Alzheimer disease research in the 21st century: past and current failures, new perspectives and funding priorities
Bibliographic record
Abstract
// Francesca Pistollato 1 , Elan L. Ohayon 2 , Ann Lam 1,2 , Gillian R. Langley 3 , Thomas J. Novak 4 , David Pamies 5 , George Perry 6 , Eugenia Trushina 7 , Robin S.B. Williams 8 , Alex E. Roher 9,10 , Thomas Hartung 5 , Stevan Harnad 11 , Neal Barnard 1 , Martha Clare Morris 12 , Mei-Chun Lai 1 , Ryan Merkley 1 and P. Charukeshi Chandrasekera 1 1 Physicians Committee for Responsible Medicine, Washington, DC, USA 2 Green Neuroscience Laboratory, Neurolinx Research Institute, San Diego, CA, USA 3 Research and Toxicology Department, Humane Society International, London, UK 4 Cellular Dynamics International, Madison, WI, USA 5 CAAT, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA 6 College of Sciences, University of Texas at San Antonio, San Antonio, TX, USA 7 Department of Neurology, Mayo Clinic, Rochester, MN, USA 8 Centre for Biomedical Sciences, School of Biological Sciences, Royal Holloway University of London, Egham, UK 9 Division of Clinical Education, Midwestern University, Glendale, AZ, USA 10 Division of Neurobiology, Barrow Neurological Institute, Phoenix, AZ, USA 11 Department of Psychology, University of Quebec/Montreal, Montreal, Canada 12 Section of Nutrition and Nutritional Epidemiology, Department of Internal Medicine, Rush University, Chicago, IL, USA Correspondence to: Francesca Pistollato, email: // Keywords : Alzheimer disease, animal models, human methods, induced pluripotent stem cells, computational models, Gerotarget Received : September 29, 2015 Accepted : April 18, 2016 Published : May 04, 2016 Abstract Much of Alzheimer disease (AD) research has been traditionally based on the use of animals, which have been extensively applied in an effort to both improve our understanding of the pathophysiological mechanisms of the disease and to test novel therapeutic approaches. However, decades of such research have not effectively translated into substantial therapeutic success for human patients. Here we critically discuss these issues in order to determine how existing human-based methods can be applied to study AD pathology and develop novel therapeutics. These methods, which include patient-derived cells, computational analysis and models, together with large-scale epidemiological studies represent novel and exciting tools to enhance and forward AD research. In particular, these methods are helping advance AD research by contributing multifactorial and multidimensional perspectives, especially considering the crucial role played by lifestyle risk factors in the determination of AD risk. In addition to research techniques, we also consider related pitfalls and flaws in the current research funding system. Conversely, we identify encouraging new trends in research and government policy. In light of these new research directions, we provide recommendations regarding prioritization of research funding. The goal of this document is to stimulate scientific and public discussion on the need to explore new avenues in AD research, considering outcome and ethics as core principles to reliably judge traditional research efforts and eventually undertake new research strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.096 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".