Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI).

The accurate prediction of in-hospital mortality in Asian women after ST-Elevation Myocardial Infarction (STEMI) remains a crucial issue in medical research. Existing models frequently neglect this demographic's particular attributes, resulting in poor treatment outcomes. This study aims to improve the prediction of in-hospital mortality in multi-ethnic Asian women with STEMI by employing both base and ensemble machine learning (ML) models.

[Adsorption Mechanisms of Ciprofloxacin by Extracellular Polymeric Substances of Sulfate-reducing Bacteria Sludge].

Extracellular polymeric substances (EPS) in microbial sludge, fulfils a key role in removal of micro-organic pollutants during biological wastewater treatment. In this study, the authors evaluated the removal of ciprofloxacin (CIP) by sulfate-reducing bacteria (SRB) sludge in a sulfate-reducing up-flow sludge bed (SRUSB) reactor, and examined the role of EPS on CIP removal in an SRB sludge system. The results indicated that CIP was removed efficiently through adsorption and biodegradation by SRB sludge, with adsorption the major removal pathway.

Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation.

Objectives: Prediction of activities of daily living (ADL) is crucial for optimized care of post-stroke patients. However, no suitably-validated and practical models are currently available in clinical practice.

Methods: Participants of a Post-acute Care-Cerebrovascular Diseases (PAC-CVD) program from a reference hospital in Taiwan between 2014 and 2016 were enrolled in this study.