Large-scale genomic analysis reveals significant role of insertion sequences in antimicrobial resistance of Acinetobacter baumannii DOI Creative Commons
Fei Xie, Lifeng Wang, Song Li

et al.

mBio, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 20, 2025

ABSTRACT Acinetobacter baumannii , a prominent nosocomial pathogen renowned for its extensive resistance to antimicrobial agents, poses significant challenge in the accurate prediction of (AMR) from genomic data. Despite thorough researches on molecular mechanisms AMR, gaps remain our understanding key contributors. This study utilized rule-based and three machine learning models predict AMR phenotypes, aiming decipher factors associated with AMR. Genomes antibiotic phenotypes 1,012 public isolates were employed model construction training. To validate models, data set comprising 164 self-collected strains underwent next-generation sequencing, nanopore long-read susceptibility testing using broth dilution method. It was found that presence genes (ARGs) alone insufficient accurately phenotype majority antibiotics (90%, 18 out 20) set. Conversely, it observed combining ARGs insertion sequence (IS) elements significantly enhanced predictive performance. The Random Forest outperform support vector (SVM), logistic regression model, method across all 20 antibiotics, accuracies ranging 83.80% 97.70%. In validation set, even higher achieved, 85.63% 99.31%. Furthermore, conserved patterns between IS validated sequencing data, substantially enhancing accuracy A. . underscores pivotal role IMPORTANCE interplay sequences (ISs) contributes against specific antibiotics. Conventionally, genetic variations have been predicting potential largely overlooked. Our advances this approach by integrating both enhances prediction, emphasizing function resistance. Notably, we uncover series linking ARGs, which phenotypic prediction. findings are crucial bioinformatics strategies aimed at studying tracking offering novel insights into combating escalating challenge.

Language: Английский

Antimicrobial resistance: Impacts, challenges, and future prospects DOI Creative Commons
Sirwan Khalid Ahmed, Safin Hussein, Karzan Qurbani

et al.

Journal of Medicine Surgery and Public Health, Journal Year: 2024, Volume and Issue: 2, P. 100081 - 100081

Published: March 2, 2024

Antimicrobial resistance (AMR) is a critical global health issue driven by antibiotic misuse and overuse in various sectors, leading to the emergence of resistant microorganisms. The history AMR dates back discovery penicillin, with rise multidrug-resistant pathogens posing significant challenges healthcare systems worldwide. antibiotics human animal health, as well agriculture, contributes spread genes, creating "Silent Pandemic" that could surpass other causes mortality 2050. affects both humans animals, treating infections. Various mechanisms, such enzymatic modification biofilm formation, enable microbes withstand effects antibiotics. lack effective threatens routine medical procedures lead millions deaths annually if left unchecked. economic impact substantial, projected losses trillions dollars financial burdens on agriculture. Artificial intelligence being explored tool combat improving diagnostics treatment strategies, although data quality algorithmic biases exist. To address effectively, One Health approach considers human, animal, environmental factors crucial. This includes enhancing surveillance systems, promoting stewardship programs, investing research development for new antimicrobial options. Public awareness, education, international collaboration are essential combating preserving efficacy future generations.

Language: Английский

Citations

204
Genomic surveillance for antimicrobial resistance — a One Health perspective DOI
Steven P. Djordjevic, Veronica M. Jarocki, Torsten Seemann

et al.

Nature Reviews Genetics, Journal Year: 2023, Volume and Issue: 25(2), P. 142 - 157

Published: Sept. 25, 2023

Language: Английский

Citations

106
Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician DOI Creative Commons
Anastasia A Theodosiou, Robert C. Read

Journal of Infection, Journal Year: 2023, Volume and Issue: 87(4), P. 287 - 294

Published: July 17, 2023

BackgroundArtificial intelligence (AI), machine learning and deep (including generative AI) are increasingly being investigated in the context of research management human infection.ObjectivesWe summarise recent potential future applications AI its relevance to clinical infection practice.Methods1,617 PubMed results were screened, with priority given trials, systematic reviews meta-analyses. This narrative review focusses on studies using prospectively collected real-world data validation, translational potential, such as novel drug discovery microbiome-based interventions.ResultsThere is some evidence utility applied laboratory diagnostics (e.g. digital culture plate reading, malaria diagnosis, antimicrobial resistance profiling), imaging analysis pulmonary tuberculosis diagnosis), decision support tools sepsis prediction, prescribing) public health outbreak COVID-19). Most date lack any validation or metrics. Significant heterogeneity study design reporting limits comparability. Many practical ethical issues exist, including algorithm transparency risk bias.ConclusionsInterest development AI-based for undoubtedly gaining pace, although appears much more modest.

Language: Английский

Citations

99
Real-time genomic surveillance for enhanced control of infectious diseases and antimicrobial resistance DOI Creative Commons
Marc Struelens, Catherine Ludden, Guido Werner

et al.

