5% for Hatchery B. Mapping the bacteriome in live feeds is pivotal for understanding the marine environment and distinct aquaculture practices and can guide improvements in hatchery management, enhancing fish health and sustainability in the Mediterranean region.

Research on the Application of Computer Technology in Biostatistics

This paper explores the application of computer technology in biostatistics, focusing on the technological advancements and their impact on data management, data processing and analysis, and data visualization. With the development of computer technology, data storage and management methods have significantly improved, particularly with the introduction of database systems and big data technologies, which have greatly enhanced the efficiency and accuracy of handling biostatistical data. Additionally, the application of machine learning and artificial intelligence has made biostatistical data analysis more intelligent and automated, providing new tools for identifying and predicting complex data patterns. Furthermore, advanced data visualization techniques have enhanced the interpretability of statistical results through clear graphical presentations, promoting the application of biostatistics. Through the analysis of specific case studies, this paper demonstrates the practical effects and challenges of computer technology in improving biostatistical research capabilities. Finally, the paper anticipates the future impact of emerging computing technologies, such as quantum computing, on biostatistics and suggests future research directions and recommendations.

A new frontier in biostatistics: evaluating the accuracy of ChatGPT-4 vs. R in analysing liver resection data

Background The rise of ChatGPT-4’s Data Analyst tool presents a new frontier for biostatistical computations. This study evaluates the reliability and improvements of ChatGPT-4 Data Analyst tool by comparing it to R package in performing biostatistical analysis on liver surgery patients. Methods Utilizing data from LiverGroup.org, we conducted our comparative study between October 2023 and March 2024. The variables analyzed by the R package and ChatGPT-4 Data Analyst included age, sex, hospital stay duration, income group, and mortality. Analysis on ChatGPT-4 were performed using two methods: a holistic prompt which included all-at-once analysis were requested and segmented prompts, one-by-one test request for analysis. After the analysis figures were requested from ChatGPT-4, comparison with R package figures was done. Results Descriptive analysis including N (%), Standard Deviation, and (25th–75th Percentile) were consistent between ChatGPT-4 March version and R with a minor variation in the holistic approach on the analysis performed in October. The inferential statistical results of ChatGPT-4 showed inconsistencies in October 2023 while March 2024 revealed accurate results with Crosstabulations, Kruskal Wallis, Wilcoxon Rank Sum, T-test, Pearson’s Chi-squared, and Fisher’s Exact test p-value. ChatGPT-4 March 2024 version was able to inform the user with possible inaccuracies in certain tests (Mann-Whitney U Test: Hospital stay vs mortality p value, Levene’s Test p-value: Age vs mortality, and Fisher’s Exact Test: Odds ratio gender vs. mortality 95% CI). The survival curve and box-and-whisker plot generated by ChatGPT-4 in March 2024 matched those generated by R package except for the CI of survival curve. Conclusions The high accuracy of ChatGPT-4 in certain biostatistical analysis has reached the point where it can replace established statistical software like R for some purposes. Artificial intelligence tools show significant promise but should still be used in conjunction with traditional methods to ensure precision in complex analysis. Consensus on the use of these tools is needed by the scientific community.

Fundamentals of Nonparametric Statistical Tests for Dental Clinical Research.

This article provides the foundation for employing nonparametric testing in dental clinical research. To make wise judgments in their research, investigators should learn more about the main nonparametric tests and their particular uses. Biostatistical analysis is essential in dental research; dental research frequently deviates from the assumptions that underpin traditional parametric statistics. Nonparametric statistics are useful for studies with small sample sizes, nominal- or ordinal-level data, and non-normally distributed variables. These statistical tests make no assumptions about the sampled population. Nonparametric tests are statistical methods based on signs and ranks. For dental research to be conducted effectively and accurately, statistical approaches must be applied correctly. Therefore, dental researchers must understand the many statistical methods at their disposal and know when to use them.

Assessing the Metabolic Variations of Invasive and Noninvasive Unilateral Retinoblastoma Patients.

