Annals of medicine
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Predicting acute exacerbations (AEs) in chronic obstructive pulmonary disease (COPD) is crucial. This study aimed to identify blood biomarkers for predicting COPD exacerbations by inflammatory phenotypes. ⋯ Our study indicates that distinct white blood cell profiles in COPD patients, with or without eosinophilic inflammation, can help assess the risk of AE in clinical settings.
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Studies demonstrated that outpatient aerobic exercise programs (aEP) can significantly decrease aortic stiffness in people with metabolic syndrome (MetS). There is some limited data that remotely supervised home-based aEP can also improve arterial stiffness in this population. We aimed to evaluate the changes in the arterial wall parameters after the 2-month ambulatory supervised aEP followed by the 6-month home-based aEP with and without targeting of heart rate (HR) by electrocardiogram (ECG) in people with MetS. ⋯ The combination of 2-month ambulatory supervised aEP and successive 6-month home-based aEP targeted by HR monitoring using ECG improved arterial properties in MetS subjects more than the same combination without HR targeting, leading to the greater reduction of c-r PWV and cIMT.
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To explore the novel applications of histological factors by stratifying the prognostic markers of the overall CRC patients in subgroups. ⋯ The stratification analyses of prognostic markers in CRC patients indicate novel applications of the above histopathological and molecular markers in clinic and the findings provide new insights into future investigations of precision pathology.
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This study aimed to investigate the association between cardiorespiratory fitness (CRF) and perioperative morbidity and long-term mortality in operable patients with early-stage non-small cell lung cancer (NSCLC). ⋯ The study found that low CRF is significantly associated with increased perioperative morbidity and long-term mortality in operable patients with early-stage NSCLC.
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Artificial intelligence (AI) and machine learning (ML) are revolutionizing human activities in various fields, with medicine and infectious diseases being not exempt from their rapid and exponential growth. Furthermore, the field of explainable AI and ML has gained particular relevance and is attracting increasing interest. ⋯ For example, they have been employed or proposed to better understand complex models aimed at improving the diagnosis and management of coronavirus disease 2019, in the field of antimicrobial resistance prediction and in quantum vaccine algorithms. Although some issues concerning the dichotomy between explainability and interpretability still require careful attention, an in-depth understanding of how complex AI/ML models arrive at their predictions or recommendations is becoming increasingly essential to properly face the growing challenges of infectious diseases in the present century.