AIMC Topic: Klebsiella pneumoniae

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

PloS one
Antimicrobials, such as antibiotics or antivirals are medications employed to prevent and treat infectious diseases in humans, animals, and plants. Antimicrobial Resistance occurs when bacteria, viruses, and parasites no longer respond to these medic...

Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis.

NPJ biofilms and microbiomes
At present, the diagnosis of lower respiratory tract infections (LRTIs) is difficult, and there is an urgent need for better diagnostic methods. This study enrolled 136 patients from 2020 to 2021 and collected bronchoalveolar lavage fluid (BALF) spec...

VacSol-ML(ESKAPE) Machine learning empowering vaccine antigen prediction for ESKAPE pathogens.

Vaccine
The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensiv...

Protein function annotation and virulence factor identification of Klebsiella pneumoniae genome by multiple machine learning models.

Microbial pathogenesis
Klebsiella pneumoniae is a type of Gram-negative bacterium which can cause a range of infections in human. In recent years, an increasing number of strains of K. pneumoniae resistant to multiple antibiotics have emerged, posing a significant threat t...

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase.

Journal of chemical information and modeling
The worldwide spread of the metallo-β-lactamases (MBL), especially New Delhi metallo-β-lactamase-1 (NDM-1), is threatening the efficacy of β-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved...

Machine-learning-based risk assessment tool to rule out empirical use of ESBL-targeted therapy in endemic areas.

The Journal of hospital infection
BACKGROUND: Antimicrobial stewardship focuses on identifying patients who require extended-spectrum beta-lactamase (ESBL)-targeted therapy. 'Rule-in' tools have been researched extensively in areas of low endemicity; however, such tools are inadequat...

Antimicrobial Activity Classification of Imidazolium Derivatives Predicted by Artificial Neural Networks.

Pharmaceutical research
PURPOSE: This study assesses the Multilayer Perceptron (MLP) neural network, complemented by other Machine Learning techniques (CART, PCA), in predicting the antimicrobial activity of 140 newly designed imidazolium chlorides against Klebsiella pneumo...