AIMC Topic: Escherichia coli

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Renewable Phytase Biocatalyst to Transform Biorefinery Waste Streams into Phosphorus Resources.

Environmental science & technology
Phosphorus (P) recovery is crucial for sustaining the global food supply and preventing freshwater pollution. Biorefinery waste streams have emerged as promising, yet underexplored sources for P recovery. Here, we presented a renewable and robust cel...

Advances in gene-targeted diagnostics for pathogenic .

The Analyst
Pathogenic (PEC) strains are important pathogens that causes a variety of infectious diseases in humans. Traditional bacterial culture and biochemical identification methods are time-consuming and lack specificity, making rapid and accurate diagnosi...

Prediction of antimicrobial resistance from MALDI-TOF mass spectra using machine learning: a validation study.

Journal of clinical microbiology
UNLABELLED: Matrix-assisted laser desorption-ionization-time of flight (MALDI-TOF) mass spectra can be used to predict antimicrobial resistance (AMR) using machine learning (ML). This study aimed to validate the performance of ML models for AMR predi...

Flexible Porous ACH/Ag Surface-Enhanced Raman Scattering Platform for Sensitive Detection and Machine-Learning-Assisted Classification of Multiple Pathogenic Bacteria.

Analytical chemistry
Pathogenic bacteria pose serious threats to public health and environmental safety. Conventional colony counting, a standard method for bacterial detection, is time-consuming and unsuitable for rapid on-site detection. In this work, a flexible ACH/Ag...

Time-Lapse Deep Learning for Single-Cell Subcellular Structural Phenotypic Antimicrobial Susceptibility Testing.

Analytical chemistry
Antimicrobial resistance (AMR) is a global health concern that complicates the effective treatment of infections, resulting in an increased severity of illness and elevated healthcare costs. Traditional phenotypic antimicrobial susceptibility testing...

An LLM-Based Tool for Automated Generation of 3D Model Organism Colony Simulations.

ACS synthetic biology
We present MicroVerse, a software application based on large language models (LLMs) designed to generate C# scripts simulating three-dimensional colonies of model organisms, including and , within the Unity platform. The system, which utilizes fine-...

Rapid key gene discovery for bacterial shape: a cross-species machine learning approach.

BMC microbiology
Accurately identifying genes responsible for specific functions is a cornerstone of biological research, but current methods are often limited to single-species analyses. Here, we present a novel method, called Genomic and Phenotype-based machine lea...

Exploring multidrug resistance patterns in community-acquired urinary tract infections with machine learning.

Antimicrobial agents and chemotherapy
While associations of antibiotic resistance traits are not random in multidrug-resistant (MDR) bacteria, clinically relevant resistance patterns remain underexplored. This study used association-set mining to explore resistance associations within i...

A machine learning approach to predict strain-specific phage-host interactions.

Scientific reports
The use of bacteriophages for biological control of bacterial infections is a promising approach to combat antimicrobial resistant bacteria. Prediction of phage-bacteria interactions is key to identify sensitive bacterial strains to phage therapy. Si...

Coral-Derived Antimicrobial Peptides Identified In Silico from Acropora digitifera Transcriptomes: Potential Candidates Against Resistant Pathogens.

Marine biotechnology (New York, N.Y.)
Antimicrobial resistance is a serious threat to global public health and requires new therapeutic approaches. Antimicrobial peptides (AMP) are recognized as promising candidates to address antimicrobial resistance. AMP can disrupt cell membranes by i...