Dural afferents express acid-sensing ion channels: A role for decreased meningeal pH in migraine headache
Bibliographic record
Abstract
Migraine headache is one of the most common neurological disorders. The pathological conditions that directly initiate afferent pain signaling are poorly understood. In trigeminal neurons retrogradely labeled from the cranial meninges, we have recorded pH-evoked currents using whole-cell patch-clamp electrophysiology. Approximately 80% of dural-afferent neurons responded to a pH 6.0 application with a rapidly activating and rapidly desensitizing ASIC-like current that often exceeded 20nA in amplitude. Inward currents were observed in response to a wide range of pH values and 30% of the neurons exhibited inward currents at pH 7.1. These currents led to action potentials in 53%, 30% and 7% of the dural afferents at pH 6.8, 6.9 and 7.0, respectively. Small decreases in extracellular pH were also able to generate sustained window currents and sustained membrane depolarizations. Amiloride, a non-specific blocker of ASIC channels, inhibited the peak currents evoked upon application of decreased pH while no inhibition was observed upon application of TRPV1 antagonists. The desensitization time constant of pH 6.0-evoked currents in the majority of dural afferents was less than 500ms which is consistent with that reported for ASIC3 homomeric or heteromeric channels. Finally, application of pH 5.0 synthetic-interstitial fluid to the dura produced significant decreases in facial and hind-paw withdrawal threshold, an effect blocked by amiloride but not TRPV1 antagonists, suggesting that ASIC activation produces migraine-related behavior in vivo. These data provide a cellular mechanism by which decreased pH in the meninges following ischemic or inflammatory events directly excites afferent pain-sensing neurons potentially contributing to migraine headache.
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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".