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Human Fungal Pathogen Identification

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Cover of 'Human Fungal Pathogen Identification'

Table of Contents

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    Book Overview
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    Chapter 1 Current Challenges in the Diagnosis of Fungal Infections.
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    Chapter 2 Human Fungal Pathogen Identification
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    Chapter 3 Current Algorithms in Fungal Diagnosis in the Immunocompromised Host.
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    Chapter 4 Commercial Molecular Tests for Fungal Diagnosis from a Practical Point of View.
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    Chapter 5 Systemic Antifungal Agents: Current Status and Projected Future Developments.
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    Chapter 6 Fungal-Grade Reagents and Materials for Molecular Analysis.
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    Chapter 7 Host-Derived Biomarkers for Risk Assessment of Invasive Fungal Diseases.
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    Chapter 8 Assessment of Immune Responses to Fungal Infections: Identification and Characterization of Immune Cells in the Infected Tissue.
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    Chapter 9 Histopathology.
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    Chapter 10 Culture-Based Techniques.
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    Chapter 11 Serological Approaches.
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    Chapter 12 Isolation of Nucleic Acids for Fungal Diagnosis.
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    Chapter 13 Prerequisites for Control of Contamination in Fungal Diagnosis.
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    Chapter 14 Broad-Spectrum Molecular Detection of Fungal Nucleic Acids by PCR-Based Amplification Techniques.
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    Chapter 15 Genus- and Species-Specific PCR Detection Methods.
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    Chapter 16 Identification of Fungal Pathogens in Tissue Samples from Patients with Proven Invasive Infection by Fluorescence In Situ Hybridization.
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    Chapter 17 Nuclear Magnetic Resonance Spectroscopy-Based Identification of Yeast.
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    Chapter 18 T2 Magnetic Resonance for Fungal Diagnosis.
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    Chapter 19 Fungal Species Identification by MALDI-ToF Mass Spectrometry.
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    Chapter 20 Immunological Identification of Fungal Species.
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    Chapter 21 Human Fungal Pathogen Identification
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    Chapter 22 Microarray Technologies in Fungal Diagnostics.
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    Chapter 23 Molecular Detection of Resistance to Echinocandins.
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    Chapter 24 Molecular Detection of Resistance to Azole Components.
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    Chapter 25 Immune Cell-Supplemented Human Skin Model for Studying Fungal Infections.
Attention for Chapter 17: Nuclear Magnetic Resonance Spectroscopy-Based Identification of Yeast.
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Chapter title
Nuclear Magnetic Resonance Spectroscopy-Based Identification of Yeast.
Chapter number 17
Book title
Human Fungal Pathogen Identification
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6515-1_17
Pubmed ID
Book ISBNs
978-1-4939-6513-7, 978-1-4939-6515-1
Authors

Uwe Himmelreich, Tania C. Sorrell, Heide-Marie Daniel

Editors

Thomas Lion

Abstract

Rapid and robust high-throughput identification of environmental, industrial, or clinical yeast isolates is important whenever relatively large numbers of samples need to be processed in a cost-efficient way. Nuclear magnetic resonance (NMR) spectroscopy generates complex data based on metabolite profiles, chemical composition and possibly on medium consumption, which can not only be used for the assessment of metabolic pathways but also for accurate identification of yeast down to the subspecies level. Initial results on NMR based yeast identification where comparable with conventional and DNA-based identification. Potential advantages of NMR spectroscopy in mycological laboratories include not only accurate identification but also the potential of automated sample delivery, automated analysis using computer-based methods, rapid turnaround time, high throughput, and low running costs.We describe here the sample preparation, data acquisition and analysis for NMR-based yeast identification. In addition, a roadmap for the development of classification strategies is given that will result in the acquisition of a database and analysis algorithms for yeast identification in different environments.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 27%
Student > Bachelor 2 18%
Student > Ph. D. Student 1 9%
Researcher 1 9%
Student > Postgraduate 1 9%
Other 0 0%
Unknown 3 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 18%
Agricultural and Biological Sciences 2 18%
Business, Management and Accounting 1 9%
Medicine and Dentistry 1 9%
Chemistry 1 9%
Other 1 9%
Unknown 3 27%