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Nevin Manimala Statistics

Caffeine Habituation, not CYP1A2 Genotype, Modulates the Acute Effect of Caffeine on Exercise-Induced Hemostatic Responses in Adults with Obesity

Med Sci Sports Exerc. 2025 Jul 10. doi: 10.1249/MSS.0000000000003816. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to investigate how genotype and caffeine habituation influence the acute effects of caffeine ingestion on exercise-induced hemostatic responses in individuals with obesity.

METHODS: Using a randomized, double-blind, placebo-controlled crossover design, 40 physically inactive young men with obesity (age, 22.2 ± 2.3 years; BMI, 34.1 ± 2.7 kg/m2) completed two moderate-to-high-intensity concurrent exercise sessions following ingestion of caffeine (3 mg/kg) or placebo. Blood samples were collected at baseline, after exercise, and after 60 minutes of recovery. Statistical analysis was performed by repeated measures MANOVA.

RESULTS: Acute exercise increased platelet count and aggregation, fibrinogen, F1 + 2, tPA antigen, D-dimer, and clot lysis time, regardless of genotype or caffeine habituation status (P < 0.05). PAI-1 antigen remained unchanged after exercise (P > 0.05) but decreased following recovery (P < 0.01). Caffeine resulted in a greater increase in platelet aggregation, fibrinogen, F1 + 2, and clot lysis time, alongside a blunted increase in tPA antigen levels post-exercise in naïve consumers (P < 0.05). In contrast, habitual caffeine consumers exhibited a mitigated increase in clot lysis time and a greater post-recovery reduction in PAI-1 antigen following caffeine ingestion (P < 0.001). Caffeine’s impact on hemostatic responses to exercise was unaffected by genotype (P > 0.05).

CONCLUSIONS: Moderate-to-high-intensity concurrent exercise induces a transient prothrombotic state in physically inactive individuals with obesity. Acute caffeine supplementation at a moderate dose modulates the hemostatic responses depending on caffeine habituation status rather than CYP1A2 genotype: it exacerbates the prothrombotic response in naïve consumers but attenuates it in habitual consumers.

PMID:40638809 | DOI:10.1249/MSS.0000000000003816

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Nevin Manimala Statistics

Genetic Determinants of Leisure-Time Physical Activity in the Taiwanese Population: A Genome-Wide Association Study

Med Sci Sports Exerc. 2025 Jul 10. doi: 10.1249/MSS.0000000000003815. Online ahead of print.

ABSTRACT

BACKGROUND: Physical inactivity contributes to systemic disease burden and premature mortality worldwide. Leisure-time physical activity (LTPA) improves health outcomes; however, its genetic determinants, particularly in Asian populations, remain unclear. This study aimed to identify genetic loci associated with LTPA in the Taiwanese population.

METHODS: We conducted genome-wide association studies (GWASs) in 122,258 Taiwan Biobank participants. LTPA was assessed both as a binary trait (regular exerciser vs. non-exerciser) and an ordinal trait (categorized by MET-hours/week into low, moderate, and high PA levels). Logistic and ordinal logistic regression models were used under an additive genetic model, adjusting for age, age2, sex, BMI, smoking, and the first 10 genetic principal components. Candidate nonsynonymous mutations were further examined in 1,494 whole-genome sequenced participants.

RESULTS: Binary trait GWAS identified genome-wide significant (GWS) loci at ATXN2 (12q24.12), FTO (16q12.2), and NOTCH4 (6p21.32), with associations for FTO and NOTCH4 only observed in BMI-adjusted models. Ordinal trait analysis (MET-hours/week <10, 10-<20, ≥20) identified a single GWS locus at BRAP (12q24.12). Fine-mapping of 12q24.12 revealed multiple GWS single nucleotide polymorphisms (SNPs) in strong linkage disequilibrium (LD) with lead variants; these signals largely disappeared after conditional analysis, consistent with a single underlying association. Whole-genome sequencing and LD analysis identified three GWS nonsynonymous mutations, with ALDH2 rs671 emerging as the most likely causal variant.

CONCLUSIONS: ATXN2-ALDH2 region on chromosome 12q24.12 was identified as a key locus for LTPA in Taiwanese individuals. These findings enhance our understanding of the genetic basis of physical activity and may inform future precision medicine and public health strategies.

PMID:40638807 | DOI:10.1249/MSS.0000000000003815

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Associations Between Screen Time Use and Health Outcomes Among US Teenagers

Prev Chronic Dis. 2025 Jul 10;22:E38. doi: 10.5888/pcd22.240537.

