Alzheimer's Disease Research Guide

Alzheimer's Disease Research Guide
Author :
Publisher : Elsevier
Total Pages : 284
Release :
ISBN-10 : 9780443289804
ISBN-13 : 0443289808
Rating : 4/5 (808 Downloads)

Book Synopsis Alzheimer's Disease Research Guide by : Takaomi C. Saido

Download or read book Alzheimer's Disease Research Guide written by Takaomi C. Saido and published by Elsevier. This book was released on 2024-07-25 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Alzheimer's Disease Research Guide: Animal Models for Understanding Mechanisms and Medications provides researchers with a comprehensive guide, detailing every aspect of Alzheimer's Disease research, including chapters on neuroinflammation, immunotherapy, biomarkers, and animal modeling. This book begins with historical perspectives of both pathological chronology and pathological biochemistry in relation to Alzheimer's disease. Other chapters review Amyloidogenic AB and Non-Amyloidogenic tau and Metabolism of AB major components to the research and understanding of Alzheimer's research. The book concludes with specific treatment chapters including how to develop safe, effective, and inexpensive medications and the application of genome editing to the treatment of Familial Alzheimer's Disease.Written by world renowned expert in Alzheimer's research, this book is a valuable resource for all researchers. - Reviews why familial Alzheimer's disease is vital to understanding sporadic Alzheimer's disease - Describes the latest "game changer" animal models of Alzheimer's disease and frontotemporal dementia in detail - Explains how various Alzheimer's disease medications have failed clinical trials - Discusses pros and cons of therapeutic antibodies, lecanemab and donanemab, that were recently found to be effective in recent clinical trials - Details the application of genome editing as a treatment for familial Alzheimer's disease - Proposes publishing "Journal of Negative Data" for the days of generative AI-assisted publication, AI being unable to distinguish between reproducible and unreproducible data, particularly important in Alzheimer's research


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