Retinoblastoma (Rb) is a pediatric eye cancer which if diagnosed at later stages can lead to Rb invasion into the choroid, optic nerve, sclera, or beyond, with the potential of undergoing metastasis. Cancer cells, including Rb cells, reprogram their metabolic circuits for their own survival and progression, which provides a great opportunity to monitor the extent of Rb progression based on metabolic differences. Henceforth, the present study aims to map the metabolic variations in patients with invasive (primarily enucleated eyes with high-risk histopathological features) and noninvasive (eyes salvaged with treatment) unilateral retinoblastoma (Rb) using nuclear magnetic resonance (NMR) based serum metabolomics. Quantification of differential metabolites in the serum obtained from 9 patients with invasive and 4 with noninvasive unilateral Rb along with 6 controls (no retinal pathology) was carried out using 1H NMR spectroscopy. A total of 71 metabolites, such as organic acids, amino acids, carbohydrates, and others, were identified in the serum obtained from 9 patients with invasive and 4 with noninvasive unilateral Rb. Partial least-squares discriminant analysis (PLS-DA) models depicted distinct grouping of invasive and noninvasive Rb patients and controls. Differential metabolic fingerprints were observed for invasive and noninvasive Rb patients based on their biostatistical analyses with respect to controls. Remarkable perturbation was observed among various metabolites such as 4-aminobutyrate, 2-phosphoglycerate, O-phosphocholine, proline, Sn-glycero-3-phosphocholine (Sn-GPC), and O-phosphoethanolamine in noninvasive and invasive Rb patients with most of the effects being heightened in the latter group. Metabolic changes unique to invasive and noninvasive Rb patients were also observed. Multivariate receiver operating characteristics (ROC) analysis unveiled the highest accuracy and potency of ROC models 2 and 5 to distinguish the noninvasive and invasive Rb from controls, respectively. Metabolites identified in the serum of patients with invasive and noninvasive Rb may aid in advancing our knowledge about Rb tumor biology. Differential aberrant metabolic variations in patients with invasive Rb compared to those with noninvasive Rb may guide the decision of enucleation versus globe salvage.

Microbial regulation on refractory dissolved organic matter in inland waters

The production of refractory dissolved organic matter (RDOM) is complex and closely related to microbial consortia in aquatic ecosystems; however, it is still unclear how microorganisms regulate the production of RDOM and its molecular composition in inland waters. Therefore, we conducted a large-scale survey of inland waters and analyzed the optical and mass spectrometric characteristics of DOM, the microbial community and functional genes, as well as related environmental parameters, to understand the abovementioned issues. Here, the RDOM production was found mainly regulated by microbial (e.g., phylogeny and community assembly) rather than other environmental factors in inland waters. Biostatistical analyses and carbon isotopic evidence indicated that the successive microbial processing from labile DOM to RDOM (i.e., carboxyl-rich alicyclic molecules, CRAMs) was widely present in inland waters, involving the microbially mediated carbon skeleton turnover and heteroatom conversion. There was a significant empirical relationship between CRAMs and the ratio of Proteobacteria to Actinobacteria, highlighting the intraspecific interaction of bacteria more important than other microbial groups (i.e., archaea, eukaryotes, and fungi) for the RDOM production. This study demonstrated a fundamental role of microbial regulation in RDOM production within the inland waters, thereby facilitating future estimation of carbon sequestration potential in inland aquatic ecosystems.

Common mistakes in biostatistics.

Biostatistics plays a pivotal role in developing, interpreting and drawing conclusions from clinical, biological and epidemiological data. However, the improper application of statistical methods can lead to erroneous conclusions and misinterpretations. This paper provides a comprehensive examination of the most frequent mistakes encountered in the biostatistical analysis process. We identified and elucidated 10 common errors in biostatistical analysis. These include using the wrong metric to describe data, misinterpreting P-values, misinterpreting the 95% confidence interval, misinterpreting the hazard ratio as an index of prognostic accuracy, ignoring the sample size calculation, misinterpreting analysis by strata in randomized clinical trials, confusing correlation and causation, misunderstanding confounders and mediators, inadequately codifying variables during the data collection, and bias arising when group membership is attributed on the basis of future exposure in retrospective studies. We discuss the implications of these errors and propose some practical strategies to mitigate their impact. By raising awareness of these pitfalls, this paper aims to enhance the rigor and reproducibility of biostatistical analyses, thereby fostering more robust and reliable biomedical research findings.

