| Chapter title |
Serum and plasma proteomics and its possible use as detector and predictor of radiation diseases.
|
|---|---|
| Chapter number | 4 |
| Book title |
Radiation Proteomics
|
| Published in |
Advances in experimental medicine and biology, January 2013
|
| DOI | 10.1007/978-94-007-5896-4_4 |
| Pubmed ID | |
| Book ISBNs |
978-9-40-075895-7, 978-9-40-075896-4
|
| Authors |
Olivier Guipaud, Guipaud, Olivier |
| Abstract |
All tissues can be damaged by ionizing radiation. Early biomarkers of radiation injury are critical for triage, treatment and follow-up of large numbers of people exposed to ionizing radiation after terrorist attacks or radiological accident, and for prediction of normal tissue toxicity before, during and after a treatment by radiotherapy. The comparative proteomic approach is a promising and powerful tool for the discovery of new radiation biomarkers. In association with multivariate statistics, proteomics enables measurement of the level of hundreds or thousands of proteins at the same time and identifies set of proteins that can discriminate between different groups of individuals. Human serum and plasma are the preferred samples for the study of normal and disease-associated proteins. Extreme complexity, extensive dynamic range, genetic and physiological variations, protein modifications and incompleteness of sampling by two-dimensional electrophoresis and mass spectrometry represent key challenges to reproducible, high-resolution, and high-throughput analyses of serum and plasma proteomes. The future of radiation research will possibly lie in molecular networks that link genome, transcriptome, proteome and metabolome variations to radiation pathophysiology and serve as sensors of radiation disease. This chapter reviews recent advances in proteome analysis of serum and plasma as well as its applications to radiation biology and radiation biomarker discovery for both radiation exposure and radiation tissue toxicity. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 5% |
| Unknown | 19 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 25% |
| Researcher | 4 | 20% |
| Professor > Associate Professor | 3 | 15% |
| Student > Bachelor | 2 | 10% |
| Student > Master | 2 | 10% |
| Other | 2 | 10% |
| Unknown | 2 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 4 | 20% |
| Medicine and Dentistry | 4 | 20% |
| Biochemistry, Genetics and Molecular Biology | 2 | 10% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 10% |
| Engineering | 2 | 10% |
| Other | 3 | 15% |
| Unknown | 3 | 15% |