Turning up the heat on L-type Ca<sup>2+</sup>channels promotes neuronal firing and seizure activity
Bibliographic record
Abstract
It is well recognized clinically that fever in young children (< 6 y of age) may lead to seizure activity in a small, but significant percentage of these individuals, which may have negative consequences for the developing brain and progressive cognitive function. In rodent models, exposure of acute brain slices to hyperthermic temperatures (i.e., 38-41 °C) is reported to evoke membrane depolarization and increased neuronal firing, although the underlying molecular/cellular events responsible for these phenomena are not fully understood. Elevated temperature may alter membrane excitability by influencing individual ion channels within a given neuron, or alter the behavior and connectivity of neurons and glia that operate within a local network. In the present study, Radzicki and colleagues have examined the possibility that modest increases in tissue/body temperature (up to 40.5 °C) may enhance the activity of voltage-gated Ca(2+) channels, which could then promote spontaneous firing of individual neurons and greater network discharge. The results of this work indicate that fever-like temperatures positively and reversibly influence the gating properties of L-type Ca(2+) channels, and that the L-type blocker nimodipine reduces both temperature-induced increases in spontaneous neuronal firing and the incidence/duration of discharge activity in a whole animal model of febrile seizure.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".