Modeling Alzheimer's disease with non-transgenic rat models
Bibliographic record
Abstract
Alzheimer's disease (AD), for which there is no cure, is the most common form of dementia in the elderly. Despite tremendous efforts by the scientific community, the AD drug development pipeline remains extremely limited. Animal models of disease are a cornerstone of any drug development program and should be as relevant as possible to the disease, recapitulating the disease phenotype with high fidelity, to meaningfully contribute to the development of a successful therapeutic agent. Over the past two decades, transgenic models of AD based on the known genetic origins of familial AD have significantly contributed to our understanding of the molecular mechanisms involved in the onset and progression of the disease. These models were extensively used in AD drug development. The numerous reported failures of new treatments for AD in clinical trials indicate that the use of genetic models of AD may not represent the complete picture of AD in humans and that other types of animal models relevant to the sporadic form of the disease, which represents 95% of AD cases, should be developed. In this review, we will discuss the evolution of non-transgenic rat models of AD and how these models may open new avenues for drug development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".