Molecular Mechanisms Responsible for Drug Resistance DOI

Ruchi Yadav,

Ekta Thakor,

Bhumika J. Patel

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Carbapenem-Resistant Pseudomonas aeruginosa’s Resistome: Pan-Genomic Plasticity, the Impact of Transposable Elements and Jumping Genes DOI Creative Commons
Theodoros Karampatakis, Katerina Tsergouli, Payam Behzadi

et al.

Antibiotics, Journal Year: 2025, Volume and Issue: 14(4), P. 353 - 353

Published: March 31, 2025

Pseudomonas aeruginosa, a Gram-negative, motile bacterium, may cause significant infections in both community and hospital settings, leading to substantial morbidity mortality. This opportunistic pathogen can thrive various environments, making it public health concern worldwide. P. aeruginosa’s genomic pool is highly dynamic diverse, with pan-genome size ranging from 5.5 7.76 Mbp. versatility arises its ability acquire genes through horizontal gene transfer (HGT) via different genetic elements (GEs), such as mobile (MGEs). These MGEs, collectively known the mobilome, facilitate spread of encoding resistance antimicrobials (ARGs), heavy metals (HMRGs), virulence (VGs), metabolic functions (MGs). Of particular are acquired carbapenemase (ACGs) other β-lactamase genes, classes A, B [metallo-β-lactamases (MBLs)], D carbapenemases, which lead increased antimicrobial resistance. review emphasizes importance mobilome understanding aeruginosa.

Language: Английский

Citations

2
A comprehensive review of antibiotic resistance gene contamination in agriculture: Challenges and AI-driven solutions DOI

Zhendong Sun,

Weichen Hong,

Chenyu Xue

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 953, P. 175971 - 175971

Published: Sept. 3, 2024

Language: Английский

Citations

7
Artificial intelligence and its application in clinical microbiology DOI
Assia Mairi,

Lamia Hamza,

Abdelaziz Touati

et al.

Expert Review of Anti-infective Therapy, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, advancements, integration clinical microbiology. This examines AI-driven methodologies, including machine learning (ML), deep (DL), convolutional neural networks (CNNs), for enhancing detection, AMR prediction, diagnostic imaging. Applications virology (e.g. COVID-19 RT-PCR optimization), parasitology malaria detection), bacteriology automated colony counting) are analyzed. A literature search was conducted using PubMed, Scopus, Web Science (2018-2024), prioritizing peer-reviewed studies on AI's accuracy, workflow efficiency, validation. AI significantly improves precision operational efficiency but requires robust validation to address data heterogeneity, model interpretability, ethical concerns. Future success hinges interdisciplinary collaboration develop standardized, equitable tools tailored global healthcare settings. Advancing explainable federated frameworks will be critical bridging current implementation gaps maximizing potential combating infectious diseases.

Language: Английский

Citations

0
Atmospheric detection, prevalence, transmission, health and ecological consequences of antibiotic resistance genes and resistant bacteria: A comprehensive review DOI Creative Commons

Fan Liang,

Chun Chen,

Haijie Zhang

et al.

Emerging contaminants, Journal Year: 2025, Volume and Issue: unknown, P. 100514 - 100514

Published: May 1, 2025

Language: Английский

Citations

0
Rise of the Machines - Artificial Intelligence in Healthcare Epidemiology DOI
Lemuel Non, Alexandre R. Marra, Dilek İnce

et al.

Current Infectious Disease Reports, Journal Year: 2024, Volume and Issue: 27(1)

Published: Dec. 26, 2024

Language: Английский

Citations

1
Recent developments in antibiotic resistance: an increasing threat to public health DOI Open Access
Safin Hussein, Sirwan Khalid Ahmed,

Saman M. Mohammed

et al.

Annals of Animal Science, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 27, 2024

Abstract Antibiotic resistance (ABR) is a major global health threat that puts decades of medical progress at risk. Bacteria develop through various means, including modifying their targets, deactivating drugs, and utilizing efflux pump systems. The main driving forces behind ABR are excessive antibiotic use in healthcare agriculture, environmental contamination, gaps the drug development process. advanced detection technologies, such as next-generation sequencing (NGS), clustered regularly interspaced short palindromic repeats (CRISPR)-based diagnostics, metagenomics, has greatly improved identification resistant pathogens. consequences on public significant, increased mortality rates, endangerment modern procedures, resulting higher expenses. It been expected could potentially drive up to 24 million individuals into extreme poverty by 2030. Mitigation strategies focus stewardship, regulatory measures, research incentives, raising awareness. Furthermore, future directions involve exploring potential CRISPR-Cas9 (CRISPR-associated protein 9), nanotechnology, big data analytics new solutions. This review explores resistance, mechanisms, recent trends, drivers, technological advancements detection. also evaluates implications for presents mitigating resistance. emphasizes significance needs, stressing necessity sustained collaborative efforts tackle this issue.

Language: Английский

Citations

0
Molecular Mechanisms Responsible for Drug Resistance DOI

Ruchi Yadav,

Ekta Thakor,

Bhumika J. Patel

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Citations

0