Frontiers in Science, Journal Year: 2024, Volume and Issue: 2

Published: April 25, 2024

This article advocates for mobilizing pathogen genomic surveillance to contain and mitigate health threats from infectious diseases antimicrobial resistance (AMR), building upon successes achieved by large-scale genome sequencing analysis of SARS-CoV-2 variants in guiding COVID-19 monitoring public responses adopting a One Health approach. Capabilities laboratory-based epidemic alert systems should be enhanced fostering (i) universal access real-time whole sequence (WGS) data pathogens inform clinical practice, infection control, policies, vaccine drug research development; (ii) integration diagnostic microbiology data, testing asymptomatic individuals, epidemiological into programs; (iii) stronger cross-sectorial collaborations between healthcare, health, animal environmental using approaches, toward understanding the ecology transmission pathways AMR across ecosystems; (iv) international collaboration interconnection networks, harmonization laboratory methods, standardization methods global reporting, including on variant or strain nomenclature; (v) responsible sharing databases, platforms according FAIR (findability, accessibility, interoperability, reusability) principles; (vi) system implementation its cost-effectiveness different settings. Regional policies governance initiatives foster concerted development efficient utilization protect humans, animals, environment.

Language: Английский

Citations

25
Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future DOI Creative Commons
Francesco Branda, Fabio Scarpa

Antibiotics, Journal Year: 2024, Volume and Issue: 13(6), P. 502 - 502

Published: May 29, 2024

Antibiotic resistance poses a significant threat to global public health due complex interactions between bacterial genetic factors and external influences such as antibiotic misuse. Artificial intelligence (AI) offers innovative strategies address this crisis. For example, AI can analyze genomic data detect markers early on, enabling interventions. In addition, AI-powered decision support systems optimize use by recommending the most effective treatments based on patient local patterns. accelerate drug discovery predicting efficacy of new compounds identifying potential antibacterial agents. Although progress has been made, challenges persist, including quality, model interpretability, real-world implementation. A multidisciplinary approach that integrates with other emerging technologies, synthetic biology nanomedicine, could pave way for prevention mitigation antimicrobial resistance, preserving antibiotics future generations.

Language: Английский

Citations

18
The Role of Artificial Intelligence and Machine Learning in Predicting and Combating Antimicrobial Resistance DOI Creative Commons
Hazrat Bilal, Muhammad Nadeem Khan, Sabir Khan

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: 27, P. 423 - 439

Published: Jan. 1, 2025

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised deep reinforcement natural language processing are some main tools used this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, epidemiological for predicting AMR outbreaks. Although relatively new fields, numerous case studies offer substantial evidence their successful application outbreaks with greater accuracy. These provide insights into discovery novel antimicrobials, repurposing existing drugs, combination therapy through analysis molecular structures. In addition, AI-based decision support systems real-time guide healthcare professionals improve prescribing antibiotics. also outlines how AI surveillance, analyze trends, enable early outbreak identification. Challenges, ethical considerations, privacy, model biases exist, however, continuous development methodologies enables play significant combating

Language: Английский

Citations

5
Innovations in genomic antimicrobial resistance surveillance DOI Creative Commons
Nicole E. Wheeler, Vivien Price, Edward Cunningham-Oakes

et al.

The Lancet Microbe, Journal Year: 2023, Volume and Issue: 4(12), P. e1063 - e1070

Published: Nov. 14, 2023

Whole-genome sequencing of antimicrobial-resistant pathogens is increasingly being used for antimicrobial resistance (AMR) surveillance, particularly in high-income countries. Innovations genome and analysis technologies promise to revolutionise AMR surveillance epidemiology; however, routine adoption these challenging, low-income middle-income As part a wider series workshops online consultations, group experts pathogen genomics computational tool development conducted situational analysis, identifying the following under-used innovations genomic surveillance: clinical metagenomics, environmental gene or plasmid tracking, machine learning. The recommended developing cost-effective use cases each approach mapping data outputs outcomes interest justify additional investment capacity, training, staff required implement technologies. Harmonisation standardisation methods, creation equitable sharing governance frameworks, will facilitate successful implementation innovations.

Language: Английский

Citations

33
From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance – a Comprehensive Review DOI Creative Commons
José Manuel Pérez de la Lastra, Samuel J. T. Wardell, Tarun Pal

et al.

Journal of Medical Systems, Journal Year: 2024, Volume and Issue: 48(1)

Published: Aug. 1, 2024

Abstract The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims explore the role AI/ML in AMR management, with focus on identifying pathogens, understanding patterns, predicting treatment outcomes, discovering new antibiotic agents. Recent advancements enabled efficient analysis large datasets, facilitating reliable prediction trends responses minimal human intervention. ML can analyze genomic data identify genetic markers associated resistance, enabling development targeted strategies. Additionally, techniques show promise optimizing drug administration developing alternatives traditional antibiotics. By analyzing patient clinical these technologies assist healthcare providers diagnosing infections, evaluating their severity, selecting appropriate therapies. While integration settings is still its infancy, quality algorithm suggest that widespread adoption forthcoming. conclusion, holds improving management outcome.

Language: Английский

Citations

14
Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records DOI Creative Commons
Masayuki Nigo, Laila Rasmy, Bingyu Mao

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: March 6, 2024

Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific to predict culture positivity within two weeks. 8,164 22,393 non-MRSA events from Memorial Hermann Hospital System, Houston, Texas are used model development. PyTorch_EHR outperforms logistic regression (LR) light gradient boost machine (LGBM) models accuracy (AUROC

Language: Английский

Citations

13
Healthcare as a driver, reservoir and amplifier of antimicrobial resistance: opportunities for interventions DOI
Derek Cocker, Gabriel Birgand, Nina Zhu

et al.

Nature Reviews Microbiology, Journal Year: 2024, Volume and Issue: 22(10), P. 636 - 649

Published: July 24, 2024

Language: Английский

Citations

12