Bacterial Infection

Machine learning reveals the dynamic importance of accessory sequences for outbreak clustering.

UNLABELLED: Bacterial typing at whole-genome scales is now feasible owing to decreasing costs in high-throughput sequencing and the recent advances in computation. The unprecedented resolution of whole-genome typing is achieved by genotyping the variable segments of bacterial genomes that can fluctuate significantly in gene content. However, due to the transient and hypervariable nature of many ac...

Establishment and evaluation of a UPLC-MS/MS method for simultaneous determination of bongkrekic acid and dehydroacetic acid in rice noodles.

A UPLC-MS/MS method with one-step extraction and simultaneous determination of bongkrekic acid (BA) and dehydroacetic acid (DHA) in rice noodles has been established in this study for the first time. The extraction solvent consisted of a mixture of methanol, ammonia, and water. Electrospray ionization negative ion mode (ESI-) was used for multiple reaction detection and external standard method wa...

Predicting biomass conversion and COD removal in wastewater treatment by phototrophic bacteria with interpretable machine learning.

Photosynthetic bacteria (PSB) excel in wastewater treatment by removing pollutants and generating biomass but are challenging to optimize due to compl...

AI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study.

BACKGROUND: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Fai...

Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning models.

BACKGROUND: Bloodstream infection (BSI) is a systemic infection that predisposes individuals to sepsis and multiple organ dysfunction syndrome. Early ...

Development and validation of a machine learning-based prediction model for hepatorenal syndrome in liver cirrhosis patients using MIMIC-IV and eICU databases.

Hepatorenal syndrome (HRS) is a key contributor to poor prognosis in liver cirrhosis. This study aims to leverage the database to build a predictive m...

Robotic versus manual disinfection of global priority pathogens at COVID-19-dedicated hospitals.

BACKGROUND: Twelve bacterial families identified as global priority pathogens (GPPs) pose the greatest threat to human health due to declining antibio...

Bacteria Flagella-Mimicking Polymer Multilayer Magnetic Microrobots.

Mass production of biomedical microrobots demands expensive and complex preparation techniques and versatile biocompatible materials. Learning from na...

CAM/TMA-DPH as a promising alternative to SYTO9/PI for cell viability assessment in bacterial biofilms.

INTRODUCTION: Accurately assessing biofilm viability is essential for evaluating both biofilm formation and the efficacy of antibacterial treatments. ...

Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens.

Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-bas...

Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning.

Although wastewater-based epidemiology has been used extensively for the surveillance of viral diseases, it has not been used to a similar extent for ...

Field-scale detection of Bacterial Leaf Blight in rice based on UAV multispectral imaging and deep learning frameworks.

Bacterial Leaf Blight (BLB) usually attacks rice in the flowering stage and can cause yield losses of up to 50% in severely infected fields. The resul...

W70: a probiotic with capacity to enhance ammonia assimilation in the rumen.

Ruminants excrete both undigested and excess consumed nitrogen (N) through feces and urine, which results in the waste of feed protein and environment...

Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach.

BACKGROUND: Leptospirosis is a neglected zoonotic disease prevalent worldwide, particularly in tropical regions experiencing frequent rainfall and sev...

Determining causal pathogens and inflammatory state of mastitis in dairy cows via Gram staining of precipitates in milk.

Early detection of bovine mastitis-causing pathogens is necessary for treatment. As culturing methods are time-consuming, a more rapid detection techn...

Machine Learning-Assisted High-Throughput Screening of Nanozymes for Ulcerative Colitis.

Ulcerative colitis (UC) is a chronic gastrointestinal inflammatory disorder with rising prevalence. Due to the recurrent and difficult-to-treat nature...

Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments.

Pathogenic bioaerosols are critical for outbreaks of airborne disease; however, rapidly and accurately identifying pathogens directly from complex air...

Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning.

Gram staining has been a frequently used staining protocol in microbiology. It is vulnerable to staining artifacts due to, e.g., operator errors and c...

ARGai 1.0: A GAN augmented in silico approach for identifying resistant genes and strains in E. coli using vision transformer.

The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. coli) shows the necessity for novel computational ...

ProPr54 web server: predicting σ promoters and regulon with a hybrid convolutional and recurrent deep neural network.

σ serves as an unconventional sigma factor with a distinct mechanism of transcription initiation, which depends on the involvement of a transcription ...