ABSTRACT

INTRODUCTION: Associations between screen time and health outcomes among teenagers are well established. However, most studies use parent-reported information, which may misrepresent the magnitude or nature of these associations. In addition, timely nationally representative estimates are needed to correspond with evolving screen use. This study aimed to address these gaps by using data from a nationally representative survey of teenagers.

METHODS: Data came from the 2021-2023 National Health Interview Survey-Teen (NHIS-Teen), a follow-back web-based survey designed to collect health information directly from teenagers aged 12 to 17 years. NHIS-Teen provides a unique opportunity to assess teenagers’ self-reported health in conjunction with a rich set of parent-reported covariates, including family income, from the National Health Interview Survey. This study examines associations between high daily non-schoolwork screen time, defined as 4 or more hours of daily screen time, and adverse health outcomes across the domains of physical activity, sleep, weight, mental health, and perceived support.

RESULTS: Teenagers with higher non-schoolwork screen use were more likely to experience a series of adverse health outcomes, including infrequent physical activity, infrequent strength training, being infrequently well-rested, having an irregular sleep routine, weight concerns, depression symptoms, anxiety symptoms, infrequent social and emotional support, and insufficient peer support.

CONCLUSION: Results of this study include associations between high screen time and poor health among teenagers using self-reported data. Future work may further investigate these associations and their underlying mechanisms, including the content viewed on screens and the interactions taking place across screens.

PMID:40638804 | DOI:10.5888/pcd22.240537

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Nevin Manimala Statistics

Prevalence of diabetes and characteristics associated with poor diabetes outcomes among different migrant groups in Australia

Aust J Prim Health. 2025 Jul;31:PY25091. doi: 10.1071/PY25091.

ABSTRACT

Background Diabetes-associated morbidity and mortality are higher among some groups of migrants. However, differences in age structure between migrants and the destination countries’ populations can affect the estimated prevalence. There is also a lack of knowledge about the characteristics associated with poor diabetes outcomes among migrants. This study aimed to report the age-standardised prevalence of diabetes and characteristics associated with poor diabetes outcomes among different migrant groups in Australia, based on region of birth. Methods Using the whole population data from the Australian 2021 census data, diabetes age-specific and sex-specific prevalence, age-standardised prevalence and age-standardised prevalence ratio (ASPR) were calculated for people aged ≥30 years. Characteristics associated with poor diabetes outcomes were analysed. Results Age-standardised prevalence was higher than the Australian-born population among migrants from South-East Asia (ASPR: 1.4), North Africa and the Middle East (ASPR: 1.7), Southern and Central Asia (ASPR: 2.2) and Oceania (ASPR: 2.2). Among those with diabetes >50% had a weekly income 31% of individuals born in Australia, North-West Europe and Southern and Eastern Europe had ≥3 comorbidities. Over 37% of people born in Southern and Eastern Europe and North Africa and the Middle East needed assistance with core activities, and >8% born in Southern and Eastern Europe, North Africa and the Middle East and South-East Asia had no formal education. People born in Northeast Asia had the highest percentage of people with low English proficiency (55.2%) and who arrived in Australia Conclusion In prioritising the migrant populations for diabetes prevention, control and healthcare delivery, characteristics associated with poor diabetes outcomes and prevalence of diabetes in different migrant populations in Australia should be considered. Strategies should be designed based on the characteristics of different migrant populations to empower them to manage their diabetes.

PMID:40638791 | DOI:10.1071/PY25091

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Are differences in xylem vessel traits and their geographical variation among liana species related to the distribution patterns of climbing mechanisms in a temperate zone?

Ann Bot. 2025 Jul 10:mcaf138. doi: 10.1093/aob/mcaf138. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Xylem and vessel structures help to maintain water transport in woody plants in response to environmental changes. Many studies have demonstrated the relationships between vessel structures and, in particular, temperature. However, the effects of environmental factors on the of vessel size distribution and on vessels of different sizes have not been fully assessed statistically. Lianas are characterized by large vessels and vessel dimorphism. Liana abundance decreases with decreasing temperature; this pattern is attributed to the vulnerability of their large vessels to freeze-thaw embolism. However, in temperate zones, the relationships between liana abundance and temperature differ between twining and root climber.

METHODS: We sampled wood discs of eight liana species distributed across Japan and measured size and shape of 130,940 vessels in 836 sections from 219 individuals. We classified vessels of each species into two diameter clusters (large and small) and calculated vessel traits and potential hydraulic conductivity (Kp). Vessel traits were compared among climbing mechanisms and the relationships between vessel traits and temperature were analyzed for species.

