Targeting firing rate neuronal homeostasis can prevent seizures
Bibliographic record
Abstract
Manipulating firing-rate neuronal homeostasis, which enables neurons to regulate their intrinsic excitability, offers an attractive opportunity to prevent seizures. However, to date, no drug-based interventions have been reported that manipulate this type of neuronal homeostatic mechanism. Here, we used a combination of Drosophila and mouse, and, in the latter, both a pentylenetetrazole (PTZ)-induced seizure model and an electrically induced seizure model for refractory seizures to evaluate the anticonvulsant efficacy of a novel class of anticonvulsant compounds, based on 4-tert-butyl-benzaldehyde (4-TBB). The mode of action included increased expression of the firing rate homeostatic regulator Pumilio (PUM). Knockdown of pum expression, in Drosophila, blocked anticonvulsive effects of 4-TBB, while analysis of validated PUM targets in mouse brain revealed significant reductions following exposure to this compound. A structure-activity study identified the active parts of the molecule and, further, showed that the pyrazole analogue demonstrates highest efficacy, being active against both PTZ-induced and electrically induced seizures. This study provides a proof of principle that anticonvulsant effects can be achieved through regulation of firing rate neuronal homeostasis and identifies a possible chemical compound for future development.
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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 | 0.001 |
| 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".