AIMC Topic: Anti-Bacterial Agents

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Novel antimicrobial peptides against Cutibacterium acnes designed by deep learning.

Scientific reports
The increasing prevalence of antibiotic resistance in Cutibacterium acnes (C. acnes) requires the search for alternative therapeutic strategies. Antimicrobial peptides (AMPs) offer a promising avenue for the development of new treatments targeting C....

Predicting S. aureus antimicrobial resistance with interpretable genomic space maps.

Molecular informatics
Increasing antimicrobial resistance (AMR) represents a global healthcare threat. To decrease the spread of AMR and associated mortality, methods for rapid selection of optimal antibiotic treatment are urgently needed. Machine learning (ML) models bas...

Real-time machine learning-assisted sepsis alert enhances the timeliness of antibiotic administration and diagnostic accuracy in emergency department patients with sepsis: a cluster-randomized trial.

Internal and emergency medicine
Machine learning (ML) has been applied in sepsis recognition across different healthcare settings with outstanding diagnostic accuracy. However, the advantage of ML-assisted sepsis alert in expediting clinical decisions leading to enhanced quality fo...

Raman spectrum combined with deep learning for precise recognition of Carbapenem-resistant Enterobacteriaceae.

Analytical and bioanalytical chemistry
Carbapenem-resistant Enterobacteriaceae (CRE) is a major pathogen that poses a serious threat to human health. Unfortunately, currently, there are no effective measures to curb its rapid development. To address this, an in-depth study on the surface-...

Investigation of bacterial DNA gyrase Inhibitor classification models and structural requirements utilizing multiple machine learning methods.

Molecular diversity
Infections from multidrug-resistant (MDR) bacteria have emerged as a paramount global health concern, and the therapeutic effectiveness of current treatments is swiftly diminishing. An urgent need exists to explore innovative strategies for counterin...

Nanozyme-induced deep learning-assisted smartphone integrated colorimetric and fluorometric dual-mode for detection of tetracycline analogs.

Analytica chimica acta
In this work, a colorimetric and fluorescent dual-mode probe controlled by NH-MIL-88 B (Fe, Ni) nanozymes was developed to visually detect tetracycline antibiotics (TCs) residues quantitatively, as well as accurately distinguish the four most widely ...

Deep Learning-Based Culture-Free Bacteria Detection in Urine Using Large-Volume Microscopy.

Biosensors
Bacterial infections, increasingly resistant to common antibiotics, pose a global health challenge. Traditional diagnostics often depend on slow cell culturing, leading to empirical treatments that accelerate antibiotic resistance. We present a novel...

Antimicrobial resistance crisis: could artificial intelligence be the solution?

Military Medical Research
Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of...

Predicting Penicillin Allergy: A United States Multicenter Retrospective Study.

The journal of allergy and clinical immunology. In practice
BACKGROUND: Using the reaction history in logistic regression and machine learning (ML) models to predict penicillin allergy has been reported based on non-US data.

Effect of palladium(II) complexes on NorA efflux pump inhibition and resensitization of fluoroquinolone-resistant : and approach.

Frontiers in cellular and infection microbiology
leads to diverse infections, and their treatment relies on the use of antibiotics. Nevertheless, the rise of antibiotic resistance poses an escalating challenge and various mechanisms contribute to antibiotic resistance, including modifications to d...