Wakefield's Harm-Based Critique of the Biostatistical Theory.

Jerome Wakefield criticizes my biostatistical analysis of the pathological-as statistically subnormal biological part-functional ability relative to species, sex, and age-for its lack of a harm clause. He first charges me with ignoring two general distinctions: biological versus medical pathology, and disease of a part versus disease of a whole organism. He then offers 10 counterexamples that, he says, are harmless dysfunctions but not medical disorders. Wakefield ends by arguing that we need a harm clause to explain American psychiatry's 1973 decision to declassify homosexuality. I reply, first, that his two distinctions are philosophic fantasies alien to medical usage, invented only to save his own harmful-dysfunction analysis (HDA) from a host of obvious counterexamples. In any case, they do not coincide with the harmless/harmful distinction. In reality, medicine admits countless chronic diseases that are, contrary to Wakefield, subclinical for most of their course, as well as many kinds of typically harmless skin pathology. As for his 10 counterexamples, no medical source he cites describes them as he does. I argue that none of his examples contradicts the biostatistical analysis: all either are not part-dysfunctions (situs inversus, incompetent sperm, normal-flora infection) or are indeed classified as medical disorders (donated kidney, Typhoid Mary's carrier status, latent tuberculosis or HIV, cherry angiomas). And if Wakefield's HDA fits psychiatry, the fact that it does not fit medicine casts doubt on psychiatry's status as a medical specialty.

Penerapan Machine Learning untuk Mengetahui Kelangsungan Hidup Pasien Gagal Jantung (Kardiovaskular) Menggunakan Kreatinin Serum dan Fraksi Ejeksi

Cardiovascular diseases cause deaths worldwide every year, most of which manifest primarily as heart attacks or heart failure. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the body's needs. Available electronic health records can measure symptoms, physical characteristics, and laboratory values, and perform biostatistical analysis to uncover patterns and relationships unknown to general practitioners. In particular, Machine Learning can predict patient survival based on data and personalize key characteristics of medical records. This article analyzes a data set of 299 heart failure patients by applying machine learning algorithms using an Adaboost-based artificial neural network algorithm to improve further the accuracy of the artificial neural network (ANN) algorithm. In the experimental results of this study, it was found that the accuracy of the AdaBoost-based artificial neural network (ANN) algorithm became very significant with an accuracy result of 81.01%.

Suggested mechanism of CCR5Δ32, CCR2-64I and SDF 1-3'A allele frequency change in Polish and Lithuanian gene pools from the perspective of passing time.

The study aimed to determine the frequency of the alleles associated with hereditary immune response in 16 historical populations and assess which evolutionary forces may have contributed to the observed frequency fluctuation. The analysed polymorphic sites are located in three genes - CCR5, CCR2 and SDF 1 (CXCL12). Protein products are involved in the innate immune response and are also involved in various types of infections, autoimmune diseases and tumours. The frequency of the alleles found in the DNA of the studied individuals was determined by the Sanger methodology and was compared with the data obtained for modern populations. To confirm the authenticity of the obtained results, mtDNA HVRI haplotypes of all the studied samples were obtained and compared with the genetic database of the laboratory personnel who came into contact with the studied material. Based on the variability of allele frequency, advanced biostatistical analysis was used to distinguish the effect of natural selection from genetic drift, i.e. the forces operating on the polymorphic sites studied. All procedures were performed according to the guidelines for working with ancient DNA to avoid contamination with modern DNA molecules. 681 samples from 39 archaeological sites in Poland and Lithuania dated to the 40th century BC and the 19th century were studied. The biostatistical analysis showed that the fluctuations in the frequency of CCR5Δ32 in the analysed time interval could be mainly the effect of genetic drift. Nevertheless, for CCR2-64I and SDF 1-3'A, the results confirm the suggestion of negative selection as the mechanism involved. Since all the polymorphic sites encode the elements of innate immune response that are indirectly associated with the process of an HPV infection and the development of cervical cancer, the human papillomavirus may be a good candidate for a selection coefficient affecting the frequency of CCR2-64I and SDF 1-3'A. However, for CCR5Δ32, selection was not detected despite its proven role in the molecular mechanism involved in the response to an HPV infection. The presented work seems to be the first in which the problem of the pattern of CCR5Δ32, CCR2-64I and SDF 1-3'A frequency fluctuations in a temporal perspective was discussed, proposing HPV as a factor influencing the occurrence of the CCR2 and SDF1 alleles.

