Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST.

BACKGROUND: The completeness of adverse event (AE) reports, crucial for assessing putative causal relationships, is measured using the vigiGrade completeness score in VigiBase, the World Health Organization global database of reported potential AEs. Malaysian reports have surpassed the global average score (approximately 0.44), achieving a 5-year average of 0.79 (SD 0.23) as of 2019 and approaching the benchmark for well-documented reports (0.80). However, the contributing factors to this relatively high report completeness score remain unexplored. OBJECTIVE: This study aims to explore the main drivers influencing the completeness of Malaysian AE reports in VigiBase over a 15-year period using vigiGrade. A secondary objective was to understand the strategic measures taken by the Malaysian authorities leading to enhanced report completeness across different time frames. METHODS: We analyzed 132,738 Malaysian reports (2005-2019) recorded in VigiBase up to February 2021 split into historical International Drug Information System (INTDIS; n=63,943, 48.17% in 2005-2016) and newer E2B (n=68,795, 51.83% in 2015-2019) format subsets. For machine learning analyses, we performed a 2-stage feature selection followed by a random forest classifier to identify the top features predicting well-documented reports. We subsequently applied tree Shapley additive explanations to examine the magnitude, prevalence, and direction of feature effects. In addition, we conducted time-series analyses to evaluate chronological trends and potential influences of key interventions on reporting quality. RESULTS: Among the analyzed reports, 42.84% (56,877/132,738) were well documented, with an increase of 65.37% (53,929/82,497) since 2015. Over two-thirds (46,186/68,795, 67.14%) of the Malaysian E2B reports were well documented compared to INTDIS reports at 16.72% (10,691/63,943). For INTDIS reports, higher pharmacovigilance center staffing was the primary feature positively associated with being well documented. In recent E2B reports, the top positive features included reaction abated upon drug dechallenge, reaction onset or drug use duration of <1 week, dosing interval of <1 day, reports from public specialist hospitals, reports by pharmacists, and reaction duration between 1 and 6 days. In contrast, reports from product registration holders and other health care professionals and reactions involving product substitution issues negatively affected the quality of E2B reports. Multifaceted strategies and interventions comprising policy changes, continuity of education, and human resource development laid the groundwork for AE reporting in Malaysia, whereas advancements in technological infrastructure, pharmacovigilance databases, and reporting tools concurred with increases in both the quantity and quality of AE reports. CONCLUSIONS: Through interpretable machine learning and time-series analyses, this study identified key features that positively or negatively influence the completeness of Malaysian AE reports and unveiled how Malaysia has developed its pharmacovigilance capacity via multifaceted strategies and interventions. These findings will guide future work in enhancing pharmacovigilance and public health.

Competing risks data in clinical oncology.

Competing risks data analysis plays a critical role in the evaluation of clinical utility of specific cancer treatments and can inform the development of future treatment approaches. Although competing risks data are ubiquitous in cancer studies, competing risks data are infrequently recognized and competing risks data analysis is not commonly performed. Consequently, efficacy of specific treatments is often incompletely and inaccurately presented and thus study results may be interpreted improperly. In the present article, we aim to enhance awareness of competing risks data and provide a general overview and guidance on competing risks data and its analysis using cancer clinical studies.

Neonatal brain inflammation enhances methamphetamine-induced reinstated behavioral sensitization in adult rats analyzed with explainable machine learning.

Neonatal brain inflammation produced by intraperitoneal (i.p.) injection of lipopolysaccharide (LPS) results in long-lasting brain dopaminergic injury and motor disturbances in adult rats. The goal of the present work is to investigate the effect of neonatal systemic LPS exposure (1 or 2 mg/kg, i.p. injection in postnatal day 5, P5, male rats)-induced dopaminergic injury to examine methamphetamine (METH)-induced behavioral sensitization as an indicator of drug addiction. On P70, subjects underwent a treatment schedule of 5 once daily subcutaneous (s.c.) administrations of METH (0.5 mg/kg) (P70-P74) to induce behavioral sensitization. Ninety-six hours following the 5th treatment of METH (P78), the rats received one dose of 0.5 mg/kg METH (s.c.) to reintroduce behavioral sensitization. Hyperlocomotion is a critical index caused by drug abuse, and METH administration has been shown to produce remarkable locomotor-enhancing effects. Therefore, a random forest model was used as the detector to extract the feature interaction patterns among the collected high-dimensional locomotor data. Our approaches identified neonatal systemic LPS exposure dose and METH-treated dates as features significantly associated with METH-induced behavioral sensitization, reinstated behavioral sensitization, and perinatal inflammation in this experimental model of drug addiction. Overall, the analysis suggests that the implementation of machine learning strategies is sensitive enough to detect interaction patterns in locomotor activity. Neonatal LPS exposure also enhanced METH-induced reduction of dopamine transporter expression and [3H]dopamine uptake, reduced mitochondrial complex I activity, and elevated interleukin-1ß and cyclooxygenase-2 concentrations in the P78 rat striatum. These results indicate that neonatal systemic LPS exposure produces a persistent dopaminergic lesion leading to a long-lasting change in the brain reward system as indicated by the enhanced METH-induced behavioral sensitization and reinstated behavioral sensitization later in life. These findings indicate that early-life brain inflammation may enhance susceptibility to drug addiction development later in life, which provides new insights for developing potential therapeutic treatments for drug addiction.

