Post-Traumatic Epilepsy in Zebrafish Is Drug-Resistant and Impairs Cognitive Function
Bibliographic record
Abstract
Post-traumatic epilepsy (PTE) is acquired epilepsy after traumatic brain injury (TBI). Despite the availability of more than 20 antiseizure medications (ASMs), there is no way at present to prevent epileptogenesis in TBI survivors, and many cases of PTE become drug-resistant. Importantly, the adverse effects of ASMs can significantly affect patients' quality of life. Mammalian models are commonly used for studying refractory PTE, but are expensive and laborious. Zebrafish models have become popular for studying epilepsy, but most focus on larvae, and there have been no reports to date of pharmacological screening in an adult zebrafish model of acquired epilepsy. Valid animal models are critical for understanding PTE and for developing novel therapeutics. The aim of the present study was to characterize the cognitive impairments of a zebrafish model of TBI that leads to the development of PTE. Using combined behavioral and electrophysiological approaches, we also characterized the pharmacological effects of the most commonly used ASMs to manage PTE (valproate, carbamazepine, and phenytoin). Zebrafish with PTE exhibited impairments in learning and memory, difficulty in decision making, and reduced social preference. Valproate and carbamazepine had a limited protective effect against behavioral seizures, and all three drugs failed to significantly reduce electrographical seizures. The negative impacts of TBI and ASMs in zebrafish parallel those observed in other animals, making the zebrafish model of PTE a promising high-throughput model of refractory and drug-resistant epilepsy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".