Efficacy of P2Y12 receptor inhibitors in myocardial infarction in individuals carrying different CYP2C19 genotypes living in the northern region of Russia

Aim. To evaluate the efficacy of P2Y12 receptor inhibitors (clopidogrel and ticagrelor) in patients with myocardial infarction (MI) living in the northern region of Russia (Khanty-Mansi Autonomous Okrug — Yugra), depending on the carriage of various CYP2C19 allelic variants.Material and methods. This prospective observational study included 218 patients with acute MI who underwent percutaneous coronary intervention (PCI). The patients also underwent determination of allelic variants of the CYP2C19 gene. Patient were divided into groups receiving clopidogrel (n=164, 75%) and ticagrelor (n=54, 25%). Using biostatistical analysis methods, a comparison of clinical and genetic characteristics was performed, as well as an assessment of the risk of ischemic events between the groups in the long-term (108 months, 9 years) post-infarction period.Results. Reduced (*1/*2, *2/*2, *1/*3) and increased (*1/*17, *17/*17) metabolizer CYP2C19 genotypes, as well as severe comorbidity, are reliable independent predictors of composite outcome (cardiovascular death, recurrent acute coronary syndrome, coronary stent/bypass thrombosis, myocardial revascularization, acute ischemic cerebrovascular accident) during a long-term (9 years) follow-up. The advantages of ticagrelor over clopidogrel in terms of the effect on the incidence of ischemic events in the long-term period were established without a significant difference for bleeding both in the general cohort of patients and among carriers of CYP2C19 allelic variants (*1/*2, *2/*2, *1/*3) and (*1/*17,*17/*17) in patients with MI.Conclusion. Ticagrelor is significantly more effective than clopidogrel in reducing the risk of ischemic events in the general cohort of patients, as well as in carriers of reduced (*1/*2, *2/*2, *1/*3) and increased (*1/*17, *17/*17) metabolizer CYP2C19 genotypes during 9-year follow-up after the index MI.

Selection versus transmission: Quantitative and organismic biology in antibiotic resistance

We aimed to determine the importance of selection (mostly dependent on the anthropogenic use of antimicrobials) and transmission (mostly dependent on hygiene and sanitation) as drivers of the spread of antibiotic-resistant bacterial populations. The first obstacle to estimating the relative weight of both independent variables is the lack of detailed quantitative data concerning the number of bacterial cells, potentially either pathogenic or harmless, and bacterial species exposed to antimicrobial action in the microbiotas of specific environments. The second obstacle is the difficulty of considering the relative importance of the transmission and selection exerting their combined effects on antibiotic resistance across eco-biological levels. As a consequence, advances are urgently required in quantitative biology and organismic biology of antimicrobial resistance. The absolute number of humans exposed to antibiotics and the absolute number of potentially pathogenic and commensal bacteria in their microbiomes should influence both the selection and transmission of resistant bacterial populations. The “whole Earth” microbiome, with astonishingly high numbers of bacterial cells and species, which are also exposed to anthropogenic antimicrobials in various biogeographical spaces, shapes the antibiotic resistance landscape. These biogeographical spaces influence various intensities of selection and transmission of potentially pathogenic bacteria. While waiting for more precise data, biostatistics analysis and mathematical or computational modeling can provide proxies to compare the influence of selection and transmission in resistant bacteria. In European countries with lower sanitation levels, antibiotic consumption plays a major role in increasing antibiotic resistance; however, this is not the case in countries with high sanitation levels. Although both independent variables are linked, their relative influence on the level of antibiotic resistance varies according to the particular location. Therefore, interventions directed to decrease antibiotic resistance should be designed “a la carte” for specific locations with particular ecological conditions, including sanitation facilities.