Using Risk Ratios to Quantify Potential Behavior-Environment Relations.

Behavior-environment functional relations are the units of explanation in applied behavior analysis (ABA). Whether hypothesized experimentally or descriptively, quantification of putative functional relations improves our ability to predict and influence behavior. Risk ratios are an accessible, straightforward quantitative analysis that can serve this purpose. They have been employed to great effect in other fields (e.g., medicine, public health), but are rarely used within ABA. In this tutorial, we describe risk ratios and how they are calculated, discuss why risk ratios are well suited for quantifying behavior-environment relations, and illustrate their utility and applicability across five demonstrations from real clinical cases. Recommendations for the use of risk ratios in research and practice are discussed.

Homicide numbers, rates, and victims' characteristics in the Tri-City metropolitan area, Poland, between 2010 and 2019. / Liczby i wskazniki zabójstw oraz charakterystyka ofiar na terenie aglomeracji Trójmiasta w latach 2010-2019.

Despite a clear global downward trend, homicides still account for a relatively high proportion of all violent deaths, making them a serious problem both in Poland and worldwide. The discrepancy in available data prompted the authors of the study to analyse the numbers and rates of homicides and the characteristics of the homicide victims in the Tri-City area of northern Poland. The study was based on data from autopsy reports, supplemented by information from prosecutor's files on all homicides in the Tri-City area between 2010 and 2019. A total of 107 homicides were statistically analysed for age, sex, blood alcohol concentration at the time of death, time and place of death. The annual homicide rate was 1.24 per 100,000 inhabitants, with a clear downward trend over the period analysed. The average age of victims was about 48 years, and the majority of victims were male (70.1%). 92.5% of homicides were committed in the Tri-City, with a clear predominance of Gdansk (49.5%) over other, mostly rural, areas of the analysed agglomeration. The majority of victims (57.8%) whose blood alcohol concentration was measured were intoxicated, with a clear predominance of males in this group (70.9%). Victim characteristics and the homicide rates obtained from the analysed material were similar to other countries in Central and Eastern Europe, which may be related to historical, cultural, and demographic similarities. The study highlights the significant impact of alcohol abuse on the risk of homicide.

[Clinical Characteristics Analysis of 1051 Cases of Cleft Lip With/Without Cleft Palate in the Ethnic Tibetan Population]. / 1051ä¾‹è—æ—å”‡è ­è£‚æ‚£è€ ä¸´åºŠç‰¹å¾åˆ†æž.

