PAC1 receptor mRNA and protein distribution in rat and human trigeminal and sphenopalatine ganglia, spinal trigeminal nucleus and in dura mater
Bibliographic record
Abstract
BACKGROUND: To further understand the role of pituitary adenylate cyclase-activating polypeptide 1 (PAC1) receptors in headache disorders, we mapped their expression in tissues of the trigemino-autonomic system by immunohistochemistry and in situ hybridization. METHODS: To optimize screening for monoclonal antibodies suitable for immunohistochemistry on formalin-fixed, paraffin-embedded tissues, we developed a new enzyme-linked immunosorbent assay using formalin-fixed, paraffin-embedded cells overexpressing human PAC1 receptors. 169G4.1 was selected from these studies for analysis of rat and human tissues and chimerized onto a mouse backbone to avoid human-on-human cross-reactivity. Immunoreactivity was compared to PAC1 receptor mRNA by in situ hybridization in both species. RESULTS: 169G4.1 immunoreactivity delineated neuronal cell bodies in the sphenopalatine ganglion in both rat and human, whereas no staining was detected in the trigeminal ganglion. The spinal trigeminal nucleus in both species showed immunoreactivity as especially strong in the upper laminae with both cell bodies and neuropil being labelled. No immunoreactivity was seen in either rat or human dura mater vessels. In situ hybridization in both species revealed mRNA in sphenopalatine ganglion neurons and the spinal trigeminal nucleus, a weak signal in the trigeminal nucleus and no signal in dural vessels. CONCLUSION: Taken together, these data support a role for PAC1 receptors in the trigemino-autonomic system as it relates to headache pathophysiology.
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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) | 0.003 | 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".