Using R Programming for Somatoploting

Introduction: Anthropometry is a technique employed to evaluate body dimensions and ratios by examining body length, width, circumference, and skinfold thickness. It is cost-effective, uncomplicated, and easily transportable, and it may be used in diverse industries. Somatotyping is a primary method used to classify the human physique based on three main components: endomorphy, mesomorphy, and ectomorphy. Heath and Carter established and modified the standards of somatotype, which continue to be employed for global measurements. Several software tools have been created for somatoploting, including SAS/GRAPH, Houcine, Orhan, and machine-learning models. Nevertheless, most of these tools are not open-source, resulting in laborious manual enumeration and hindering the accurate representation of differences among groups. A functional, open-source, precise tool is required to categorise somatotypes of extensive sample sizes and illustrate their differences. Method: R programming is a powerful and versatile programme language, particularly popular in statistical computing and graphics. It is widely used in various fields, like biostatistics, bioinformatics, and financial market analysis. R incorporates original programming concepts like object-oriented programming, which users can use transparently. This paper introduces how to use R programming as a tool for somatoploting, introduces the code for somatoploting, inserts x and y data, and executes the program to get a somatochart. It uses anthropometric data of 34 school-going students collected in Shindikurbet, Karnataka, aged between 10 and 12 years, collected through ISAK protocol guidelines to develop somatotypes and further plot them. Result: The paper holistically demonstrates using R programming to plot somatotypes in a 2-D Somatochart. Using this process, the reader can develop high-quality somatocharts in image or PDF formats. Conclusion: This study explores using R programming, an open-source software, for somatoploting and generating somatocharts. This method aids in understanding complex information, fact-explaining, and guiding action in various fields. It offers accessible data processing, analysis, and presentation, making implementing and saving budgets for students, researchers, and institutions easy. Further research could be conducted to make the code easier to use in Excel sheets or mobile applications.

Indian Perspective on De-escalation from Dual Antiplatelet Therapy to Single Antiplatelet Therapy Study: A Knowledge, Attitude, and Practice Study among Indian Interventional Cardiologists.

