Bacterial Infection

sRNAdeep: a novel tool for bacterial sRNA prediction based on DistilBERT encoding mode and deep learning algorithms.

BACKGROUND: Bacterial small regulatory RNA (sRNA) plays a crucial role in cell metabolism and could be used as a new potential drug target in the treatment of pathogen-induced disease. However, experimental methods for identifying sRNAs still require a large investment of human and material resources.

Rapid and accurate bacteria identification through deep-learning-based two-dimensional Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) offers a distinctive vibrational fingerprint of the molecules and has led to widespread applications in medical diagnosis, biochemistry, and virology. With the rapid development of artificial intelligence (AI) technology, AI-enabled Raman spectroscopic techniques, as a promising avenue for biosensing applications, have significantly boosted bacteria ident...

A novel machine-learning aided platform for rapid detection of urine ESBLs and carbapenemases: URECA-LAMP.

Pathogenic gram-negative bacteria frequently carry genes encoding extended-spectrum beta-lactamases (ESBL) and/or carbapenemases. Of great concern are...

Machine learning models reveal how polycyclic aromatic hydrocarbons influence environmental bacterial communities.

Polycyclic aromatic hydrocarbons (PAHs) are harmful and widespread pollutants in the environment, posing an ecological threat. However, exploring the ...

Leveraging Broad-Spectrum Fluorescence Data and Machine Learning for High-Accuracy Bacterial Species Identification.

Rapid and accurate identification of bacterial species is essential for the effective treatment of infectious diseases and suppression of antibiotic-r...

Enhancing the accuracy and effectiveness of diagnosis of spontaneous bacterial peritonitis in cirrhotic patients: A machine learning approach utilizing clinical and laboratory data.

PURPOSE: Spontaneous bacterial peritonitis (SBP) is a bacterial infection of ascitic fluid that develops naturally, without being triggered by any sur...

Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis.

Despite advancements in detection and treatment, tuberculosis (TB), an infectious illness caused by the Mycobacterium TB bacteria, continues to pose a...

Predicting Lactobacillus delbrueckii subsp. bulgaricus-Streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method.

Lactobacillus delbrueckii subsp. bulgaricus (L. bulgaricus) and Streptococcus thermophilus (S. thermophilus) are commonly used starters in milk fermen...

A novel ensemble approach with deep transfer learning for accurate identification of foodborne bacteria from hyperspectral microscopy.

The detection of foodborne bacteria is critical in ensuring both consumer safety and food safety. If these pathogens are not properly identified, it c...

Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests.

BACKGROUND: Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the effic...

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 a...

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...

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...