The Neuropeptide Pituitary Adenylate Cyclase‐Activating Polypeptide Exerts Anti‐Apoptotic and Differentiating Effects during Neurogenesis: Focus on Cerebellar Granule Neurones and Embryonic Stem Cells
Bibliographic record
Abstract
Pituitary adenylate cyclase-activating polypeptide (PACAP) was originally isolated from ovine hypothalamus on the basis of its hypophysiotrophic activity. It has subsequently been shown that PACAP and its receptors are widely distributed in the central nervous system of adult mammals, indicating that PACAP may act as a neurotransmitter and/or neuromodulator. It has also been found that PACAP and its receptors are expressed in germinative neuroepithelia, suggesting that PACAP could be involved in neurogenesis. There is now compelling evidence that PACAP exerts neurotrophic activities in the developing cerebellum and in embryonic stem (ES) cells. In particular, the presence of PACAP receptors has been demonstrated in the granule layer of the immature cerebellar cortex, and PACAP has been shown to promote survival, inhibit migration and activate neurite outgrowth of granule cell precursors. In cerebellar neuroblasts, PACAP is a potent inhibitor of the mitochondrial apoptotic pathway through activation of the MAPkinase extracellular regulated kinase. ES cells and embryoid bodies (EB) also express PACAP receptors and PACAP facilitates neuronal orientation and induces the appearance of an electrophysiological activity. Taken together, the anti-apoptotic and pro-differentiating effects of PACAP characterised in cerebellar neuroblasts as well as ES and EB cells indicate that PACAP acts not only as a neurohormone and a neurotransmitter, but also as a growth factor.
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 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".