Coronary artery disease (CAD) management is one of the most significant facets of interventional cardiology. Evidence from several clinical trials has redefined the drug management of CAD, including optimizing the duration of antiplatelet treatment regimens in the management of CAD, which is an intricate clinical issue. The available evidence indicates that East Asians have a higher bleeding risk. However, the Indian phenotype differs from that of East Asians, making this data confounding when applied to clinical decision-making among Indian patients. There is a need for a close understanding of Indian interventional cardiologists' perceptions of complex decision-making pertaining to antiplatelet agents among Indian CAD patients in real-world clinical settings. This Indian Perspective on De-escalation from Dual Antiplatelet Therapy to Single Antiplatelet Therapy (INDEPTH) study aims to assess the perspective of Indian interventional cardiologists regarding de-escalating from dual antiplatelet therapy (DAPT) to single antiplatelet therapy (SAPT), approach to decision-making, barriers, and related challenges in CAD management. A cross-sectional knowledge, attitude, and practice (KAP) study survey was carried out among Indian interventional cardiologists practicing across different regions of India. A total of 209 responses were received. Descriptive statistics was used to summarize all the parameters. IBM Statistical Package for the Social Sciences (SPSS) statistics was used for biostatistical analysis. The study indicated that >90% of CAD patients received DAPT therapy immediately after percutaneous coronary intervention (PCI) (86.1%, p < 0.001). About 115 (55%) of the respondents reported using calculator-based scoring for evaluating bleeding risk in patients on DAPT therapy for the management of acute coronary syndrome (ACS) with post-PCI (p = 0.167). Regarding the usual duration of DAPT therapy post-ACS, nearly half of the respondents, 94 (45%), said that 6-12 months is the usual duration for DAPT therapy in post-ACS patients, followed by > 12 months 94 (45%) of the respondents; 17 (8.1%) of the respondents reported it is 3-6 months, and lastly up to 3 months as per four (1.9%) of the respondents (p < 0.001). A total of 128 (61%) of the respondents strongly believe that balancing bleeding with ischemic risk influenced the choice of antiplatelet agent when treating established CAD. As per interventional cardiologists surveyed, the perfect de-escalation time frame for Indian CAD patients with high bleeding risk (HBR) is up to 3 months (35.9%, p < 0.001), 6-12 months for medium bleeding risk (48.8%, p < 0.001), and >12 months for low bleeding risk (65.6%, p < 0.001). Regarding SAPT therapy, almost one-third of the respondents, 65 (31.1%), reported that they prescribed antiplatelet therapy other than aspirin in 20-40% of their SAPT-eligible patients. Furthermore, 69 (33%) of the respondents said that they preferred to prescribe clopidogrel in 50-75% of SAPT-eligible patients. While 64 (30.5%) prescribed in 25-50%, 53 (25.4%) prescribed in <25% and 23 (11%) of the respondents prescribed the drug in >75% of the SAPT-eligible patients. (p < 0.001). "Atorvastatin + clopidogrel" is the most preferred combination of SAPT primarily for the management of CAD among the majority of interventional cardiologists [33%, 95% confidence interval (CI): 1.97-2.24, p < 0.001]. The study respondents also indicated a need for Indian-specific guidelines on de-escalating from DAPT to SAPT in CAD management. The INDEPTH study indicated that the majority of CAD patients received DAPT immediately after PCI. The perfect de-escalation time frame for Indian CAD patients with "high-bleeding" risk is up to 3 and 6-12 months for "medium-bleeding" risk and >12 months for "low-bleeding" risk. One-third of respondents used clopidogrel as an antiplatelet agent in 50-75% of SAPT-eligible patients. Atorvastatin + clopidogrel is predominantly the most preferred combination of statin + SAPT for the management of CAD. Although the current international guidelines cover the Indian perspective to some extent, there is a need for Indian-specific guidelines on de-escalating from DAPT to SAPT.

Validation of an Anti-Müllerian Hormone Cutoff for Polycystic Ovarian Morphology in the Diagnosis of Polycystic Ovary Syndrome in the HARMONIA Study: Protocol for a Prospective, Noninterventional Study.

Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women and is diagnosed using the Rotterdam criteria, including diagnosis of polycystic ovarian morphology (PCOM) by transvaginal ultrasound (TVUS). Due to high cost, availability, and the impact of the operator and ultrasound equipment on the reliability of the antral follicle count (AFC) by TVUS, an unmet need exists for a diagnostic test to determine PCOM without TVUS. A strong positive correlation between elevated anti-Müllerian hormone (AMH) levels and AFCs has been demonstrated in women with PCOS. In addition, recent updates to the international evidence-based PCOS guidelines state that serum AMH can be used as an alternative to TVUS-determined AFC, in the diagnosis of PCOM. The retrospective APHRODITE study derived and validated an AMH cutoff of 3.2 ng/mL for the Elecsys AMH Plus or Elecsys AMH assays (Roche) to diagnose PCOM in patients with PCOS. This study aims to further validate, in an independent prospective cohort, the AMH cutoff (3.2 ng/mL) for PCOM determination, which was previously derived and validated in the APHRODITE study. This large, prospective, multicenter, population-based, noninterventional study will evaluate the previously established AMH cutoff for the determination of PCOM during the diagnosis of PCOS using the Elecsys AMH Plus immunoassay in an independent population. Participants were women born between July 1985 and December 1987 in Northern Finland; the study partially links to the Northern Finland Birth Cohort 1986. We assessed the enrolled women, determined with the 2023 PCOS Guidelines, for current PCOS status and divided them by phenotype if positive. Each participant had 1 study visit to collect serum samples, record clinical data, and undergo a gynecological examination including TVUS. All data were collected by highly trained midwives or trained gynecologists. Sensitivity, specificity, and agreement measures were used to validate the previously determined cutoff in the whole population and in subpopulations based on phenotype and relevant demographic or clinical factors. The minimum target sample size was approximately 1800 women, including approximately 10% with PCOS. At the time of manuscript submission, participant recruitment had concluded, and 1803 women were enrolled into the study. Data collection is complete and biostatistical analysis is planned for 2023. To limit variability, there were few TVUS operators and only 2 TVUS machines of the same type. Additionally, all women who were taking oral contraceptives were excluded from the primary analysis population. Selection bias was limited as this was a population-based study and participants were not seeking treatment for PCOS symptoms. Validating the AMH cutoff in a large, population-based study will provide further evidence on the utility of the Elecsys AMH Plus or Elecsys AMH assays in PCOM diagnosis as an alternative to TVUS. Measuring AMH for PCOM diagnosis could reduce delayed or missed diagnoses due to operator-dependent TVUS examinations. ClinicalTrials.gov NCT05527353; http://tinyurl.com/2f3ffbdz. DERR1-10.2196/48854.

