A M1 muscarinic acetylcholine receptor-specific positive allosteric modulator VU0486846 reduces neurogliosis in female Alzheimer’s mice
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most prevalent type of dementia, disproportionately affecting females, who make up nearly 60% of diagnosed cases. In AD patients, the accumulation of beta-amyloid (Aβ) in the brain triggers a neuroinflammatory response driven by neuroglia, worsening the condition. We have previously demonstrated that VU0486846, an orally available positive allosteric modulator (PAM) targeting M1 muscarinic acetylcholine receptors, enhances cognitive function and reduces Aβ pathology in female APPswe/PSEN1ΔE9 (APP/PS1) mice. However, it remained unclear whether these improvements were linked to a decrease in neuroglial activation. To investigate, we treated nine-month-old APP/PS1 and wildtype mice with VU0486846 for 8 weeks and analyzed brain slices for markers of microglial activation (ionized calcium binding adaptor molecule 1, Iba1) and astrocyte activation (Glial fibrillary acidic protein, GFAP). We find that VU0486846 reduces the presence of Iba1-positive microglia and GFAP-positive astrocytes in the hippocampus of female APP/PS1 mice and limits the recruitment of these cells to remaining Aβ plaques. This study sheds light on an additional mechanism through which novel M1 mAChR PAMs exhibit disease-modifying effects by reducing neuroglial activation and underscore the potential of these ligands for the treatment of AD, especially in females.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".