Journal of investigative medicine : the official publication of the American Federation for Clinical Research
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Randomized Controlled Trial
EXPRESS: Effects of Prehabilitation Resistance Training in Mild to Moderate Clinically Frail Patients Awaiting Coronary Artery Bypass Graft Surgery.
Coronary artery disease is one of the main causes of mortality and morbidity among chronic heart diseases worldwide. Patients reported chronic chest pain as the primary symptom of coronary artery disease. Due to its progressive nature, it affects the health status and functional capacity of the patients. ⋯ Clinical frailty scores in group A and group B were 2.68 and 2.74, respectively, with the essential frailty toolset in group A and group B were 1.14 and 1.11, respectively. There were significant (p < 0.05) differences within and between groups for prehabilitation resistance training after CABG. The study showed that the resistance training group had improved the clinical frailty score, strength, endurance, and functional capacity in patients who underwent elective CABG.
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This study intended to delineate the mechanism and functional role of integrin α2 (ITGA2) in non-small-cell lung cancer (NSCLC) cell immune escape. Bioinformatics analysis was utilized to analyze ITGA2 expression in NSCLC tissues, and correlations between ITGA2 expression and patient survival time, ITGA2 expression and programmed cell death ligand 1 (PD-L1; CD274) expression, and ITGA2 expression and CD8+ T-cell infiltration. Quantitative real-time polymerase chain reaction detected ITGA2 expression. ⋯ Clinical sample testing unveiled that ITGA2 was upregulated in NSCLC tissues. PD-L1 upregulation was seen in exosomes separated from patient blood, and correlation analysis showed a positive correlation of exosomal PD-L1 expression in blood with ITGA2 expression in tissues. This study displays a novel mechanism and role of ITGA2 in NSCLC immune escape, providing directions for the clinical therapy of NSCLC patients.
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The generalizability of artificial intelligence (AI) models is a major issue in the field of AI applications. Therefore, we aimed to overcome the generalizability problem of an AI model developed for a particular center for pneumothorax detection using a small dataset for external validation. Chest radiographs of patients diagnosed with pneumothorax (n = 648) and those without pneumothorax (n = 650) who visited the Ankara University Faculty of Medicine (AUFM; center 1) were obtained. ⋯ The positive predictive value increased from 0.525 to 0.886 after external validation (p = 0.041). The physicians' sensitivity and specificity for detecting pneumothorax were 0.585 and 0.988, respectively. The performance scores of the algorithms were increased with a small dataset; however, further studies are required to determine the optimal amount of external validation data to fully address the generalizability issue.
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Atrial fibrillation (AF) frequently occurs concurrently with heart failure (HF). The two conditions can exacerbate each other, resulting in higher morbidity and mortality. In our analysis, we evaluated mortality trends related to AF in individuals with underlying HF. ⋯ Our results demonstrate existing disparities among age, gender, racial, and geographic subgroups related to AF mortality among individuals with comorbid HF. Although decreased overall mortality was observed within younger populations compared to older populations, the prominent AAPC seen in younger populations warrants further investigation. Detection of AF among younger adults with comorbid HF should prompt the intensification of preventative and treatment measures.
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The coronavirus disease 2019 (COVID-19) pandemic, which emerged in late 2019, has caused millions of infections and fatalities globally, disrupting various aspects of human society, including socioeconomic, political, and educational systems. One of the key challenges during the COVID-19 pandemic is accurately predicting the clinical development and outcome of the infected patients. In response, scientists and medical professionals globally have mobilized to develop prognostic strategies such as risk scores, biomarkers, and machine learning models to predict the clinical course and outcomes of COVID-19 patients. ⋯ Our model outperforms the clinical predictive models regarding patient mortality risk and classification in the literature. Therefore, we conclude that our robust model can help healthcare professionals to manage COVID-19 patients more effectively. We expect that early prediction of COVID-19 patients and preventive interventions can reduce the mortality risk of patients.