A positive allosteric modulator of M1 Acetylcholine receptors improves pathology and cognitive deficits in female APPswe/PSEN1ΔE9 mice
Bibliographic record
Abstract
Alzheimer's disease (AD) is an age-related neurodegenerative disorder characterized by progressive cognitive decline with no effective treatments to slow progression. Beta-amyloid (Aβ) protein is considered the principal neurotoxic species in AD brains. The m1 Acetylcholine receptor (m1 mAChR) plays a key role in memory and learning. m1 mAChR agonists shows pro-cognitive activity but cause many off target adverse effects including seizures. A new m1 mAChR positive allosteric modulator (PAM), VU0486846, is devoid of direct agonist activity and adverse effects but was not tested for efficacy in AD mice. Since women account for more than 60% of cases and most AD research is conducted in male models, we tested the efficacy of VU0486846 in female AD mice first. Here, we treated 9-month-old female APPswe/PSEN1ΔE9 (APPswe) and wild-types with VU0486846 in drinking water (10mg/kg/day) for 4 or 8 weeks. Cognitive function of all mice was assessed after treatment and brains were harvested for biochemical and immunohistochemical assessment. Both 4 and 8 weeks of treatment with 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 hippocampal Aβ oligomers and plaques. VU0486846 did not change the expression of amyloid precursor protein in APPswe but the reduction in Aβ load in VU0486846-treated mice was due to a shift in the processing of amyloid precursor protein from β-cleavage to non-amyloidogenic cleavage. Specifically, VU0486846 reduced expression of β-secretase 1 (BACE1) whereas enhanced expression of the α-secretase ADAM10 in APPswe hippocampus. Thus, using m1 AChR PAMs can be a viable disease-modifying approach that should be exploited clinically to slow AD.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".