CaV3.2 calcium channels contribute to trigeminal neuralgia
Bibliographic record
Abstract
ABSTRACT: Trigeminal neuralgia (TN) is a rare but debilitating disorder characterized by excruciating facial pain, with a higher incidence in women. Recent studies demonstrated that TN patients present mutations in the gene encoding the Ca V 3.2 T-type calcium channel, an important player in peripheral pain pathways. We characterize the role of Ca V 3.2 channels in TN at 2 levels. First, we examined the biophysical properties of CACNA1H variants found in TN patients. Second, we investigated the role of Ca V 3.2 in an animal model of trigeminal neuropathic pain. Whole-cell patch-clamp recordings from 4 different mutants expressed in tsA-201 cells (E286K in the pore loop of domain I, H526Y, G563R, and P566T in the domain I-II linker) identified a loss of function in activation in the E286K mutation and gain of function in the G563R and P566T mutations. Moreover, a loss of function in inactivation was observed with the E286K and H526Y mutations. Cell surface biotinylation revealed no difference in channel trafficking among the variants. The G563R mutant also caused a gain of function in the firing properties of transfected trigeminal ganglion neurons. In female and male mice, constriction of the infraorbital nerve induced facial thermal heat hyperalgesia. Block of T-type channels with Z944 resulted in antihyperalgesia. The effect of Z944 was absent in Ca V 3.2 -/- mice, indicating that Ca V 3.2 is the molecular target of the antihyperalgesic Z944 effect. Finally, enzyme-linked immunosorbent assay analysis revealed increased Ca V 3.2 channel expression in the spinal trigeminal subnucleus caudalis. Altogether, the present study demonstrates an important role of Ca V 3.2 channels in trigeminal pain.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".