Objective: To examine the characteristics of the prevalence of congenital cleft lip with/without cleft palate in the ethnic Tibetan population and to provide support for the precise prevention and treatment of cleft lip with/without cleft palate in the Tibetan population. Methods: The clinical data of Tibetan patients with cleft lip with/without cleft palate were collected and the clinical characteristics of the patients were analyzed. The patients' age ranged from 2 months to 51 years old. All the subjects were admitted to West China Stomatology Hospital, Sichuan University for the treatment of cleft lip with/without cleft palate between January 2016 and August 2023. Most of the subjects came from Sichuan Province and the Tibet Autonomous Region. Results: A total of 1051 patients were enrolled and children aged under 12 months (460 cases) accounted for the largest proportion. Among the subjects, 383 had cleft lip only (36.44%), 140 had cleft palate only (13.32%), and 528 had cleft lip with cleft palate (50.24%). The male-to-female ratios of patients with cleft lip only (0.99∶1), cleft palate only (0.54∶1), and cleft lip with cleft palate (1.67∶1) exhibited significant differences (P<0.001). However, there was no significant difference in the male-to-female ratio in patients with cleft lip only or those with cleft lip with cleft palate when the subjects were divided into two groups according to whether they had unilateral or bilateral cleft lip with/without cleft palate. Most of the patients with bilateral cleft lip were female, while most of the patients with unilateral cleft lip and unilateral or bilateral cleft lip with cleft palate were male. The unilateral cleft lip with/without cleft palate was located predominantly on the left side. Syndromic cleft lip with/without cleft palate accounted for 3.43% of all the cases and the most common concomitant deformity was congenital heart disease. 3.81% (40 cases) of the patients had a family history. In the patients with cleft lip only and those with cleft palate only, the proportion of patients having parents with corresponding phenotypes was higher than those of other phenotypes of cleft lip with/without cleft palate. Regarding the birth time distribution of the children with cleft lip with/without cleft palate, Spring saw the highest number of births of these children (311 cases, 29.59%), while Winter saw the lowest number of births (231 cases, 21.98%). Conclusion: The cases of cleft lip with/without cleft palate in the ethnic Tibetan population are predominantly cleft lip and palate. Unilateral cleft lip only or cleft lip with palate is predominantly located on the left side. Lip disease phenotypes may be more heritable.

Global Measles Surveillance: Trends, Challenges, and Implications for Public Health Interventions.

Measles, a highly contagious disease primarily affecting children, carries serious health risks, including complications and mortality. Vaccination remains the most effective preventive measure against measles transmission. The COVID-19 pandemic has exacerbated challenges in surveillance and immunization efforts, leaving millions of people exposed to preventable diseases such as measles. Globally accelerated immunization campaigns are critical for achieving regional elimination goals and mitigating the risk of outbreaks. Our team has developed an open-access database for global measles monitoring, facilitating standardized data collection and analysis. The analysis of measles cases from 2011 to 2023 reveals fluctuating trends, with notable increases in Africa in 2019 and 2023, indicating potential gaps in control strategies. Using an automated signal detection tool developed by the European Centre for Disease Prevention and Control (ECDC) team, we identified significant variations between World Health Organization (WHO) regions, underscoring the importance of continuous monitoring to detect epidemiological changes early. These results underscore the need for robust surveillance systems and accelerated vaccination efforts to safeguard public health.

Studying Venom Toxin Variation Using Accurate Masses from Liquid Chromatography-Mass Spectrometry Coupled with Bioinformatic Tools.

This study provides a new methodology for the rapid analysis of numerous venom samples in an automated fashion. Here, we use LC-MS (Liquid Chromatography-Mass Spectrometry) for venom separation and toxin analysis at the accurate mass level combined with new in-house written bioinformatic scripts to obtain high-throughput results. This analytical methodology was validated using 31 venoms from all members of a monophyletic clade of Australian elapids: brown snakes (Pseudonaja spp.) and taipans (Oxyuranus spp.). In a previous study, we revealed extensive venom variation within this clade, but the data was manually processed and MS peaks were integrated into a time-consuming and labour-intensive approach. By comparing the manual approach to our new automated approach, we now present a faster and more efficient pipeline for analysing venom variation. Pooled venom separations with post-column toxin fractionations were performed for subsequent high-throughput venomics to obtain toxin IDs correlating to accurate masses for all fractionated toxins. This workflow adds another dimension to the field of venom analysis by providing opportunities to rapidly perform in-depth studies on venom variation. Our pipeline opens new possibilities for studying animal venoms as evolutionary model systems and investigating venom variation to aid in the development of better antivenoms.

Mining and analysis of security alert signals of valbenazine based on the Food and Drug Administration Adverse Event Reporting System database.

BACKGROUND: Valbenazine is used for tardive movement disorders in adults. Current studies on its safety are mostly from clinical trials and small case reports, limiting information on rare adverse reactions. This study investigated valbenazine-related adverse event (AE) risk signals using the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. METHODS: Valbenazine AEs data were collected from the FAERS database from 2017 Q2 to 2023 Q1, employing methods like reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network, and empirical Bayesian geometric mean. RESULTS: After data cleaning and drug screening, there were 20,837 AEs primarily suspecting valbenazine, involving 26 system organ classes and 125 AEs related to valbenazine at the preferred terms level. AEs related to valbenazine were mainly concentrated in nervous system disorders, general disorders and administration site conditions, and psychiatric disorders. Eye disorders and gastrointestinal disorders are new AEs not labeled in the valbenazine instructions. In addition, some new potential AE signals were found, such as Tardive dyskinesia and eyelid function disorder. CONCLUSION: The common AEs of valbenazine in the real world are consistent with the instructions, but there are some newly discovered suspicious AEs.

Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology.

Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative, and increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.

Why an integrated view of gene expression studies on hematopoiesis in mouse aging is better than the sum of their parts.

Globally, the human population is aging, with an increased proportion of people in "old age" (over 60 years). This trend leads to a growing demand in aging research, stimulating studies in animal models such as mice, fish, and invertebrates. Recently, we published a research summary on the aging of hematopoietic stem cells (HSCs) in C57BL/6 mice based on 12 gene expression datasets. Here, I discuss in greater detail the added value of taking an integrated view, rather than considering each publication separately, to determine genes involved in aging. Considerable variation exists between lists of differentially expressed (DE) genes in HSCs, comparing young and old mice. This variation can result from factors such as inconsistent definitions of "young" and "old", technical variations and variations between laboratory mouse strains. We previously demonstrated that the variation between gene lists could be circumvented by forming a unified list of DE genes-the "aging list"-with citation indexes attached. The most frequently detected DE genes [approximately 200 most cited, which we named the "aging signature" (AS)] were highly consistent across publications. Gene Ontology classification of the AS list identified additional sources of variation between studies: one comes from the specifics of how the data are collected and analyzed; another comes from inconsistencies between how we define the gene categories. As discussed, overcoming these variations is the next challenge toward an integral approach to our systematic knowledge of the aging process.

Data analysis of the influence of microstructure, composition, and loading conditions on stress corrosion cracking behavior of Mg alloys.

Stress corrosion cracking (SCC) can be a crucial problem in applying rare earth (RE) Magnesium alloys in environments where mechanical loads and electrochemical driven degradation processes interact. It has been proven already that the SCC behavior is associated with microstructural features, compositions, loading conditions, and corrosive media, especially in-vivo. However, it is still unclear when and how mechanisms acting on multiple scales and respective system descriptors predictable contribute to SCC for the wide set of existing Mg alloys. In the present work, suitable literature data along SCC of Mg alloys has been analyzed to enable the development of a reliable SCC model for MgGd binary alloys. Pearson correlation coefficient and linear fitting are utilized to describe the contribution of selected parameters to corrosion and mechanical properties. Based on our data analysis, a parameter ranking is obtained, providing information on the SCC impact with regard to ultimate tensile strength (UTS) and fracture elongation of respective materials. According to the analyzed data, SCC susceptibility can be grouped and mapped onto Ashby type diagrams for UTS and elongation of respective base materials tested in air and in corrosive media. The analysis reveals the effect of secondary phase content as a crucial materials descriptor for our analyzed materials and enables better understanding towards SCC model development for Mg-5Gd alloy based implant.

Enricherator: A Bayesian Method for Inferring Regularized Genome-wide Enrichments from Sequencing Count Data.

A pervasive question in biological research studying gene regulation, chromatin structure, or genomics is where, and to what extent, does a signal of interest arise genome-wide? This question is addressed using a variety of methods relying on high-throughput sequencing data as their final output, including ChIP-seq for protein-DNA interactions,1 GapR-seq for measuring supercoiling,2 and HBD-seq or DRIP-seq for R-loop positioning.3,4 Current computational methods to calculate genome-wide enrichment of the signal of interest usually do not properly handle the count-based nature of sequencing data, they often do not make use of the local correlation structure of sequencing data, and they do not apply any regularization of enrichment estimates. This can result in unrealistic estimates of the true underlying biological enrichment of interest, unrealistically low estimates of confidence in point estimates of enrichment (or no estimates of confidence at all), unrealistic gyrations in enrichment estimates at very close (<10 bp) genomic loci due to noise inherent in sequencing data, and in a multiple-hypothesis testing problem during interpretation of genome-wide enrichment estimates. We developed a tool called Enricherator to infer genome-wide enrichments from sequencing count data. Enricherator uses the variational Bayes algorithm to fit a generalized linear model to sequencing count data and to sample from the approximate posterior distribution of enrichment estimates (https://github.com/jwschroeder3/enricherator). Enrichments inferred by Enricherator more precisely identify known binding sites in cases where low coverage between binding sites leads to false-positive peak calls in these noisy regions of the genome; these benefits extend to published datasets.

The relationship between joint kinematic patterns during single-leg drop landing and perceived instability in individuals with chronic ankle instability.

