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Cited by 236

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

Evaluation of Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry for Identification of Clinically Important Yeast Species

Open access
Stevenson, Lindsay G., Drake, Steven K., Shea, Yvonne R., Zelazny, Adrian M., Murray, Patrick R.·2010·Journal of Clinical Microbiology

Abstract

We evaluated the use of matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) for the rapid identification of yeast species. Using Bruker Daltonics MALDI BioTyper software, we created a spectral database library with m/z ratios of 2,000 to 20,000 Da for 109 type and reference strains of yeast (44 species in 8 genera). The database was tested for accuracy by use of 194 clinical isolates (23 species in 6 genera). A total of 192 (99.0%) of the clinical isolates were identified accurately by MALDI-TOF MS. The MALDI-TOF MS-based method was found to be reproducible and accurate, with low consumable costs and minimal preparation time.

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Metadata

Authors
Stevenson, L. G., Drake, S. K., Shea, Y. R., Zelazny, A. M., & Murray, P. R.
Venue
Journal of Clinical Microbiology
Published
2010-10
Volume
48
Issue
10
Pages
3482-3486
ISSN
  • 0095-1137 (ISSN-L)
  • 1098-660X
OpenAlex
W2057299457
Cited by
236
References
2
FWCI
16.43
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Evaluation of Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry for Identification of Clinically Important Yeast Species. https://app.cochat.ai/papers/10.1128/jcm.00687-09
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Stevenson, L. G., Drake, S. K., Shea, Y. R., Zelazny, A. M., & Murray, P. R. (2010). Evaluation of Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry for Identification of Clinically Important Yeast Species. Journal of Clinical Microbiology. https://doi.org/10.1128/jcm.00687-09

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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.