Immunotherapy Targeting Pathological Tau Prevents Cognitive Decline in a New Tangle Mouse Model
Bibliographic record
Abstract
Harnessing the immune system to clear protein aggregates is emerging as a promising approach to treat various neurodegenerative diseases. In Alzheimer's disease (AD), several clinical trials are ongoing using active and passive immunotherapy targeting the amyloid-β (Aβ) peptide. Limited emphasis has been put into clearing tau/tangle pathology, another major hallmark of the disease. Recent findings from the first Aβ vaccination trial suggest that this approach has limited effect on tau pathology and that Aβ plaque clearance may not halt or slow the progression of dementia in individuals with mild-to-moderate AD. To assess within a reasonable timeframe whether targeting tau pathology with immunotherapy could prevent cognitive decline, we developed a new model with accelerated tangle development. It was generated by crossing available strains that express all six human tau isoforms and the M146L presenilin mutation. Here, we show that this unique approach completely prevents severe cognitive impairment in three different tests. This remarkable effect correlated well with extensive clearance of abnormal tau within the brain. Overall, our findings indicate that immunotherapy targeting pathological tau is very feasible for tauopathies, and should be assessed in clinical trials in the near future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".