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1. Introduction
2. Mammals are complex organisms
3. Microscopic structures reveal high complexity
4. Single biomarkers are of limited specificity
5. Limited precision of single marker X
6. Increased precision of two markers, X and Y
7. Higher precision of 3 markers, X, Y and Z
8. Omics technologies or: why proteomics?
9. Clinical proteomics: recommended steps
10. Clinical diagnosis of diabetic nephropathy
11. The course of diabetic nephropathy
12. Why urine?
13. Why not blood?
14. Technology platforms: 2DE-MS
15. Technology platforms: LC-MS-MS
16. Technology platforms: CE/MS
17. CE/MS movie
18. CE/MS data flow
19. Graphical depiction of CE/MS analysis
20. Human urinary LMW proteome database
21. Requirements for biomarker selection
22. Classification using n-dimensional models
23. Valid biomarkers and biomarker models
24. How to identify valid proteomic biomarkers (1)
25. How to identify valid proteomic biomarkers (2)
26. Effect size estimation and the number of samples
27. Erroneous biomarkers
28. Sample size reflects the number of biomarkers
29. Classification using different sizes of training sets
30. Classification error depends on sample size
31. Example
32. Proteomic CKD biomarker discovery
33. CKD-biomarkers and their regulation (1)
34. CKD-biomarkers and their regulation (2)
35. Biomarker validation
36. Pathophysiological suggestions
37. Selected urinary peptides and CKD staging
38. DN progression in diabetes type 2 patients
39. Prediction of DN
40. CKD biomarker profile vs. AER for DN detection
41. Multicentric European PRIORITY trial
42. Coronary artery disease
43. Proteomics of coronary artery disease
44. Assessment of therapy success
45. Results
46. Summary

Topics Covered

• Omics technologies
• Technology platforms: (2DE-MS, LC-MS-MS, CE/MS)
• Clinical proteomics
• Precision as a function of no. of markers
• Human urinary LMW proteome database
• Requirements for biomarker selection
• How to identify valid proteomic biomarkers
• Effect size estimation and the number of samples
• Erroneous biomarkers
• Classification using different sizes of training sets
• Classification errors
• CKD biomarkers
• Biomarker validation
• Pathophysiological suggestions
• Selected urinary peptides and CKD staging
• Multicentric European PRIORITY trial
• Proteomics of coronary artery disease and of diabetic nephropathy

Links

Series:

• Clinical Proteomics
• The Kidney in Health and Disease

Categories:

• Biochemistry
• Cell Biology
• Clinical Practice
• Methods

Therapeutic Areas:

• Cardiovascular & Metabolic
• Gastroenterology & Nephrology

Talk Citation

Mischak, H. (2013, November 5). Urinary proteomics in kidney and cardiovascular disease [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved April 16, 2025, from https://doi.org/10.69645/BTPJ2409.
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Publication History

• Published on November 5, 2013

Financial Disclosures

• Prof. Harald Mischak, Stock Shareholder (Self-managed): Mosaiques Diagnostics.