BACKGROUND: Perceived instability is a primary symptom among individuals with chronic ankle instability. However, the relationship between joint kinematics during landing remains unclear. Therefore, we investigated the relationships between landing kinematics and perceived instability in individuals with chronic ankle instability. METHODS: In 32 individuals with chronic ankle instability, we recorded ankle, knee, and hip joint angles during a single-leg drop landing. Joint angle waveforms during 200 ms before and after initial contact were summarized into single values using two methods: peak joint angles and principal component scores via principal component analysis. Using Spearman's rank correlation coefficient (ρ), we examined the relationships of peak joint angles and principal component scores with the Cumberland Ankle Instability Tool score, with a lower score indicating a greater perceived instability (α = 0.05). FINDINGS: The second principal component scores of ankle angle in the horizontal and sagittal planes significantly correlated with the Cumberland Ankle Instability Tool score (Horizontal: ρ = 0.507, P = 0.003; Sagittal: ρ = -0.359, P = 0.044). These scores indicated the differences in the magnitude of angles before and after landing. Significant correlations indicated a greater perceived instability correlated with smaller internal rotation and plantarflexion before landing and smaller external rotation and dorsiflexion after landing. In contrast, no peak joint angles correlated with the Cumberland Ankle Instability Tool score (P > 0.05). INTERPRETATION: In individuals with chronic ankle instability, ankle movements during landing associated with perceived instability may be a protective strategy before landing and potentially cause ankle instability after landing.

Exploring gene regulation and biological processes in insects: Insights from omics data using gene regulatory network models.

Gene regulatory network (GRN) comprises complicated yet intertwined gene-regulator relationships. Understanding the GRN dynamics will unravel the complexity behind the observed gene expressions. Insect gene regulation is often complicated due to their complex life cycles and diverse ecological adaptations. The main interest of this review is to have an update on the current mathematical modelling methods of GRNs to explain insect science. Several popular GRN architecture models are discussed, together with examples of applications in insect science. In the last part of this review, each model is compared from different aspects, including network scalability, computation complexity, robustness to noise and biological relevancy.

Integrated bioinformatics analysis identified leucine rich repeat containing 15 and secreted phosphoprotein 1 as hub genes for calcific aortic valve disease and osteoarthritis.

Calcific aortic valve disease (CAVD) and osteoarthritis (OA) are common diseases in the ageing population and share similar pathogenesis, especially in inflammation. This study aims to discover potential diagnostic and therapeutic targets in patients with CAVD and OA. Three CAVD datasets and one OA dataset were obtained from the Gene Expression Omnibus database. We used bioinformatics methods to search for key genes and immune infiltration, and established a ceRNA network. Immunohistochemical staining was performed to verify the expression of candidate genes in human and mice aortic valve tissues. Two key genes obtained, leucine rich repeat containing 15 (LRRC15) and secreted phosphoprotein 1 (SPP1), were further screened using machine learning and verified in human and mice aortic valve tissues. Compared to normal tissues, the infiltration of immune cells in CAVD tissues was significantly higher, and the expressions of LRRC15 and SPP1 were positively correlated with immune cells infiltration. Moreover, the ceRNA network showed extensive regulatory interactions based on LRRC15 and SPP1. The authors' findings identified LRRC15 and SPP1 as hub genes in immunological mechanisms during CAVD and OA initiation and progression, as well as potential targets for drug development.

Analysis of post-market adverse events of istradefylline: a real-world study base on FAERS database.

Analyze the adverse event (AE) signals of istradefylline based on the FAERS database. By extracting large-scale data from the FAERS database, this study used various signal quantification techniques such as ROR, PRR, BCPNN, and MGPS to calculate and evaluate the ratio and association between istradefylline and specific AEs. In the FAERS database, this study extracted data from the third quarter of 2019 to the first quarter of 2023, totaling 6,749,750 AE reports. After data cleansing and drug screening, a total of 3633 AE reports related to istradefylline were included for analysis. Based on four calculation methods, this study unearthed 25 System Organ Class (SOC) AE signals and 82 potential preferred terms (PTs) related to istradefylline. The analysis revealed new AEs during istradefylline treatment, including reports of Parkinsonism hyperpyrexia syndrome (n = 3, ROR 178.70, PRR 178.63, IC 1.97, EBGM 165.63), Compulsions (n = 5, ROR 130.12, PRR 130.04, IC 2.53, EBGM 123.02), Deep brain stimulation (n = 10, ROR 114.42, PRR 114.27, IC 3.33, EBGM 108.83), and Freezing phenomenon (n = 60, ROR 97.52, PRR 96.76, IC 5.21, EBGM 92.83). This study provides new risk signals and important insights into the use of istradefylline, but further research and validation are needed, especially for those AE that may occur in actual usage scenarios but are not yet explicitly described in the instructions.

