Neuropathological changes and cognitive deficits in rats transgenic for human mutant tau recapitulate human tauopathy
Bibliographic record
Abstract
The assembly of tau protein into abnormal filaments and brain cell degeneration are characteristic of a number of human neurodegenerative diseases, including Alzheimer's disease and frontotemporal dementia and parkinsonism linked to chromosome 17. Several murine models have been generated to better understand the mechanisms contributing to tau assembly and neurodegeneration. Taking advantage of the more elaborate central nervous system and higher cognitive abilities of the rat, we generated a model expressing the longest human tau isoform (2N4R) with the P301S mutation. This transgenic rat line, R962-hTau, exhibits the main features of human tauopathies, such as: age-dependent increase in inclusions comprised of aggregated-tau, neuronal loss, global neurodegeneration as reflected by brain atrophy and ventricular dilation, alterations in astrocytic and microglial morphology, and myelin loss. In addition, substantial deficits across multiple memory and learning paradigms, including novel object recognition, fear conditioning and Morris water maze tasks, were observed at the time of advanced tauopathy. These results support the concept that progressive tauopathy correlates with brain atrophy and cognitive impairment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".