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Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.

Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.

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Objective: To investigate the effects and mechanisms of miRNA 221 on myocardial ischemia/reperfusion injury (MIRI) in mice through the regulation of phospholamban (PLB) expression.

Methods: The MIRI mouse model was created and mice were divided into sham, MIRI, MIRI+ 221, and MIRI+ scr groups, with miRNA 221 overexpression induced in the myocardium of MIRI mice by targeted myocardial injection. Quantitative RT-PCR analysis was performed to observe the variation in miRNA 221, PLB, SERCA2, RYR2, NCX1, Cyt C and caspase 3 mRNA levels in myocardium, while Western blot assessed the levels of PLB, p-PLB (Ser16), p-PLB (Thr17), SERCA2, RYR2, NCX1, Cyt C and caspase 3 proteins.

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Background: Heart muscle damage from myocardial infarction (MI) is brought on by insufficient blood flow. The leading cause of death for middle-aged and older people worldwide is myocardial infarction (MI), which is difficult to diagnose because it has no symptoms. Clinicians must evaluate electrocardiography (ECG) signals to diagnose MI, which is difficult and prone to observer bias.

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Interleukin-34 (IL-34) was recently reported to be a new biomarker for atherosclerosis diseases, such as coronary artery disease and vascular dementia. IL-34 regulates the expression of proinflammatory cytokines (IL-17A, IL-1 and IL-6), which are classical cytokines involved in myocardial ischemia‒reperfusion (MI/R) injury. However, the exact role of IL-34 in MI/R remains unknown.

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Video-based estimation of blood pressure.

PLoS One

January 2025

Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States of America.

In this work, we propose a non-contact video-based approach that estimates an individual's blood pressure. The estimation of blood pressure is critical for monitoring hypertension and cardiovascular diseases such as coronary artery disease or stroke. Estimation of blood pressure is typically achieved using contact-based devices which apply pressure on the arm through a cuff.

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