Background: Coronary artery disease (CAD) is a common complex disease caused by atherosclerosis. Autophagy is a cellular degradation process that delivers long-lived macromolecules and dysfunctional organelles into lysosomes for digestion. Autophagy regulates lipid and cholesterol metabolism. We have previously shown that expression of autophagic and lysosomal genes is altered in CAD patients. In this study, we investigated gene expression of a lysosomal hydrolase, acid α-glucosidase (GAA), in CAD patients and controls.
Methods: GAA gene expression was examined in large cohorts of CAD patients (n=248) and ethnically matched controls (n=208). GAA enzymatic activity, protein levels, and transcript levels were determined and compared between CAD patients and controls.
Results: GAA activities in CAD patients were significantly elevated (P<0.05) compared with controls. Consistently, GAA transcription levels were also significantly increased in CAD patients (P<0.01). Multivariate logistic regression analyses (GAA transcript level, hypertension, diabetes, and smoking) revealed that GAA transcript levels were strongly associated with CAD (odds ratio 5.93, 95% confidence interval 2.98-11.78, P=3.89×10(-7)). GAA protein levels were insignificantly increased in CAD patients (P>0.05), likely due to assay insensitivity.
Conclusion: Compared with controls, GAA gene expression levels in CAD patients were significantly increased, suggesting that GAA may be involved in the CAD development.
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http://dx.doi.org/10.1097/MCA.0000000000000322 | DOI Listing |
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January 2025
Neurology. Universitat Autònoma de Barcelona, Univ Hosp Vall d'Hebron, SPAIN.
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Institute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli 350, Taiwan.
An AI-assisted algorithm has been developed to improve the detection of significant coronary artery disease (CAD) in high-risk individuals who have normal electrocardiograms (ECGs). This retrospective study analyzed ECGs from patients aged ≥ 18 years who were undergoing coronary angiography to obtain a clinical diagnosis at Chang Gung Memorial Hospital in Taiwan. Utilizing 12-lead ECG datasets, the algorithm integrated features like time intervals, amplitudes, and slope between peaks, a total of 561 features, with the XGBoost model yielding the best performance.
View Article and Find Full Text PDFHealth Inf Sci Syst
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Institute for Healthcare Artificial Intelligence Application, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510317 China.
Coronary artery disease (CAD) remains the leading cause of death globally, highlighting the critical need for accurate diagnostic tools in medical imaging. Traditional segmentation methods for coronary angiograms often struggle with vessel discontinuity and inaccuracies, impeding effective diagnosis and treatment planning. To address these challenges, we developed the Local Adaptive Segmentation Framework (LASF), enhancing the YOLOv8 architecture with dilation and erosion algorithms to improve the continuity and precision of vascular image segmentation.
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