Zebrafish as a robust preclinical platform for screening plant-derived drugs with anticonvulsant properties—a review
Bibliographic record
Abstract
Traditionally, selected plant sources have been explored for medicines to treat convulsions. This continues today, especially in countries with low-income rates and poor medical systems. However, in the low-income countries, plant extracts and isolated drugs are in high demand due to their good safety profiles. Preclinical studies on animal models of seizures/epilepsy have revealed the anticonvulsant and/or antiepileptogenic properties of, at least some, herb preparations or plant metabolites. Still, there is a significant number of plants known in traditional medicine that exert anticonvulsant activity but have not been evaluated on animal models. Zebrafish is recognized as a suitable in vivo model of epilepsy research and is increasingly used as a screening platform. In this review, the results of selected preclinical studies are summarized to provide credible information for the future development of effective screening methods for plant-derived antiseizure/antiepileptic therapeutics using zebrafish models. We compared zebrafish vs. rodent data to show the translational value of the former in epilepsy research. We also surveyed caveats in methodology. Finally, we proposed a pipeline for screening new anticonvulsant plant-derived drugs in zebrafish (“from tank to bedside and back again”).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".