Protective Effects of Pituitary Adenylate Cyclase-Activating Polypeptide (PACAP) Against Apoptosis
Bibliographic record
Abstract
Apoptosis is a regulated process leading to cell death, which is implicated both in normal development and in various pathologies including heart failure, stroke and neurodegenerative diseases. Caspase-3, a key enzyme of the apoptotic pathway, is considered as a major target for the treatment of abnormal cell death. Many factors that inhibit cell death have been identified, but the mechanisms involved are not always fully understood. Pituitary adenylate cylase-activating polypeptide (PACAP) has been shown to exert neuroprotective activities during development. PACAP also inhibits apoptosis in cardiomyopathy, decreases glutamate-induced retinal injury, reduces neuronal loss in case of stroke, and prevents ethanol neurotoxicity. Most of the antiapoptotic effects of PACAP are mediated through the PAC1 receptor. This receptor activates a transduction cascade of second messengers to stimulate Bcl-2 expression which inhibits cytochrome c release and blocks in turn caspase activation. PACAP also acts through the PI3K/Akt pathway and inhibits the expression of proapoptotic factors such as c-Jun or Bax. The remarkable effect of PACAP on the apoptotic cascade suggests that innovative PACAP derivatives could potentially be useful for treatment of post-traumatic lesions, chronic neurodegenerative diseases, cardiac ischemia and/or retinopathy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".