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Anti-Bacterial Agents

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Miniature Robots for Battling Bacterial Infection.

ACS nano
Micro/nanorobots have shown great promise for minimally invasive bacterial infection therapy. However, bacterial infections usually form biofilms inside the body by aggregation and adhesion, preventing antibiotic penetration and increasing the likeli...

Plasma treated bimetallic nanofibers as sensitive SERS platform and deep learning model for detection and classification of antibiotics.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Design of a sensitive, cost-effective SERS substrate is critical for probing analyte in trace concentration in real field environment. Present work reports the fabrication of an oxygen (O) plasma treated bimetallic nanofibers as a sensitive SERS plat...

Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.

Frontiers in cellular and infection microbiology
The issue of antimicrobial resistance (AMR) in pathogenic microorganisms has emerged as a global public health crisis, posing a significant threat to the modern healthcare system. The advent of Artificial Intelligence (AI) and Machine Learning (ML) t...

Nanomaterial Texture-Based Machine Learning of Ciprofloxacin Adsorption on Nanoporous Carbon.

International journal of molecular sciences
Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. This study focuses on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experi...

Impacts of micro/nano plastics on the ecotoxicological effects of antibiotics in agricultural soil: A comprehensive study based on meta-analysis and machine learning prediction.

The Science of the total environment
Micro/nano plastics (M/NPs) and antibiotics, as widely coexisting pollutants in environment, pose serious threats to soil ecosystem. The purpose of this study was to systematically evaluate the ecological effects of the co-exposure of M/NPs and antib...

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

PeerJ
BACKGROUND: Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pat...

A novel interpretable machine learning and metaheuristic-based protocol to predict and optimize ciprofloxacin antibiotic adsorption with nano-adsorbent.

Journal of environmental management
The existence of antibiotics in water sources poses substantial hazards to both the environment and public health. To effectively monitor and combat this problem, accurate predictive models are essential. This research focused on employing machine le...

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

European biophysics journal : EBJ
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 bacter...

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

Interdisciplinary sciences, computational life sciences
The sluggish pace of new antibacterial drug development reflects a vulnerability in the face of the current severe threat posed by bacterial resistance. Microbial natural products (NPs), as a reservoir of immense chemical potential, have emerged as t...