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Cited by 294

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Performance and Cost Analysis of Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry for Routine Identification of Yeast

Open access
Dhiman, Neelam, Hall, Leslie, Wohlfiel, Sherri L., Buckwalter, Seanne P., Wengenack, Nancy L.·2011·Journal of Clinical Microbiology

Abstract

Matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) mass spectrometry was compared to phenotypic testing for yeast identification. MALDI-TOF mass spectrometry yielded 96.3% and 84.5% accurate species level identifications (spectral scores, ≥1.8) for 138 common and 103 archived strains of yeast. MALDI-TOF mass spectrometry is accurate, rapid (5.1 min of hands-on time/identification), and cost-effective ($0.50/sample) for yeast identification in the clinical laboratory.

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Crossref · 10.1128/jcm.02381-10

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Metadata

Authors
Dhiman, N., Hall, L., Wohlfiel, S. L., Buckwalter, S. P., & Wengenack, N. L.
Venue
Journal of Clinical Microbiology
Published
2011-04
Volume
49
Issue
4
Pages
1614-1616
ISSN
  • 0095-1137 (ISSN-L)
  • 1098-660X
OpenAlex
W2135225892
Cited by
294
References
33
FWCI
21.44
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Performance and Cost Analysis of Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry for Routine Identification of Yeast. https://app.cochat.ai/papers/10.1128/jcm.02381-10
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Dhiman, N., Hall, L., Wohlfiel, S. L., Buckwalter, S. P., & Wengenack, N. L. (2011). Performance and Cost Analysis of Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry for Routine Identification of Yeast. Journal of Clinical Microbiology. https://doi.org/10.1128/jcm.02381-10

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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.