Alleviation of migraine related pain and anxiety by inhibiting calcium-stimulating AC1-dependent CGRP in the insula of adult rats
Bibliographic record
Abstract
BACKGROUND: Recent animal and clinical findings consistently highlight the critical role of calcitonin gene-related peptide (CGRP) in chronic migraine (CM) and related emotional responses. CGRP antibodies and receptor antagonists have been approved for CM treatment. However, the underlying CGRP-related signaling pathways in the pain-related cortex remain poorly understood. METHODS: The SD rats were used to establish the CM model by dural infusions of inflammatory soup. Periorbital mechanical thresholds were assessed using von-Frey filaments, and anxiety-like behaviors were observed via open field and elevated plus maze tests. Expression of c-Fos, CGRP and NMDA GluN2B receptors was detected using immunofluorescence and western blotting analyses. The excitatory synaptic transmission was detected by whole-cell patch-clamp recording. A human-used adenylate cyclase 1 (AC1) inhibitor, hNB001, was applied via insula stereotaxic and intraperitoneal injections in CM rats. RESULTS: The insular cortex (IC) was activated in the migraine model rats. Glutamate-mediated excitatory transmission and NMDA GluN2B receptors in the IC were potentiated. CGRP levels in the IC significantly increased during nociceptive and anxiety-like activities. Locally applied hNB001 in the IC or intraperitoneally alleviated periorbital mechanical thresholds and anxiety behaviors in migraine rats. Furthermore, CGRP expression in the IC decreased after the hNB001 application. CONCLUSIONS: Our study indicated that AC1-dependent IC plasticity contributes to migraine and AC1 may be a promising target for treating migraine in the future.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".