ArticleNature communications2024
Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 39 citations in OpenAlex.
- Concentrations of ciprofloxacin in food defined as safe alter the gut microbiome and ciprofloxacin susceptibility in humans: an interventional clinical study.Scientific reports · 2025Trial
- Rapid delineation ofMicrobiology spectrum · 2026Article
- Multiplexed, rapid phenotypic antibiotic susceptibility testing based on angle-resolved light scattering imaging of microfluidic droplets.Journal of advanced research · 2026Article
- CApEsid biOfilm: a suggested pipeline for clinical phage microbiology for biofilm infections based on comparative method study.Scientific reports · 2026Article
- Integrated iPRISM Direct-on-Urine Platform for Rapid UTI Diagnosis in a Double-Blind Clinical Trial.ACS measurement science au · 2026Article
- Nanomotion-enabled ultra-rapid antibiotic susceptibility testing with magnetic bead-based pathogen enrichment for accelerated sepsis diagnostics.Scientific reports · 2026Article
- Molecular insights into antimicrobial resistance in human bacterial pathogens: mechanisms, resistance genes, and translational diagnostic applications.Frontiers in microbiology · 2026Review
- Antibiotic resistance inFrontiers in microbiology · 2026Review
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- Diagnostic stewardship in sepsis: minding the gap from the microbiology lab to ward.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2025Review
- Unveiling the real-time behaviour of Staphylococcus aureus through nanomotion sensing.Scientific reports · 2025Article
- Nanomotion-Based Drug Sensitivity Prediction in Ovarian and Colon Cancer Cell Lines Using Machine Learning.ACS pharmacology & translational science · 2025Article
- Review
- Nanomotion technology for testing azithromycin susceptibility ofMicrobiology spectrum · 2025Article
- Effect of host microenvironment and bacterial lifestyles on antimicrobial sensitivity and implications for susceptibility testing.npj antimicrobials and resistance · 2025Review
- Article
- Modifying the severity and appearance of psoriasis using deep learning to simulate anticipated improvements during treatment.Scientific reports · 2025Article
- Ribosome phenotypes for rapid classification of antibiotic-susceptible and resistant strains of Escherichia coli.Communications biology · 2025Article
- Real-time evaluation of macozinone activity againstAntimicrobial agents and chemotherapy · 2025Article
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) is a major public health threat, reducing treatment options for infected patients. AMR is promoted by a lack of access to rapid antibiotic susceptibility tests (ASTs). Accelerated ASTs can identify effective antibiotics for treatment in a timely and informed manner. We describe a rapid growth-independent phenotypic AST that uses a nanomotion technology platform to measure bacterial vibrations. Machine learning techniques are applied to analyze a large dataset encompassing 2762 individual nanomotion recordings from 1180 spiked positive blood culture samples covering 364 Escherichia coli and Klebsiella pneumoniae isolates exposed to cephalosporins and fluoroquinolones. The training performances of the different classification models achieve between 90.5 and 100% accuracy. Independent testing of the AST on 223 strains, including in clinical setting, correctly predict susceptibility and resistance with accuracies between 89.5% and 98.9%. The study shows the potential of this nanomotion platform for future bacterial phenotype delineation.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.