Differential senolytic inhibition of normal versus Aβ-associated cholinesterases: implications in aging and Alzheimer’s disease
Bibliographic record
Abstract
Cellular senescence is a hallmark of aging and the age-related condition, Alzheimer’s disease (AD). How senescence contributes to cholinergic and neuropathologic changes in AD remains uncertain. Furthermore, little is known about the relationship between senescence and cholinesterases (ChEs). Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) are important in neurotransmission, cell cycle regulation, and AD amyloid-β (Aβ) pathology. Senolytic agents have shown therapeutic promise in AD models. Therefore, we evaluated in vitro and in silico activity of senolytics, dasatinib (1), nintedanib (2), fisetin (3), quercetin (4), GW2580 (5), and nootropic, meclofenoxate hydrochloride (6), toward AChE and BChE. As ChEs associated with AD pathology have altered biochemical properties, we also evaluated agents 1-6 in AD brain tissues. Enzyme kinetics showed agents 1, 3, 4, and 6 inhibited both ChEs, while 2 and 5 inhibited only AChE. Histochemistry showed inhibition of Aβ plaque-associated ChEs (1 and 2: both ChEs; 5: BChE; 6: AChE), but not normal neural-associated ChEs. Modeling studies showed 1-6 interacted with the same five binding locations of both ChEs, some of which may be allosteric sites. These agents may exert their beneficial effects, in part, by inhibiting ChEs associated with AD pathology and provide new avenues for development of next-generation inhibitors targeting pathology-associated ChEs.
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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) | 0.000 | 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) | 0.001 | 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 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".