KEY RESULTS: Twining climbers had larger vessel diameters than root climbers and had greater Kp. However, the relationships between temperature and vessel traits of species were inconsistent within climbing mechanisms. The decrease in Kp in certain species with decreasing temperature might result from species-specific changes in xylem structure. Vessels of the two clusters related differently to temperature in some species.

CONCLUSIONS: The vessel traits in each climbing mechanism might partly explain the distribution patterns of these lianas in the study region. Furthermore, changes in Kp in some species supported the prediction that liana competitiveness decreases with decreasing temperature. Understanding the mechanisms behind the changes in vessel traits and vessel size distribution along environmental factors will provide fundamental insights into how environmental changes affect forest ecosystems by altering plant hydraulic function.

PMID:40638780 | DOI:10.1093/aob/mcaf138

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Predicting Drug-Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach

JMIR Med Inform. 2025 Jul 10;13:e67513. doi: 10.2196/67513.

ABSTRACT

BACKGROUND: Adverse drug reactions (ADRs) pose serious risks to patient health, and effectively predicting and managing them is an important public health challenge. Given the complexity and specificity of biomedical text data, the traditional context-independent word embedding model, Word2Vec, has limitations in fully reflecting the domain specificity of such data. Although Bidirectional Encoder Representations from Transformers (BERT)-based models pretrained on biomedical corpora have demonstrated high performance in ADR-related studies, research using these models to predict previously unknown drug-side effect relationships remains insufficient.

OBJECTIVE: This study proposes a method for predicting drug-side effect relationships by leveraging the parametric knowledge embedded in biomedical BERT models. Through this approach, we predict promising candidates for potential drug-side effect relationships with unknown causal mechanisms by leveraging parametric knowledge from biomedical BERT models and embedding vector similarities of known relationships.

METHODS: We used 158,096 pairs of drug-side effect relationships from the side effect resource (SIDER) database to generate an adjacency matrix and calculate the cosine similarity between word embedding vectors of drugs and side effects. Relation scores were calculated for 8,235,435 drug-side effect pairs using this similarity. To evaluate the prediction accuracy of drug-side effect relationships, the area under the curve (AUC) value was measured using the calculated relation score and 158,096 known drug-side effect relationships from SIDER.

RESULTS: The clagator/biobert_v1.1 model achieved an AUC of 0.915 at an optimal threshold of 0.289, outperforming the existing Word2Vec model with an AUC of 0.848. The BERT-based models pretrained on the biomedical corpus outperformed the vanilla BERT model with an AUC of 0.857. External validation with the FDA (Food and Drug Administration) Adverse Event Reporting System data, using Fisher exact test based on 8,235,435 predicted drug-side effect pairs and 901,361 known relationships, confirmed high statistical significance (P<.001) with an odds ratio of 4.822. In addition, a literature review of predicted drug-side effect relationships not confirmed in the SIDER database revealed that these relationships have been reported in recent studies published after 2016.

CONCLUSIONS: This study introduces a method for extracting drug-side effect relationships embedded in parameters of language models pretrained on biomedical corpora and using this information to predict previously unknown drug-side effect relationships. We found that BERT-based models pretrained with biomedical corpora consider contextual information and achieve better performance in drug-side effect relationship prediction. External validation using the FDA Adverse Event Reporting System dataset and the literature review of certain cases confirmed high statistical significance, demonstrating practical applicability. These results highlight the utility of natural language processing-based approaches for predicting and managing ADR.

PMID:40638775 | DOI:10.2196/67513

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Nevin Manimala Statistics

Classification of commercial districts based on predicting the survival rate of food service market in Seoul

PLoS One. 2025 Jul 10;20(7):e0326307. doi: 10.1371/journal.pone.0326307. eCollection 2025.

ABSTRACT

The chronic small-business closure has emerged as a critical economic issue in South Korea, considering more than half of businesses have been closed within three years. While some previous literature has analyzed the causes and their negative impacts on the economy, there is still a lack of studies on understanding the pattern dynamics and predicting future possible closure scenarios due to the lack of appropriate data. Using 3,000,000 individual commercial facility data from 2004 to 2018 in Seoul, Korea, the primary purpose of this research is two-fold: (1) to develop a methodological framework to simulate survival rate pattern change using a deep-learning based model and (2) to reclassify the commercial districts based on the prediction outcomes to 8 survival rate change types. The results indicate that the LSTM model can be useful in predicting and simulating the survival rate of commercial facilities. Moreover, the CBD area showed a decrease in the survival rate in the future, and the commercial districts around university districts and IT industry clusters were divided into commercial districts with an increased survival rate in the future. The results of this study are expected to be used as quantitative evidence for more direct and realistic policy establishment.

