A Critical Review of Zebrafish Models of Parkinson’s Disease
Bibliographic record
Abstract
A wide variety of human diseases have been modelled in zebrafish, including various types of cancer, cardiovascular diseases and neurodegenerative diseases like Alzheimer’s and Parkinson’s. Recent reviews have summarized the currently available zebrafish models of Parkinson’s Disease, which include gene-based, chemically induced and chemogenetic ablation models. The present review updates the literature, critically evaluates each of the available models of Parkinson’s Disease in zebrafish and compares them with similar models in invertebrates and mammals to determine their advantages and disadvantages. We examine gene-based models, including ones linked to Early-Onset Parkinson’s Disease: PARKIN, PINK1, DJ-1, and SNCA; but we also examine LRRK2, which is linked to Late-Onset Parkinson’s Disease. We evaluate chemically induced models like MPTP, 6-OHDA, rotenone and paraquat, as well as chemogenetic ablation models like metronidazole-nitroreductase. The article also reviews the unique advantages of zebrafish, including the abundance of behavioural assays available to researchers and the efficiency of high-throughput screens. This offers a rare opportunity for assessing the potential therapeutic efficacy of pharmacological interventions. Zebrafish also are very amenable to genetic manipulation using a wide variety of techniques, which can be combined with an array of advanced microscopic imaging methods to enable in vivo visualization of cells and tissue. Taken together, these factors place zebrafish on the forefront of research as a versatile model for investigating disease states. The end goal of this review is to determine the benefits of using zebrafish in comparison to utilising other animals and to consider the limitations of zebrafish for investigating human disease.
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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 | 0.001 |
| 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) | 0.007 | 0.003 |
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".