AIMC Topic: Pseudomonas aeruginosa

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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...

Impact of COVID-19 isolation measures on ICU microbial resistance dynamics: simulation-based statistical modeling analysis.

Antimicrobial resistance and infection control
BACKGROUND: The transmission of antibiotic-resistant bacteria in intensive care units (ICUs) poses a significant challenge to infection control and patient safety. While direct patient-to-patient transmission is well documented, the relative contribu...

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...

Analysis of disruptive action of electrical current on cell membrane integrity and modelling its antimicrobial activity.

Archives of microbiology
It is essential to eliminate harmful microbes from vital aspects of our lives, including dental instruments and other healthcare devices, cosmetics, foods, and products that come into contact with them. Electrical stimulation (ES) has been proposed t...

Rapid label-free identification of seven bacterial species using microfluidics, single-cell time-lapse phase-contrast microscopy, and deep learning-based image and video classification.

PloS one
For effective treatment of bacterial infections, it is essential to identify the species causing the infection as early as possible. Current methods typically require hours of overnight culturing of a bacterial sample and a larger quantity of cells t...

Machine learning-assisted comparative QSTR, i-QSTTR, qRASTR, and i-qRASTTR modelling for toxicity of Ionic liquids against three different bacteria S. aureus, E. coli, and P. aeruginosa.

Journal of hazardous materials
Ionic liquids (ILs) with tunable structures have emerged as promising next-generation biocides. In this study, we presented an ML-based q-RASTR framework, along with i-qRASTTR approach, to predict the toxicity of ILs against different bacteria. Vario...

Antimicrobial Peptides Design Using Deep Learning and Rational Modifications: Activity in Bacteria, Candida albicans, and Cancer Cells.

Current microbiology
Resistance to antimicrobial agents has become a global threat, estimated to cause 10-million deaths annually by 2050. Antimicrobial peptides are emerging as an alternative and offer advantages over traditional antibiotics. Antimicrobial peptides gene...

Combinatorial discovery of microtopographical landscapes that resist biofilm formation through quorum sensing mediated autolubrication.

Nature communications
Bio-instructive materials that intrinsically inhibit biofilm formation have significant anti-biofouling potential in industrial and healthcare settings. Since bacterial surface attachment is sensitive to surface topography, we experimentally surveyed...

Predicting carbapenem-resistant Pseudomonas aeruginosa infection risk using XGBoost model and explainability.

Scientific reports
The prevalence and spread of carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a global public health problem. This study aims to identify the risk factors of CRPA infection and construct a machine learning model to provide a prediction tool for ...