Evidence map›Paper›PMID 42536802›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Rapid Multiplex Antimicrobial Resistance Profiling and Bacterial Identification by LAP-MALDI Mass Spectrometry Biotyping.

Lily R Adair, Shabnam Iyer, Ian M Jones, Rainer Cramer

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lily R AdairDepartment of Chemistry, University of Reading, Reading, Berkshire, UK.ORCID https://orcid.org/0009-0001-2652-9960
Shabnam IyerRoyal Berkshire NHS Foundation Trust, Reading, Berkshire, UK.
Ian M JonesSchool of Biological Sciences, University of Reading, Reading, Berkshire, UK.ORCID https://orcid.org/0000-0002-7738-2516
Rainer CramerDepartment of Chemistry, University of Reading, Reading, Berkshire, UK.ORCID https://orcid.org/0000-0002-8037-2511

Funding

Engineering and Physical Sciences Research Council EP/V047485/1
6 · The paper itself

Abstract

Rapid and accurate characterization of antimicrobial resistance is essential for effective patient treatment and outcomes. Infection-causing microorganisms often harbour multi-drug resistance, requiring multiple tests for identification. Here, we present a multiplex functional assay using liquid atmospheric pressure (LAP) matrix-assisted laser desorption/ionization (MALDI) as next-generation MALDI biotyping technology, which can accurately determine antibiotic resistance/susceptibility within three hours from <5 µL of bacterial culture, employing a beta-lactam antibiotic panel. Strains with common resistance genes, including OXA-48, KPC-3, and VIM-1, as well as susceptible isolates, are easily and reliably classified. Concurrent with multi-drug testing, the same bacterial sample provides species-identifying lipid and protein profiles (up to 100% classification accuracy) and characterization through tandem mass spectrometry (MS/MS) protein sequencing, facilitated by LAP-MALDI's ability to generate multiply charged protein ions. Detection of multiple antibiotics, their degradation products and lipids/proteins with high mass accuracy, along with the possibility of protein sequencing, offers new diagnostic possibilities for clinical microbiology and antimicrobial stewardship that are less probability-based than conventional MALDI biotyping.

Indexed as

antimicrobial resistanceclinical microbiologyLAP‐MALDI mass spectrometryMALDI biotypingtop‐down proteomics

Identifiers

PMID42536802
PMCPMC13427227

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Registered trials

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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.