Ca<sub>V</sub>3.2 drives sustained burst‐firing, which is critical for absence seizure propagation in reticular thalamic neurons
Bibliographic record
Abstract
Summary Objective Genetic alterations have been identified in the CACNA1H gene, encoding the CaV3.2 T‐type calcium channel in patients with absence epilepsy, yet the precise mechanisms relating to seizure propagation and spike‐wave‐discharge (SWD) pacemaking remain unknown. Neurons of the thalamic reticular nucleus (TRN) express high levels of CaV3.2 calcium channels, and we investigated whether a gain‐of‐function mutation in the Cacna1h gene in Genetic Absence Epilepsy Rats from Strasbourg (GAERS) contributes to seizure propagation and pacemaking in the TRN. Methods Pathophysiological contributions of CaV3.2 calcium channels to burst firing and absence seizures were assessed in vitro using acute brain slice electrophysiology and quantitative real‐time polymerase chain reaction (PCR) and in vivo using free‐moving electrocorticography recordings. Results TRN neurons from GAERS display sustained oscillatory burst‐firing that is both age‐ and frequency‐dependent, occurring only in the frequencies overlapping with GAERS SWDs and correlating with the expression of a CaV3.2 mutation‐sensitive splice variant. In vivo knock‐down of CaV3.2 using direct thalamic injection of lipid nanoparticles containing CaV3.2 dicer small interfering (Dsi) RNA normalized TRN burst‐firing, and in free‐moving GAERS significantly shortened seizures. Significance This supports a role for TRN CaV3.2 T‐type channels in propagating thalamocortical network seizures and setting the pacemaking frequency of SWDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".