Localization and characterization of pituitary adenylate cyclase‐activating polypeptide receptors in the human cerebellum during development
Bibliographic record
Abstract
Pituitary adenylate cyclase-activating polypeptide (PACAP) receptors are actively expressed in the cortical layers of the cerebellum of rodents and contribute to cerebellar development. The present report provides the first anatomical localization and characterization of PACAP receptors in the developing human cerebellum. RT-PCR analysis from 15-week-old fetuses to 22-year-old subject showed that PAC1-R and VPAC1-R are expressed in the cerebellum at all stages, whereas VPAC2-R mRNA was barely detectable. In situ hybridization labeling indicated that, in human fetuses, PAC1-R mRNA is associated with the external granule cell layer (EGL), a germinative neuroepithelium, and with the internal granule cell layer (IGL). The distribution pattern of VPAC1-R mRNA was very similar to that of PAC1-R mRNA, whereas VPAC2-R mRNA was visualized only in 7-22-year-old subjects. The localization of [(125)I]PACAP27 binding sites was fully consistent with the distribution of PAC1-R and VPAC1-R mRNA. Pharmacological characterization revealed that, in the EGL and IGL from 15-24-week-old fetuses and in the granule cell layer from 7-22-year-old patients, binding sites exhibit a PAC1-R profile. In contrast, PACAP binding sites observed in the molecular layer and medulla of the adult cerebellum consisted of a heterogeneous population of PAC1-R and VPAC(1/2)-R. Altogether, these data provide the first evidence that PACAP receptors are expressed in the human cerebellar cortex. PAC1-R is the predominant PACAP receptor found in fetuses, and both PAC1-R and VPAC1-R are expressed in the mature cerebellum. These observations suggest that PACAP has neurodevelopmental functions in the human cerebellum.
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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 | 0.001 | 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".