Multi-label learning is a rapidly growing research area that aims to predict multiple labels
from a single input data point. In the era of big data, tasks involving multi-label classification …

Motivation Large-scale clinical proteomics datasets of infectious pathogens, combined with
antimicrobial resistance outcomes, have recently opened the door for machine learning …

Introduction Sepsis is the most severe infectious disease with the highest mortality rate,
particularly among neonates admitted to the neonatal intensive care unit (NICU). This study …

Background Antimicrobial resistance (AMR) poses a significant global health threat, and an
accurate prediction of bacterial resistance patterns is critical for effective treatment and …

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face
in the future. There have been various attempts to preserve the efficacy of existing …

Introduction Bacterial infections have become serious threats to human health, and the
excessive use of antibiotics has led to the emergence of multidrug-resistant (MDR) bacteria …

Due to multiple comorbid illnesses, polypharmacy, and age-related changes in
pharmacokinetics and pharmacodynamics in older adults, the prevalence of potentially …

This article describes the in vitro antibacterial and β-lactamase inhibition of a novel silver (I)
complex with the sulfonamide probenecid (Ag-PROB). The formula Ag 2 C 26 H 36 N2O 8 S …

Predicting Antimicrobial Resistance (AMR) from genomic sequence data has become a
significant component of overcoming the AMR challenge, especially given its potential for …

食源性致病菌引起的食源性疾病在全球范围内时有暴发, 对致病菌的调查和监测是预防和控制
疾病大范围发生的有效手段. 然而菌株的遗传变异给监测工作带来了挑战, 同时罕见的 …