ArticleJournal of clinical microbiology2021
An Improved Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry Data Analysis Pipeline for the Identification of Carbapenemase-Producing Klebsiella pneumoniae.
Article in Journal of clinical microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- Review
- Accelerated detection ofMicrobiology spectrum · 2026Article
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- Direct Detection of Carbapenemase-Producing Klebsiella pneumoniae by MALDI-TOF Analysis of Full Spectra Applying Machine Learning.Journal of clinical microbiology · 2023Article
- NDM-5-carrying3 Biotech · 2023Article
- Rapid discrimination ofFrontiers in microbiology · 2023Article
- Simultaneous and Visual Detection of KPC and NDM Carbapenemase-Encoding Genes Using Asymmetric PCR and Multiplex Lateral Flow Strip.Journal of analytical methods in chemistry · 2023Article
- Ultrasensitive and rapid identification of ESRI developer- and piperacillin/tazobactam-resistantEmerging microbes & infections · 2022Article
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Authors and funding
15 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing emergence of carbapenemase-producing Klebsiella pneumoniae (CPK) isolates is a global health alarm. Rapid methods that require minimum sample preparation and rapid data analysis are urgently required. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has recently been used by clinical laboratories for identification of antibiotic-resistant bacteria; however, discrepancies have arisen regarding biological and technical issues. The aim of this study was to standardize an operating procedure and data analysis for identification of CPK by MALDI-TOF MS. To evaluate this approach, a series of 162 K. pneumoniae isolates (112 CPK and 50 non-CPK) were processed in the MALDI BioTyper system (Bruker Daltonik, Germany) following a standard operating procedure. The study was conducted in two stages; the first is denominated the "reproducibility stage" and the second "CPK identification." The first stage was designed to evaluate the biological and technical variation associated with the entire analysis of CPK and the second stage to assess the final accuracy of MALDI-TOF MS for the identification of CPK. Therefore, we present an improved MALDI-TOF MS data analysis pipeline using neural network analysis implemented in Clover MS Data Analysis Software (Clover Biosoft, Spain) that is designed to reduce variability, guarantee interlaboratory reproducibility, and maximize the information selected from the bacterial proteome. Using the random forest (RF) algorithm, 100% of CPK isolates were correctly identified when all the peaks in the spectra were selected as input features and total ion current (TIC) normalization was applied. Thus, we have demonstrated that real-time direct tracking of CPK is possible using MALDI-TOF MS.
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