Biophotonics, Tryptophan and Disease is a comprehensive resource on the key role of tryptophan in wide range of diseases as seen by using optics techniques. It explores the use of fluorescence spectroscopy, Raman, imaging techniques and time-resolved spectroscopy in normal and diseased tissues and shows the reader how light techniques (i.e. spectroscopy and imaging) can be used to detect, distinguish and evaluate diseases. Diseases covered include cancer, neurodegenerative diseases and other age-related diseases.
Biophotonics, Tryptophan and Disease offers a clear presentation of techniques and integrates material from different disciplines into one resource. It is a valuable reference for students and interdisciplinary researchers working on the interface between biochemistry and molecular biology, translational medicine, and biophotonics.
1. Biophotonics, Tryptophan and Disease - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Dedication - p. Page v 7. Contributors - p. Page v 8. About the editors - p. Page vii 9. Preface - p. Page viii 10. Acknowledgments - p. Page ix 11. List of Illustrations - p. Page x 12. List of Tables - p. Page xi 13. Chapter 1 : The physics of key biophotonic techniques - p. Page xi 14. Chapter 2 : Tryptophan analysis using multiphoton microscopy and fluorescence lifetime imaging - p. Page 8 15. Chapter 3 : Deep-ultraviolet microscopy for tryptophan label-free imaging in cells and tissue - p. Page 22 16. Chapter 4 : Tryptophan as a biomarker using terahertz spectroscopy - p. Page 38 17. Chapter 5 : The role of tryptophan in Chagas disease and other trypanosomatid infections - p. Page 49 18. Chapter 6 : Tryptophan fluorescence for early evaluation of cataracts - p. Page 65 19. Chapter 7 : Tryptophan, after inflammatory cytokine stimulation, determines plaque vulnerability and risk of myocardial infarction - p. Page 79 20. Chapter 8 : Tryptophan and metabolites (serotonin and kynurenines) in posttraumatic stress disorder - p. Page 88 21. Chapter 9 : Effects of tryptophan metabolism on the brain: From early development to Alzheimer's disease - p. Page 101 22. Chapter 10 : Excess activity of 3-hydroxykynurenine, quinolinic acid, and other toxic tryptophan metabolites in neurogenerative diseases and other protein misfolding diseases - p. Page 111 23. Chapter 11 : Tryptophan and kynurenine levels in patients with obstructive sleep apnea syndrome - p. Page 126 24. Chapter 12 : Fluorescence-based techniques using plasma: A unique biomarker for different cancers - p. Page 133 25. Chapter 13 : Synchronous luminescence spectroscopy of tryptophan in head and neck cancer - p. Page 143 26. Chapter 14 : Tryptophan fluorescence for diagnosis and staging of gastrointestinal cancers - p. Page 154 27. Chapter 15 : Tryptophan fluorescence and machine learning to study the aggressiveness of prostate cancer cell lines: A pilot study - p. Page 167 28. Chapter 16 : The principles of machine learning algorithms: Applications to biophotonics and disease - p. Page 179 29. Index - p. Page 196
Biophotonics, Tryptophan and Disease is a comprehensive resource on the key role of tryptophan in wide range of diseases as seen by using optics techniques. It explores the use of fluorescence spectroscopy, Raman, imaging techniques and time-resolved spectroscopy in normal and diseased tissues and shows the reader how light techniques (i.e. spectroscopy and imaging) can be used to detect, distinguish and evaluate diseases. Diseases covered include cancer, neurodegenerative diseases and other age-related diseases.
Biophotonics, Tryptophan and Disease offers a clear presentation of techniques and integrates material from different disciplines into one resource. It is a valuable reference for students and interdisciplinary researchers working on the interface between biochemistry and molecular biology, translational medicine, and biophotonics.
1. Biophotonics, Tryptophan and Disease - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Dedication - p. Page v 7. Contributors - p. Page v 8. About the editors - p. Page vii 9. Preface - p. Page viii 10. Acknowledgments - p. Page ix 11. List of Illustrations - p. Page x 12. List of Tables - p. Page xi 13. Chapter 1 : The physics of key biophotonic techniques - p. Page xi 14. Chapter 2 : Tryptophan analysis using multiphoton microscopy and fluorescence lifetime imaging - p. Page 8 15. Chapter 3 : Deep-ultraviolet microscopy for tryptophan label-free imaging in cells and tissue - p. Page 22 16. Chapter 4 : Tryptophan as a biomarker using terahertz spectroscopy - p. Page 38 17. Chapter 5 : The role of tryptophan in Chagas disease and other trypanosomatid infections - p. Page 49 18. Chapter 6 : Tryptophan fluorescence for early evaluation of cataracts - p. Page 65 19. Chapter 7 : Tryptophan, after inflammatory cytokine stimulation, determines plaque vulnerability and risk of myocardial infarction - p. Page 79 20. Chapter 8 : Tryptophan and metabolites (serotonin and kynurenines) in posttraumatic stress disorder - p. Page 88 21. Chapter 9 : Effects of tryptophan metabolism on the brain: From early development to Alzheimer's disease - p. Page 101 22. Chapter 10 : Excess activity of 3-hydroxykynurenine, quinolinic acid, and other toxic tryptophan metabolites in neurogenerative diseases and other protein misfolding diseases - p. Page 111 23. Chapter 11 : Tryptophan and kynurenine levels in patients with obstructive sleep apnea syndrome - p. Page 126 24. Chapter 12 : Fluorescence-based techniques using plasma: A unique biomarker for different cancers - p. Page 133 25. Chapter 13 : Synchronous luminescence spectroscopy of tryptophan in head and neck cancer - p. Page 143 26. Chapter 14 : Tryptophan fluorescence for diagnosis and staging of gastrointestinal cancers - p. Page 154 27. Chapter 15 : Tryptophan fluorescence and machine learning to study the aggressiveness of prostate cancer cell lines: A pilot study - p. Page 167 28. Chapter 16 : The principles of machine learning algorithms: Applications to biophotonics and disease - p. Page 179 29. Index - p. Page 196