Biostatistical Analysis on the Generation Mechanism of Neutral Consensus and Opinion Polarization

We establish a biostatistical model to describe the generation mechanism of neutral consensus and opinion polarization. The paper also explores the formation mechanism of the "screaming effect," "echo chamber effect," and "information cocoon room" and studies how to use reasonable strategies to avoid the "screaming effect" and "echo chamber effect" according to the biostatistical model built.

A computational statistical approach to assess cowpea [Vigna unguiculata (L.) Walp.] cultivars diversity and select elite genotypes from Agro-morphological and biochemical traits

Computational statistic approaches play an essential role in the evaluation and processing of agronomic, biological, and bio-medical big data. The complexity and large size of those data make computational statistics a crucial tool in bio-statistical analysis. Based on this evidence, we characterized phenotype performances of cowpea cultivar in the Northern of Côte d’Ivoire, developing our own computational statistical approach, exclusively in the R programming language. Several packages of R have been executed to assess cowpea cultivar agro-morphological and biochemical performances. Z-score clustering analysis revealed four groups of cowpeas based on agro-morphological parameters. K-mean clustering survey revealed four and two groups of cowpea cultivar respectively for agro-morphological and biochemical parameters. The Horn parallel analysis highlighted two, four, two and two agro-explanatory components and/or agro-morphological parameters as influencing data variability respectively in the first, second, third, and fourth groups of cowpea cultivars previously revealed by the k-mean analysis. The same analysis exhibited two components in terms of biochemical parameters, inducing the variability in the two cluster groups of cowpea cultivar revealed by the k-mean survey. Integrative analysis of the ANOVA test and Tukey’s multi-comparative analysis displayed yield (agro-morphological parameter) and cowpea energetic content (biochemical parameter) as main sources of cowpea cultivar phenotypical variability (P &lt; 0.05). Of note, receiver operating characteristic predictive analysis showed the excellent performance of both cowpea yield and energetic content in selecting cowpea genetic germplasm area under the curve (AUC = 0.9). Considering as a whole, the present computational statistical approach shows excellent performance in the evaluation, characterization, and management of agro-morphological (yield parameter) and biochemical (energetic content) features of cowpea in genetic selection procedures.

Multivariate analysis of craniodental morphology in mouse-eared bats (Chiroptera, Vespertilionidae, Myotis) from Vietnam.

