Bacterial Infection

Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens.

The increase in antimicrobial resistance presents a major challenge in treating bacterial infections, underscoring the need for innovative drug discovery approaches and novel inhibitors. Bacterial RNA polymerase (RNAP) has emerged as a crucial target for antibiotic development due to its essential role in transcription. RNAP is a molecular motor, and its function relies heavily on the dynamic shif...

Label-free rapid antimicrobial susceptibility testing with machine-learning based dynamic holographic laser speckle imaging.

Antimicrobial resistance (AMR) presents a significant global challenge, creating an urgent need for rapid and sensitive antimicrobial susceptibility testing (AST) methods to guide timely treatment decisions. Traditional AST techniques, such as broth microdilution, disk diffusion, and gradient diffusion assays, require extended incubation times, delaying critical therapeutic interventions. In this ...

Machine learning detection of heteroresistance in Escherichia coli.

BACKGROUND: Heteroresistance (HR) is a significant type of antibiotic resistance observed for several bacterial species and antibiotic classes where a...

Cloning, Characterization, and Computer-Aided Evolution of a Thermostable Laccase of the DUF152 Family From Klebsiella michiganensis.

Bacterial laccases exhibit relatively high optimal reaction temperatures and possess a broad substrate spectrum, thereby expanding the range of potent...

SHASI-ML: a machine learning-based approach for immunogenicity prediction in vaccine development.

INTRODUCTION: Accurate prediction of immunogenic proteins is crucial for vaccine development and understanding host-pathogen interactions in bacterial...

Exploring the response of bacterial community functions to microplastic features in lake ecosystems through interpretable machine learning.

Microplastics (MPs) are ubiquitous and have various characteristics. However, their impacts on bacterial community functions in lakes remain elusive. ...

Artificial intelligence: a useful tool in active tuberculosis screening among vulnerable groups in Romania - advantages and limitations.

INTRODUCTION: Despite advances in diagnostic technologies for tuberculosis (TB), global control of this disease requires improved technologies for act...

Deep learning-driven bacterial cytological profiling to determine antimicrobial mechanisms in .

Tuberculosis (TB), caused by , remains a significant global health threat, affecting an estimated 10.6 million people in 2022. The emergence of multid...

Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides.

Artificial intelligence holds great promise for the design of antimicrobial peptides (AMPs); however, current models face limitations in generating AM...

Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction.

Identifying and characterizing virulence proteins secreted by Gram-negative bacteria are fundamental for deciphering microbial pathogenicity as well a...

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...

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 b...

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...