Comprehensive Analysis of Neurotoxin-Induced Ablation of Dopaminergic Neurons in Zebrafish Larvae
Bibliographic record
Abstract
Neurotoxin exposure of zebrafish larvae has been used to mimic a Parkinson’s disease (PD) phenotype and to facilitate high-throughput drug screening. However, the vulnerability of zebrafish to various neurotoxins was shown to be variable. Here, we provide a direct comparison of ablative effectiveness in order to identify the optimal neurotoxin-mediated dopaminergic (DAnergic) neuronal death in larval zebrafish. Transgenic zebrafish, Tg(dat:eGFP), were exposed to different concentrations of the neurotoxins MPTP, MPP+, paraquat, 6-OHDA, and rotenone for four days, starting at three days post-fertilization. The LC50 of each respective neurotoxin concentration was determined. Confocal live imaging on Tg(dat:eGFP) showed that MPTP, MPP+, and rotenone caused comparable DAnergic cell loss in the ventral diencephalon (vDC) region while, paraquat and 6-OHDA caused fewer losses of DAnergic cells. These results were further supported by respective gene expression analyses of dat, th, and p53. Importantly, the loss of DAnergic cells from exposure to MPTP, MPP+, and rotenone impacted larval locomotor function. MPTP induced the largest motor deficit, but this was accompanied by the most severe morphological impairment. We conclude that, of the tested neurotoxins, MPP+ recapitulates a substantial degree of DAnergic ablation and slight locomotor perturbations without systemic defects indicative of a Parkinsonian phenotype.
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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 | 0.000 |
| 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".