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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".