Bacterial Infection

Application of artificial neural network for the mechano-bactericidal effect of bioinspired nanopatterned surfaces.

This study aimed to calculate the effect of nanopatterns' peak sharpness, width, and spacing parameters on P. aeruginosa and S. aureus cell walls by artificial neural network and finite element analysis. Elastic and creep deformation models of bacteria were developed in silico. Maximum deformation, maximum stress, and maximum strain values of the cell walls were calculated. According to the result...

AmpClass: an Antimicrobial Peptide Predictor Based on Supervised Machine Learning.

In the last decades, antibiotic resistance has been considered a severe problem worldwide. Antimicrobial peptides (AMPs) are molecules that have shown potential for the development of new drugs against antibiotic-resistant bacteria. Nowadays, medicinal drug researchers use supervised learning methods to screen new peptides with antimicrobial potency to save time and resources. In this work, we con...

Understanding the mechanism of microplastic-associated antibiotic resistance genes in aquatic ecosystems: Insights from metagenomic analyses and machine learning.

The pervasive presence of microplastics (MPs) in aquatic systems facilitates the transmission of antibiotic resistance genes (ARGs), thereby posing ri...

CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products.

The sluggish pace of new antibacterial drug development reflects a vulnerability in the face of the current severe threat posed by bacterial resistanc...

Development of a machine learning model to support low cost real-time Legionella monitoring in premise plumbing systems.

Legionella pneumophila (L. pneumophila) is a pathogenic bacterium primarily known for causing Legionnaires' Disease which is known for high mortality ...

Transition from sulfur autotrophic to mixotrophic denitrification: Performance with different carbon sources, microbial community and artificial neural network modeling.

To address the limitations inherent in both sulfur autotrophic denitrification (SAD) and heterotrophic denitrification (HD) processes, this study intr...

Proof of concept study on early forecasting of antimicrobial resistance in hospitalized patients using machine learning and simple bacterial ecology data.

Antibiotic resistance in bacterial pathogens is a major threat to global health, exacerbated by the misuse of antibiotics. In hospital practice, resul...

Species annotation using a k-mer based KNN model.

Bacterial identification is a critical process in microbiology, clinical diagnostics, environmental monitoring, and food safety. Machine learning hold...

Recursive dynamics of GspE through machine learning enabled identification of inhibitors.

Type II secretion System has been increasingly recognized as a key driver of virulence in many pathogenic bacteria including Achromobacter xylosoxidan...

Multiplexed bacterial recognition based on "All-in-One" semiconducting polymer dots sensor and machine learning.

The accurate discrimination of bacterial infection is imperative for precise clinical diagnosis and treatment. Here, this work presents a simplified s...

Deep learning and feature reconstruction assisted vis-NIR calibration method for on-line monitoring of key growth indicators during kombucha production.

Artificial intelligence (AI) technology is advancing the digitization and intelligence development of the food industry. A promising application is us...

Synergistic biochar and Serratia marcescens tackle toxic metal contamination: A multifaceted machine learning approach.

Metal contamination in soil poses environmental and health risks requiring effective remediation strategies. This study introduces an innovative appro...

Diagnostic performance of an automated robot for MALDI target preparation in microbial identification.

The MBT Pathfinder is an automated colony-picking robot designed for efficient sample preparation in matrix-assisted laser desorption/ionization time-...

Protein interactions in human pathogens revealed through deep learning.

Identification of bacterial protein-protein interactions and predicting the structures of these complexes could aid in the understanding of pathogenic...

Prediction of antimicrobial resistance of Klebsiella pneumoniae from genomic data through machine learning.

Antimicrobials, such as antibiotics or antivirals are medications employed to prevent and treat infectious diseases in humans, animals, and plants. An...

Predictive performance of urinalysis for urine culture results according to causative microorganisms: an integrated analysis with artificial intelligence.

Urinary tract infections (UTIs) are pervasive and prevalent in both community and hospital settings. Recent trends in the changes of the causative mic...

Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units.

This retrospective study used 10 machine learning algorithms to predict the risk of healthcare-associated infections (HAIs) in patients admitted to in...

Antibiotic profile classification of Proteus mirabilis using machine learning: An investigation into multidimensional radiomics features.

Antimicrobial resistance (AMR) presents a significant threat to global healthcare. Proteus mirabilis causes catheter-associated urinary tract infectio...

Machine learning supported single-stranded DNA sensor array for multiple foodborne pathogenic and spoilage bacteria identification in milk.

Ensuring food safety through rapid and accurate detection of pathogenic bacteria in food products is a critical challenge in the food supply chain. In...

Plasma cell-free RNA signatures of inflammatory syndromes in children.

Inflammatory syndromes, including those caused by infection, are a major cause of hospital admissions among children and are often misdiagnosed becaus...