A survey into the current fitness testing practices of elite male soccer practitioners: from assessment to communicating results.

This study provides insight into the current fitness testing practices in elite male soccer. One hundred and two practitioners from professional soccer leagues across 24 countries completed an online survey comprising 29 questions, with five sections: a) background information, b) testing selection, c) testing implementation, d) data analysis, and e) data reporting. Frequency analysis was used to evaluate the responses to fixed response questions and thematic analysis was used for open-ended questions to generate clear and distinct themes. Strength (85%) and aerobic capacity (82%) represent the most frequently assessed physical qualities. Scientific literature (80%) is the most influential factor in testing selection and practitioners conduct fitness testing less frequently than their perceived ideal frequency per season (3.6 ± 2 vs. 4.5 ± 2). Time and competitive schedule were the greatest barriers to fitness testing administration. Practitioners mostly used a 'hybrid' approach (45%) to fitness testing, blending 'traditional' (i.e., a day dedicated to testing) and 'integrated' (i.e., testing within regular training sessions) methods. Microsoft Excel is the most used software for data analysis (95%) and visualization (79%). An equal use of the combination of best and mean scores of multiple trials (44%) and the best score (42%) was reported. Comparing a player's test performance with previous scores (89%) was the most common method for interpreting test results. However, only 38% considered measurement error. Digital displays and verbal feedback are the most common data reporting methods, with different data reporting processes for coaches and players. Practitioners can use data and findings from this study to inform their current testing practices and researchers to further identify areas for investigation, with the overarching aim of developing the field of fitness testing in elite male soccer.

Analyzing omics data based on sample network.

Identifying valuable features from complex omics data is of great significance for disease diagnosis study. This paper proposes a new feature selection algorithm based on sample network (FS-SN) to mine important information from omics data. The sample network is constructed according to the sample neighbor relationship at the molecular (feature) expression level, and the distinguishing ability of the feature is evaluated based on the topology of the sample network. The sample network established on a feature with a strong discriminating ability tends to have many edges between the same group samples and few edges between the different group samples. At the same time, FS-SN removes redundant features according to the gravitational interaction between features. To show the validation of FS-SN, it was compared on ten public datasets with ERGS, mRMR, ReliefF, ATSD-DN, and INDEED which are efficient in omics data analysis. Experimental results show that FS-SN performed better than the compared methods in accuracy, sensitivity and specificity in most cases. Hence, FS-SN making use of the topology of the sample network is effective for analyzing omics data, it can identify key features that reflect the occurrence and development of diseases, and reveal the underlying biological mechanism.

Utilizing the Glucose and Insulin Response Shape of an Oral Glucose Tolerance Test to Predict Dysglycemia in Children with Overweight and Obesity, Ages 8-18 Years.

Common dysglycemia measurements including fasting plasma glucose (FPG), oral glucose tolerance test (OGTT)-derived 2 h plasma glucose, and hemoglobin A1c (HbA1c) have limitations for children. Dynamic OGTT glucose and insulin responses may better reflect underlying physiology. This analysis assessed glucose and insulin curve shapes utilizing classifications-biphasic, monophasic, or monotonically increasing-and functional principal components (FPCs) to predict future dysglycemia. The prospective cohort included 671 participants with no previous diabetes diagnosis (BMI percentile ≥ 85th, 8-18 years old); 193 returned for follow-up (median 14.5 months). Blood was collected every 30 min during the 2 h OGTT. Functional data analysis was performed on curves summarizing glucose and insulin responses. FPCs described variation in curve height (FPC1), time of peak (FPC2), and oscillation (FPC3). At baseline, both glucose and insulin FPC1 were significantly correlated with BMI percentile (Spearman correlation r = 0.22 and 0.48), triglycerides (r = 0.30 and 0.39), and HbA1c (r = 0.25 and 0.17). In longitudinal logistic regression analyses, glucose and insulin FPCs predicted future dysglycemia (AUC = 0.80) better than shape classifications (AUC = 0.69), HbA1c (AUC = 0.72), or FPG (AUC = 0.50). Further research should evaluate the utility of FPCs to predict metabolic diseases.