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Urine Proteomics in Kidney Disease Biomarker Discovery

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Cover of 'Urine Proteomics in Kidney Disease Biomarker Discovery'

Table of Contents

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    Book Overview
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    Chapter 1 Urine Is a Better Biomarker Source Than Blood Especially for Kidney Diseases
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    Chapter 2 Urine Reflection of Changes in Blood
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    Chapter 3 Urimem Facilitates Kidney Disease Biomarker Research
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    Chapter 4 Human urine proteome: a powerful source for clinical research.
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    Chapter 5 Exosomes in Urine Biomarker Discovery
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    Chapter 6 Urinary Proteins with Post-translational Modifications.
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    Chapter 7 Applications of Peptide retention time in proteomic data analysis.
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    Chapter 8 Urine Sample Preparation in 96-well Filter Plates to Characterize Inflammatory and Infectious Diseases of the Urinary Tract.
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    Chapter 9 Variations of human urinary proteome.
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    Chapter 10 Evolution of the urinary proteome during human renal development and maturation.
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    Chapter 11 Hormone-dependent changes in female urinary proteome.
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    Chapter 12 Effects of exercise on the urinary proteome.
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    Chapter 13 Effects of diuretics on urinary proteins.
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    Chapter 14 Applications of urinary proteomics in renal disease research using animal models.
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    Chapter 15 The application of urinary proteomics for the detection of biomarkers of kidney diseases.
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    Chapter 16 Dynamic changes of urinary proteins in focal segmental glomerulosclerosis model.
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    Chapter 17 Using isolated rat kidney to discover kidney origin biomarkers in urine.
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    Chapter 18 Comparing plasma and urinary proteomes to understand kidney function.
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    Chapter 19 Urinary protein biomarker database: a useful tool for biomarker discovery.
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Urine Proteomics in Kidney Disease Biomarker Discovery
Published by
Springer Netherlands, October 2014
DOI 10.1007/978-94-017-9523-4
978-9-40-179522-7, 978-9-40-179523-4

Gao, Youhe

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Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 28 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 24%
Researcher 7 24%
Student > Master 7 24%
Professor > Associate Professor 3 10%
Student > Bachelor 2 7%
Other 3 10%
Readers by discipline Count As %
Medicine and Dentistry 7 24%
Biochemistry, Genetics and Molecular Biology 6 21%
Chemistry 5 17%
Agricultural and Biological Sciences 4 14%
Computer Science 2 7%
Other 5 17%