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Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide, and its pathogenesis and potentially relevant biomarkers require further study. Imbalance in copper (Cu) metabolism is related to a series of diseases, but its role in COPD has not been specified.
Methods: A dataset relevant to COPD was downloaded from Gene Expression Omnibus database, among which a total of 18 cuproptosis-related genes (CRGs) were screened. The SimDesign package was used to perform single-factor Rogers regression to screen genes associated with disease phenotypes, risk score prediction models were constructed, and Receiver Operating Characteristic (ROC) curves were used to evaluate the efficacy of the prediction models. In addition, we verified the expression of CRGs in subtypes and the correlation between subtypes and clinical characteristics using a database. Finally, immune correlation analysis was used to explore immune cell infiltration.
Results: Five biomarkers (DLST, GLS, LIPT1, MTF1, and PDHB) were identified. ROC analysis illustrated that these five biomarkers performed well (area under the curve (AUCs)>0.7), and the enrichment scores of diagnostic CRGs were significantly different among subtypes, among which the chi-square test P-values of the age groups were significantly different. The immune infiltration evaluation of cuproptosis subtypes revealed that the correlation analysis results of 22 types of immune cells showed a significant correlation between these cells, and the five CRGs were significantly correlated with the content of most immune cells in the 22 types of immune cells. The four pathways with the most significant differences in GSEA among subtypes were Oxidative Phosphorylation, Parkinson's Disease, Purine Metabolism, and Drug Metabolism Cytochrome P450.
Conclusion: This study identified five candidate genes for further investigation (DLST, GLS, LIPT1, MTF1, and PDHB) and constructed disease prediction models and pathogenesis pathways. This study can provide a basis for further research on the role of cuproptosis in COPD.
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http://dx.doi.org/10.2147/COPD.S497473 | 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