Tau, synapse loss and gliosis progress in an Alzheimer’s mouse model after amyloid-β immunotherapy
Bibliographic record
Abstract
Preclinical studies assessing drugs for Alzheimer's disease (AD) are conducted in animal models that usually display only one neuropathological feature of AD, whereas patients present with a complex combination of comorbidities and neuropathologies. Importantly, it is well established that amyloid-β (Aβ) plaque and tau tangle accumulation interact in a phase-dependent manner, making it difficult to predict how targeting one might influence the other, as well as downstream degeneration. We developed a transgenic mouse model, APP/PS1xTau22, with progressive cortical Aβ deposition and hippocampal tau neurofibrillary inclusions, to investigate how both neuropathologies act jointly to bring about neural degeneration, synapse loss and glial phenotypes. We then assessed whether applying murine chimeric aducanumab, an anti-amyloid immunotherapy, could impact the synergistic relationship between amyloid and tau. Drug treatment resulted in a ∼70% reduction in Aβ deposition in hippocampal and cortical areas and produced a robust peri-plaque microglial and astrocytic response. Removing amyloid from the brain did not reverse or slow tau pathology or alter synapse loss. Our findings suggest that, once the interaction between amyloid and tau is set in motion, reducing plaque burden by Aβ immunotherapy may stimulate glial responses, but is insufficient to curb degenerative phenotypes in this model.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".