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Cited by 534

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

MALDI-TOF MS for the Diagnosis of Infectious Diseases

Open access
Patel, Robin·2015·Clinical Chemistry

Abstract

BACKGROUND: First introduced into clinical microbiology laboratories in Europe, MALDI-TOF MS is being rapidly embraced by laboratories around the globe. Although it has multiple applications, its widespread adoption in clinical microbiology relates to its use as an inexpensive, easy, fast, and accurate method for identification of grown bacteria and fungi based on automated analysis of the mass distribution of bacterial proteins. CONTENT: This review provides a historical perspective on this new technology. Modern applications in the clinical microbiology laboratory are reviewed with a focus on the most recent publications in the field. Identification of aerobic and anaerobic bacteria, mycobacteria, and fungi are discussed, as are applications for testing urine and positive blood culture bottles. The strengths and limitations of MALDI-TOF MS applications in clinical microbiology are also addressed. SUMMARY: MALDI-TOF MS is a tool for rapid, accurate, and cost-effective identification of cultured bacteria and fungi in clinical microbiology. The technology is automated, high throughput, and applicable to a broad range of common as well as esoteric bacteria and fungi. MALDI-TOF MS is an incontrovertibly beneficial technology for the clinical microbiology laboratory.

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Metadata

Authors
Patel, R.
Venue
Clinical Chemistry
Published
2015-01-01
Volume
61
Issue
1
Pages
100-111
ISSN
  • 0009-9147 (ISSN-L)
  • 1530-8561
OpenAlex
W2087310128
Cited by
534
References
2
FWCI
18.86
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: MALDI-TOF MS for the Diagnosis of Infectious Diseases. https://app.cochat.ai/papers/10.1373/clinchem.2014.221770
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Patel, R. (2015). MALDI-TOF MS for the Diagnosis of Infectious Diseases. Clinical Chemistry. https://doi.org/10.1373/clinchem.2014.221770

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.