Antagonist of the Amylin Receptor Blocks β-Amyloid Toxicity in Rat Cholinergic Basal Forebrain Neurons
Bibliographic record
Abstract
Salvage of cholinergic neurons in the brain through a blockade of the neurotoxic effects of amyloidbeta protein (Abeta) is one of the major, but still elusive, therapeutic goals of current research in Alzheimer's disease (AD). To date, no receptor has been unequivocally identified for Abeta. Human amylin, which acts via a receptor composed of the calcitonin receptor-like receptor and a receptor-associated membrane protein, possesses amyloidogenic properties and has a profile of neurotoxicity that is strikingly similar to Abeta. In this study, using primary cultures of rat cholinergic basal forebrain neurons, we show that acetyl-[Asn30, Tyr32] sCT(8-37) (AC187), an amylin receptor antagonist, blocks Abeta-induced neurotoxicity. Treatment of cultures with AC187 before exposure to Abeta results in significantly improved neuronal survival as judged by MTT and live-dead cell assays. Quantitative measures of Abeta-evoked apoptotic cell death, using Hoechst and phosphotidylserine staining, confirm neuroprotective effects of AC187. We also demonstrate that AC187 attenuates the activation of initiator and effector caspases that mediate Abeta-induced apoptotic cell death. These data are the first to show that expression of Abeta toxicity may occur through the amylin receptor and suggest a novel therapeutic target for the treatment of 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".