A positive allosteric modulator for the muscarinic receptor (M1 mAChR) improves pathology and cognitive deficits in female <scp>APPswe</scp>/PSEN1ΔE9 mice
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and women account for 60% of diagnosed cases. β-Amyloid (Aβ) oligomers are considered the principal neurotoxic species in AD brains. The M1 muscarinic ACh receptor (M1 mAChR) plays a key role in memory and learning. M1 mAChR agonists show pro-cognitive activity but cause many adverse off-target effects. A new orally bioavailable M1 mAChR positive allosteric modulator (PAM), VU0486846, is devoid of direct agonist activity or adverse effects but was not tested for disease-modifying efficacy in female AD mice. EXPERIMENTAL APPROACH: ) for 4 or 8 weeks. Cognitive function of mice was assessed after treatment, and brains were harvested for biochemical and immunohistochemical assessment. KEY RESULTS: VU0486846 improved cognitive function of APPswe mice when tested in novel object recognition and Morris water maze. This was paralleled by a significant reduction in Aβ oligomers and plaques and neuronal loss in the hippocampus. VU0486846 reduced Aβ oligomer production in APPswe mice by increasing M1 mAChR expression and shifting the processing of amyloid precursor protein from amyloidogenic cleavage to non-amyloidogenic cleavage. Specifically, VU0486846 reduced the expression of β-secretase 1 (BACE1), whereas it enhanced the expression of the α-secretase ADAM10 in APPswe hippocampus. CONCLUSION AND IMPLICATIONS: Using M1 mAChR PAMs can be a viable disease-modifying approach that should be exploited clinically to slow AD in women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".