Dynamic changes in CGRP, PACAP, and PACAP receptors in the trigeminovascular system of a novel repetitive electrical stimulation rat model: Relevant to migraine
Bibliographic record
Abstract
Migraine is the seventh most disabling disorder globally, with prevalence of 11.7% worldwide. One of the prevailing mechanisms is the activation of the trigeminovascular system, and calcitonin gene-related peptide (CGRP) is an important therapeutic target for migraine in this system. Recent studies suggested an emerging role of pituitary adenylate cyclase-activating peptide (PACAP) in migraine. However, the relation between CGRP and PACAP and the role of PACAP in migraine remain undefined. In this study, we established a novel repetitive (one, three, and seven days) electrical stimulation model by stimulating dura mater in conscious rats. Then, we determined expression patterns in the trigeminal ganglion and the trigeminal nucleus caudalis of the trigeminovascular system. Electrical stimulation decreased facial mechanical thresholds, and the order of sensitivity was as follows: vibrissal pad >inner canthus >outer canthus (P < 0.001). The electrical stimulation group exhibited head-turning and head-flicks (P < 0.05) nociceptive behaviors. Importantly, electrical stimulation increased the expressions of CGRP, PACAP, and the PACAP-preferring type 1 (PAC1) receptor in both trigeminal ganglion and trigeminal nucleus caudalis (P < 0.05). The expressions of two vasoactive intestinal peptide (VIP)-shared type 2 (VPAC1 and VPAC2) receptors were increased in the trigeminal ganglion, whereas in the trigeminal nucleus caudalis, their increases were peaked on Day 3 and then decreased by Day 7. PACAP was colocalized with NEUronal Nuclei (NeuN), PAC1, and CGRP in both trigeminal ganglion and the trigeminal nucleus caudalis. Our results demonstrate that the repetitive electrical stimulation model can simulate the allodynia during the migraine chronification, and PACAP plays a role in the pathogenesis of migraine potentially via PAC1 receptor.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".