AIMC Topic: Anti-Infective Agents

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Design, synthesis and antimicrobial activity of 6-N-substituted chitosan derivatives.

Bioorganic & medicinal chemistry letters
Three novel 6-N-substituted chitosan derivatives were designed and synthesised and characterized by FTIR and NMR. The degree of substitution was calculated by elemental analysis results. The antimicrobial activities of the target compounds were evalu...

Machine learning in burn care and research: A systematic review of the literature.

Burns : journal of the International Society for Burn Injuries
BACKGROUND: To date, there are no reviews on machine learning (ML) in burn care. Considering the growth of ML in medicine and the complexities and challenges of burn care, this review specializes on ML applications in burn care. The objective was to ...

Prediction of β-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine.

Journal of theoretical biology
β-Lactam class of antibiotics is used as major therapeutic agent against a number of pathogenic microbes. The widespread and indiscriminate use of antibiotics to treat bacterial infection has prompted evolution of several evading mechanisms from the ...

Harnessing advances in mechanisms, detection, and strategies to combat antimicrobial resistance.

The Science of the total environment
Antimicrobial resistance (AMR) is a growing global health crisis, threatening the effectiveness of antibiotics and other antimicrobial agents, leading to increased morbidity, mortality, and economic burdens. This review article provides a comprehensi...

Machine Learning Model to Guide Empirical Antimicrobial Therapy in Febrile Neutropenic Patients With Hematologic Malignancies.

Anticancer research
BACKGROUND/AIM: Optimal antimicrobial selection for patients with febrile neutropenia (FN) may differ depending on the underlying mechanisms. We aimed to develop a model for predicting the severity of bacteremia in patients with FN and hematologic ma...

Strategies in using artificial intelligence to combat antimicrobial resistance.

Recenti progressi in medicina
Infectious diseases caused by pathogens resistant to antimicrobial treatments, defined as antimicrobial resistance (AMR), are a serious global health crisis, considered among the main threats to global public health according to the World Health Orga...

AI Methods for Antimicrobial Peptides: Progress and Challenges.

Microbial biotechnology
Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning...

Machine learning for adverse event prediction in outpatient parenteral antimicrobial therapy: a scoping review.

The Journal of antimicrobial chemotherapy
OBJECTIVE: This study aimed to conduct a scoping review of machine learning (ML) techniques in outpatient parenteral antimicrobial therapy (OPAT) for predicting adverse outcomes and to evaluate their validation, implementation and potential barriers ...

Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections.

Annals of medicine
Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. Numerous methods are used to overcome this problem with moderate success. Besides efforts of antimicr...

Machine Learning Prediction of Antimicrobial Peptides.

Methods in molecular biology (Clifton, N.J.)
Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in c...