A Model of Excitotoxic Brain Injury in Larval Zebrafish: Potential Application for High-Throughput Drug Evaluation to Treat Traumatic Brain Injury
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a leading cause of death and morbidity with no effective therapeutic treatments for secondary injury. Preclinical drug evaluation in rodent models of TBI is a lengthy process. In this regard, the zebrafish has numerous advantages to address the technical and time-dependent obstacles associated with drug evaluation. We developed a reproducible brain injury using glutamate excitoxicity in zebrafish larvae, a known initiator of delayed cell death in TBI. Glutamate challenge resulted in dose-dependent lethality over an 84-h observation period. We report significant decrease in locomotion (p < 0.0001) and mean velocity (p < 0.001) with 10 μM glutamate application as measured through automated 96-well plate behavioral analysis. Application of the NMDA receptor antagonist MK-801 (400 nM) or the calpain inhibitor, MDL-28170 (20 μM), resulted in significant recovery of locomotor function. A secA5-YFP transgenic line was used to visualize the localization of cell death due to glutamate exposure in vivo using confocal fluorescence microscopy. Our results indicate that zebrafish larvae exhibit responses to excitotoxic injury and pharmacotherapeutic intervention with pathophysiological relevance to mammalian excitotoxic brain injury. This system has potential to be applied as a high-throughput drug screening model to quickly identify candidate lead compounds for further evaluation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".