Balanced effect of PACAP and FasL on granule cell death during cerebellar development: a morphological, functional and behavioural characterization
Bibliographic record
Abstract
It is now established that the development of the CNS requires equilibrium between cell survival and apoptosis. Pituitary adenylate cyclase-activating polypeptide (PACAP) exerts a powerful protective effect on cerebellar granule cells by inhibiting the caspase 3. In contrast, Fas ligand (FasL) plays an essential role during ontogenesis in eliminating supernumerary neurons by apoptosis. To determine if PACAP and FasL interact during cerebellar development, we characterized the effects of these factors on cerebellar morphogenesis and caspase 3 activity in PACAP+/+ and PACAP-/- mice. First, we demonstrated in vivo that PACAP is able to reverse the diminution of internal granule cell layer thickness induced by FasL in PACAP+/+ and PACAP-/- mice. Second, ex vivo and immunohistochemical studies revealed that interaction between FasL and PACAP occurs through the caspase 3 activity. Third, behavioural study showed a significant difference for the PACAP + FasL group in the righting reflex test at P8 which does not persist at P60. Finally, a time course study revealed that the pro-apoptotic effect of FasL characterized at P8 was followed by a progressive compensatory mechanism in caspase 3 activity and bromodeoxyuridine incorporation. These data suggest that PACAP and FasL interact during cerebellar development to control apoptosis of granule cells and may affect some motor cerebellar functions.
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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".