Bmc Med
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A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. ⋯ AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.
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There is currently a deficit of knowledge about how to define, quantify, and measure different aspects of daily routine disruptions amid large-scale disasters like COVID-19, and which psychiatric symptoms were more related to the disruptions. This study aims to conduct a systematic review and meta-analysis on the probable positive associations between daily routine disruptions and mental disorders amid the COVID-19 pandemic and factors that moderated the associations. ⋯ This is one of the first meta-analytic evidence to show the positive association between daily routine disruptions and symptoms of mental disorders among large populations as COVID-19 dynamically unfolded across different geo-temporal contexts. Our findings highlight the priority of behavioral adjustment for enhancing population mental health in future large-scale disasters like COVID-19.
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There are over 53million children worldwide under five with developmental disabilities who require effective interventions to support their health and well-being. However, challenges in delivering interventions persist due to various barriers, particularly in low-income and middle-income countries. ⋯ We identified geographical and disability-related inequities. There is a lack of evidence from outside high-income settings. The study underscores gaps in evidence concerning prevention, identification and intervention, revealing a stark mismatch between the available evidence base and the regions experiencing the highest prevalence rates of developmental disabilities.
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Heart failure (HF) is a complex clinical syndrome with persistently high mortality. High-throughput proteomic technologies offer new opportunities to improve HF risk stratification, but their contribution remains to be clearly defined. We aimed to systematically review prognostic studies using high-throughput proteomics to identify protein signatures associated with HF mortality. ⋯ In this systematic review of nine studies evaluating the association of proteomics with mortality in HF, we identified a limited number of proteins common across several studies. Heterogeneity across studies compromised drawing broad inferences, underscoring the importance of standardized approaches to reporting.
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We recently reported the first clinical case of bladder fermentation syndrome (BFS) or urinary auto-brewery syndrome, which caused the patient to fail abstinence monitoring. In BFS, ethanol is generated by Crabtree-positive fermenting yeast Candida glabrata in a patient with poorly controlled diabetes. One crucial characteristic of BFS is the absence of alcoholic intoxication, as the bladder lumen contains transitional epithelium with low ethanol permeability. ⋯ BFS and GFS are treated by yeast eradication of fermenting microorganisms with antifungals (or antibiotics for bacterial GFS cases) and modification of underlying conditions (diabetes for BFS and gut dysbiosis for GFS). The under-recognition of these rare medical conditions has led to not only harm but also adverse legal consequences for patients, such as driving under the influence (DUI). GFS patients may be at risk of various alcohol-related diseases.