Electrophysiological and computational analysis of Cav3.2 channel variants associated with familial trigeminal neuralgia
Bibliographic record
Abstract
Abstract Trigeminal neuralgia (TN) is a rare form of chronic neuropathic pain characterized by spontaneous or elicited paroxysms of electric shock-like or stabbing pain in a region of the face. While most cases occur in a sporadic manner and are accompanied by intracranial vascular compression of the trigeminal nerve root, alteration of ion channels has emerged as a potential exacerbating factor. Recently, whole exome sequencing analysis of familial TN patients identified 19 rare variants in the gene CACNA1H encoding for Ca v 3.2T-type calcium channels. An initial analysis of 4 of these variants pointed to a pathogenic role. In this study, we assessed the electrophysiological properties of 13 additional TN-associated Ca v 3.2 variants expressed in tsA-201 cells. Our data indicate that 6 out of the 13 variants analyzed display alteration of their gating properties as evidenced by a hyperpolarizing shift of their voltage dependence of activation and/or inactivation resulting in an enhanced window current supported by Ca v 3.2 channels. An additional variant enhanced the recovery from inactivation. Simulation of neuronal electrical membrane potential using a computational model of reticular thalamic neuron suggests that TN-associated Ca v 3.2 variants could enhance neuronal excitability. Altogether, the present study adds to the notion that ion channel polymorphisms could contribute to the etiology of some cases of TN and further support a role for Ca v 3.2 channels.
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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".