AIMC Topic: Drug Resistance, Bacterial
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Characterization of the prevalence of Salmonella in different retail chicken supply modes using genome-wide and machine-learning analyses.
Food research international (Ottawa, Ont.)
Jun 19, 2024
Salmonella is a foodborne pathogen that causes salmonellosis, of which retail chicken meat is a major source. However, the prevalence of Salmonella in different retail chicken supply modes and the threat posed to consumers remains unclear. The preval...
Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of Salmonella Typhimurium.
Nature communications
Jun 13, 2024
Antimicrobial resistance (AMR) is a growing public health crisis that requires innovative solutions. Current susceptibility testing approaches limit our ability to rapidly distinguish between antimicrobial-susceptible and -resistant organisms. Salmon...
Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy.
Proceedings of the National Academy of Sciences of the United States of America
Jun 11, 2024
Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organism (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen's antibiot...
Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis.
ACS synthetic biology
Jun 6, 2024
Antimicrobial resistance (AMR) poses a critical global One Health concern, ensuing from unintentional and continuous exposure to antibiotics, as well as challenges in accurate contagion diagnostics. Addressing AMR requires a strategic approach that e...
Data-driven learning of structure augments quantitative prediction of biological responses.
PLoS computational biology
Jun 3, 2024
Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experimen...
Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning.
BMC medical informatics and decision making
May 14, 2024
BACKGROUND: Predicting whether Carbapenem-Resistant Gram-Negative Bacterial (CRGNB) cause bloodstream infection when giving advice may guide the use of antibiotics because it takes 2-5 days conventionally to return the results from doctor's order.
ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences.
Microbiome
May 9, 2024
BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identific...
Application of machine learning for antibiotic resistance in water and wastewater: A systematic review.
Chemosphere
May 2, 2024
Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept...
Characterising global antimicrobial resistance research explains why One Health solutions are slow in development: An application of AI-based gap analysis.
Environment international
Apr 20, 2024
The global health crisis posed by increasing antimicrobial resistance (AMR) implicitly requires solutions based a One Health approach, yet multisectoral, multidisciplinary research on AMR is rare and huge knowledge gaps exist to guide integrated acti...
The impact of artificial intelligence in the fight against antimicrobial resistance.
Infectious diseases (London, England)
Mar 21, 2024
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