ArticleMethods in molecular biology (Clifton, N.J.)2025
Integrating MALDI-TOF Mass Spectrometry with Machine Learning Techniques for Rapid Antimicrobial Resistance Screening of Foodborne Bacterial Pathogens.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Rapid identification of carbapenem-resistant Acinetobacter baumannii based on MALDI-TOF mass spectrometry and machine learning.BMC microbiology · 2026Article
- Spectral Precision: Recent Advances in Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry for Pathogen Detection and Resistance Profiling.Microorganisms · 2025Review
- Healthcare-Associated Infections: The Role of Microbial and Environmental Factors in Infection Control-A Narrative Review.Infectious diseases and therapy · 2025Review
- Microfluidic biosensors for rapid detection of foodborne pathogenic bacteria: recent advances and future perspectives.Frontiers in chemistry · 2025Review
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
Although MALDI-TOF mass spectrometry (MS) is considered as the gold standard for rapid and cost-effective identification of microorganisms in routine laboratory practices, its capability for antimicrobial resistance (AMR) detection has received limited focus. Nevertheless, recent studies explored the predictive performance of MALDI-TOF MS for detecting AMR in clinical pathogens when machine learning techniques are applied. This chapter describes a routine MALDI-TOF MS workflow for the rapid screening of AMR in foodborne pathogens, with Campylobacter spp. as a study model.
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Registered trials
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