Effects of Small Conductance Ca <sup>2+</sup> ‐Activated Potassium Channel Agonists on SLEs in Varied Epilepsy Models, From Animal Slices to Pharmacoresistant Human Tissue
Bibliographic record
Abstract
To assess the impact of SK channel agonists on seizure-like events (SLEs) in various seizure models in slices of the temporal cortex obtained from pharmacoresistant patients. SLEs were triggered by applying 4-aminopyridine (100 μM) to slices of the entorhinal cortex taken from both normal and pilocarpine-treated rats. Additionally, SLEs were induced in slices of the temporal cortex obtained from individuals who had undergone epilepsy surgery. In the case of human slices, SLEs were also provoked by increasing potassium levels along with the administration of either 4-AP (100 μM) or bicuculline (50 μM). The activation of SK2/3 channels by the compound CYPPA (n = 8) effectively prevented SLEs in slices from the brains of normal rats, pilocarpine-treated rats (n = 8), and in human cortex slices (n = 9) when SLEs were triggered by 4-AP. In human temporal cortex slices, CYPPA also demonstrated efficacy in preventing SLEs induced by an elevation in potassium concentration combined with bicuculline application (n = 5). SKA-31, exhibited efficacy in slices from normal rats (n = 8), rats treated with pilocarpine (n = 8), and in human slices (n = 7) when SLEs were provoked by 4-AP. However, its effectiveness was limited when applied to human tissue slices exposed to bicuculline and elevated potassium levels. The SK1 channel activator GW-542573X displayed only moderate anticonvulsant effects in the models under investigation. SK2 channels showed the highest effectiveness across the different epilepsy models. Sensitivity to the SK3 channel activator was found to be more pronounced compared to the other two activators studied.
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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 | 0.000 | 0.001 |
| 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".