This study conducted biostatistical multivariate analyses on 23 craniodental morphological measurements from 209 specimens to study interspecific variations amongst 15 bat species of the genus Myotis in Vietnam. Univariate and multivariate analyses demonstrated that the studied species can be divided into four groups as follows: extra-large-sized species (M.chinensis), large-sized species (M.pilosus, M.indochinensis and M.annectans), medium-sized species (M.altarium, M.hasseltii, M.montivagus, M.horsfieldii, M.ater, M.laniger and M.muricola) and small-sized species (M.annamiticus, M.aff.siligorensis, M.rosseti and M.alticraniatus). Our data revealed that the main craniodental features contributing to the variations in distinguishing Myotis species are the width of the anterior palatal, least height of the coronoid process, length of the upper and lower canine-premolar, zygomatic width and width across the upper canines and lower premolar-molar length. Based on patterns of morphological differences, we conducted comparisons between morphometrically closely resembling species pairs and further discussed additional characteristics that are expected to support the taxonomy and systematics of Vietnamese Myotis bats.

Evaluation of functional interaction masseter and sternocleidomastoid muscles affected by TMJ dysfunction.

Aim: To evaluate the functional connection and the bioelectrical state of the m.masseter and m. sternocleidomastoid using functional tests before and after treatment. Materials and Methods: The sample consisted of 21 individuals with temporomandibular joint dysfunction. Examinations were carried out before and after treatment using repositioning splint therapy and in seated/standing positions. Results: M. masseter - p=0.072 before treatment and p=0.821 after treatment. Symmetry is also maintained after treatment. After treatment, a significant difference is noted at the level of significance p<0.001 for the right chewing muscle. In seated and standing positions before treatment did not reveal a statistically significant difference (p=0.07, p=0.143) and after (p=0.272, p=0.623).M. sternocleidomastoid- p<0.001 when comparing right and left sides. After treatment, there was no difference between the right and left sides (p=0.169). No statistical difference was found when assessing indicators separately for the right and left muscles in seated and standing positions (p=0.304, p=0.611, p=0.089, p=0.869). When comparing the bioelectric potentials of the right muscle before, after treatment, a statistically significant difference was found p=0.001. Conclusions: Biostatistical analysis of the indicators of bioelectrical activity of m. masseter and sternocleidomastoid indicates no changes in muscle microvolt indicators with changes in body position in patients. However, repositioning splint therapy is associated with reduced muscle tone in initially more spasmodic muscles. It is worth noting that the symmetry of interaction between muscles improves.

High-resolution HLA genotyping in inclusion body myositis refines 8.1 ancestral haplotype association to DRB1*03:01:01 and highlights pathogenic role of arginine-74 of DRβ1 chain

ObjectivesInclusion body myositis (IBM) is a progressive inflammatory-degenerative muscle disease of older individuals, with some patients producing anti-cytosolic 5′-nucleotidase 1A (NT5C1A, aka cN1A) antibodies. Human Leukocyte Antigens (HLA) is the highest genetic risk factor for developing IBM. In this study, we aimed to further define the contribution of HLA alleles to IBM and the production of anti-cN1A antibodies. MethodsWe HLA haplotyped a Western Australian cohort of 113 Caucasian IBM patients and 112 ethnically matched controls using Illumina next-generation sequencing. Allele frequency analysis and amino acid alignments were performed using the Genentech/MiDAS bioinformatics package. Allele frequencies were compared using Fisher's exact test. Age at onset analysis was performed using the ggstatsplot package. All analysis was carried out in RStudio version 1.4.1717. ResultsOur findings validated the independent association of HLA-DRB1*03:01:01 with IBM and attributed the risk to an arginine residue in position 74 within the DRβ1 protein. Conversely, DRB4*01:01:01 and DQA1*01:02:01 were found to have protective effects; the carriers of DRB1*03:01:01 that did not possess these alleles had a fourteenfold increased risk of developing IBM over the general Caucasian population. Furthermore, patients with the abovementioned genotype developed symptoms on average five years earlier than patients without. We did not find any HLA associations with anti-cN1A antibody production. ConclusionsHigh-resolution HLA sequencing more precisely characterised the alleles associated with IBM and defined a haplotype linked to earlier disease onset. Identification of the critical amino acid residue by advanced biostatistical analysis of immunogenetics data offers mechanistic insights and future directions into uncovering IBM aetiopathogenesis.