Mouse models of Alzheimer's disease: The long and filamentous road
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterized by memory impairment leading to dementia, deposition of amyloid plaques and neurofibrillary tangles (NFTs), and neuronal loss. The major component of plaques is the amyloid beta peptide, A beta, whereas NFTs contain hyperphosphorylated forms of the microtubule-associated protein tau (tau). Familial AD (FAD) mutations either elevate A beta synthesis by favoring 'secretase' of the Alzheimer beta-amyloid precursor protein (APP) or enhance the fibrillogenic properties of this peptide. Mutations in the tau gene cause a different disease denoted FTPD-17, but suggest that the aberrant forms of tau seen in AD are unlikely to be benign. These findings imply a complex pathogenic cascade in AD and important goals of transgenic modeling are to capture and stratify this pathogenic process. Several laboratories have created APP transgenic (Tg) mice that exhibit AD-like amyloid pathology and A beta burdens. These Tg lines also exhibit deficits in spatial reference and/or working memory, with immunization against A beta attenuating both AD-associated phenotypes. Tangle-like pathologies are observed in mice expressing FTPD-17 mutant forms of tau, but florid tau pathologies based upon the wild type (wt) tau isoforms present in AD have proven more elusive. Creation of animal models with robust amyloid and tau pathologies, yet free of irrelevant confounding pathologies, remains a major objective in this field.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".