PMID:40638714 | DOI:10.1371/journal.pone.0326307

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Scalable emulation of protein equilibrium ensembles with generative deep learning

Science. 2025 Jul 10:eadv9817. doi: 10.1126/science.adv9817. Online ahead of print.

ABSTRACT

Following the sequence and structure revolutions, predicting functionally relevant protein structure changes at scale remains an outstanding challenge. We introduce BioEmu, a deep learning system that emulates protein equilibrium ensembles by generating thousands of statistically independent structures per hour on a single GPU. BioEmu integrates over 200 milliseconds of molecular dynamics (MD) simulations, static structures and experimental protein stabilities using novel training algorithms. It captures diverse functional motions-including cryptic pocket formation, local unfolding, and domain rearrangements-and predicts relative free energies with 1 kcal/mol accuracy compared to millisecond-scale MD and experimental data. BioEmu provides mechanistic insights by jointly modelling structural ensembles and thermodynamic properties. This approach amortizes the cost of MD and experimental data generation, demonstrating a scalable path toward understanding and designing protein function.

PMID:40638710 | DOI:10.1126/science.adv9817

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Nevin Manimala Statistics

Suspected infection of unclear origin at the emergency department: diagnostic yield of thoraco-abdominal-pelvic CT in non-severe patients

Br J Radiol. 2025 Jul 10:tqaf150. doi: 10.1093/bjr/tqaf150. Online ahead of print.

ABSTRACT

OBJECTIVES: To assess the diagnostic yield of thoraco-abdominal-pelvic CT (TAP-CT) in suspected infection of unclear origin in the Emergency Department (ED) and identify predictive factors for normal TAP-CT to optimize its use.

METHODS: We retrospectively categorized 517 TAP-CT studies of adult patients with non-severe infection of unclear origin based on the presence of an infectious focus or significant findings, such as neoplasia or thrombosis. Descriptive analysis, correspondence assessment between CT results and final diagnosis, and statistical modeling were performed to identify predictors of normal TAP-CT.

RESULTS: An infectious focus was identified in 55% TAP-CT scans, mainly pulmonary (46%), bilio-digestive (25%), and genitourinary (23%). Significant noninfectious findings were detected in 20%, including thrombosis (7%) and neoplasia (12%). TAP-CT showed a sensitivity of 73%, specificity of 88%, PPV of 94%, and NPV of 57%, with moderate agreement (Kappa= 0.53) between TAP-CT findings and final diagnosis. Overall, 67% of patients had an identifiable cause for infection-like symptoms. Although C-reactive protein <128 mg/L was associated with normal TAP-CT, no model reliably predicted a normal scan.

CONCLUSIONS: TAP-CT identified a relevant finding in over two-thirds of cases, reinforcing its role in diagnosing both infectious and mimicking conditions. No specific criteria could safely exclude TAP-CT, making it a valuable tool for managing patients with suspected infections of unclear origin.

ADVANCES IN KNOWLEDGE: This study is the first to assess TAP-CT’s value in suspected non-severe infections of unclear origin in the ED, highlighting its role in detecting infectious and noninfectious conditions and optimizing diagnostic strategies.

PMID:40638233 | DOI:10.1093/bjr/tqaf150

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Nevin Manimala Statistics

The Effects of the Course on Technology Use in Nursing on Students’ Self-directed Learning Readiness and Attitudes Toward Technology

Comput Inform Nurs. 2025 Jul 9. doi: 10.1097/CIN.0000000000001358. Online ahead of print.

ABSTRACT

This study aimed to examine the effect of the course on technology use in nursing on students’ readiness for self-directed learning and attitudes toward technology. This was a quasi-experimental study. The study involved 109 first-year nursing students assigned to the intervention group (n = 53) and the control group (n = 56). Whereas the intervention group participated in the course on technology use in nursing, the control group participated in health assessment course. Data were collected with the Student Information Form, the Readiness of Self-directed Learning Scale, and Technology Attitudes Survey between March and May 2024. There was a statistically significant difference between the self-directed learning readiness and attitudes toward technology scores of the intervention and control groups (P < .05). It was found that both the self-directed learning readiness and positive attitudes toward technology scores of the students in the intervention group were significantly higher than the control group (P < .05). The study’s results indicated that course on technology use in nursing improved students’ self-directed learning readiness and positive attitudes toward technology. The integration of technology-based interventions into nursing curriculum is recommended.

PMID:40638231 | DOI:10.1097/CIN.0000000000001358