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This longitudinal cohort study aimed to create a novel prediction model for cardiovascular death with lifestyle factors. Subjects aged 40-74 years in the Japanese nationwide Specific Health Checkup Database in 2008 were included. Subjects were randomly assigned to the derivation and validation cohorts by a 2:1 ratio. Points for the prediction model were determined using regression coefficients that were derived from the Cox proportional hazards model in the derivation cohort. Models 1 and 2 were developed using known risk factors and known factors with lifestyle factors, respectively. The models were validated by comparing Kaplan-Meier curves between the derivation and validation cohorts, and by calibration plots in the validation cohort. Among 295,297 subjects, data for 120,823 were available. There were 310 cardiovascular deaths during a mean follow-up of 3.6 years. Model 1 included known risk factors. In model 2, weight gain, exercise habit, gait speed, and drinking alcohol were additionally included as protective factors. Kaplan-Meier curves matched better between the derivation and validation cohorts in model 2, and model 2 was better calibrated. In conclusion, our prediction model with lifestyle factors improved the predictive ability for cardiovascular death.
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http://dx.doi.org/10.1038/s41598-019-49003-5 | DOI Listing |
Eur J Radiol
September 2025
Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China. Electronic address:
Purpose: The present study aimed to develop a noninvasive predictive framework that integrates clinical data, conventional radiomics, habitat imaging, and deep learning for the preoperative stratification of MGMT gene promoter methylation in glioma.
Materials And Methods: This retrospective study included 410 patients from the University of California, San Francisco, USA, and 102 patients from our hospital. Seven models were constructed using preoperative contrast-enhanced T1-weighted MRI with gadobenate dimeglumine as the contrast agent.
Biomol Biomed
September 2025
Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China.
Coronary heart disease (CHD) is a leading cause of morbidity and mortality; patients with type 2 diabetes mellitus (T2DM) are at particularly high risk, highlighting the need for reliable biomarkers for early detection and risk stratification. We investigated whether combining the stress hyperglycemia ratio (SHR) and systemic inflammation response index (SIRI) improves CHD detection in T2DM. In this retrospective cohort of 943 T2DM patients undergoing coronary angiography, associations of SHR and SIRI with CHD were evaluated using multivariable logistic regression and restricted cubic splines; robustness was examined with subgroup and sensitivity analyses.
View Article and Find Full Text PDFJ Org Chem
September 2025
State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, P. R. China.
The Buchwald-Hartwig (B-H) reaction graph, a novel graph for deep learning models, is designed to simulate the interactions among multiple chemical components in the B-H reaction by representing each reactant as an individual node within a custom-designed reaction graph, thereby capturing both single-molecule and intermolecular relationship features. Trained on a high-throughput B-H reaction data set, B-H Reaction Graph Neural Network (BH-RGNN) achieves near-state-of-the-art performance with an score of 0.971 while maintaining low computational costs.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
School of Advertising, Marketing and Public Relations, Faculty of Business and Law, Queensland University of Technology, Brisbane, Australia.
Background: Labor shortages in health care pose significant challenges to sustaining high-quality care for people with intellectual disabilities. Social robots show promise in supporting both people with intellectual disabilities and their health care professionals; yet, few are fully developed and embedded in productive care environments. Implementation of such technologies is inherently complex, requiring careful examination of facilitators and barriers influencing sustained use.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
View Article and Find Full Text PDF