Zebrafish and inherited photoreceptor disease: Models and insights
Bibliographic record
Abstract
Photoreceptor dysfunctions and degenerative diseases are significant causes of vision loss in patients, with few effective treatments available. Targeted interventions to prevent or reverse photoreceptor-related vision loss are not possible without a thorough understanding of the underlying mechanism leading to disease, which is exceedingly difficult to accomplish in the human system. Cone diseases are particularly challenging to model, as some popular genetically modifiable model animals are nocturnal with a rod-dominant visual system and cones that have dissimilarities to human cones. As a result, cone diseases, which affect visual acuity, colour perception, and central vision in patients, are generally poorly understood in terms of pathology and mechanism. Zebrafish (Danio rerio) provide the opportunity to model photoreceptor diseases in a diurnal vertebrate with a cone-rich retina which develops many macular degeneration-like pathologies. Zebrafish undergo external development, allowing early-onset retinal diseases to be detected and studied, and many ophthalmic tools are available for zebrafish visual assessment during development and adulthood. There are numerous zebrafish models of photoreceptor disease, spanning the various types of photoreceptor disease (developmental, rod, cone, and mixed photoreceptor diseases) and genetic/molecular cause. In this review, we explore the features of zebrafish that make them uniquely poised to model cone diseases, summarize the established zebrafish models of inherited photoreceptor disease, and discuss how disease in these models compares to the human presentation, where applicable. Further, we highlight the contributions of these zebrafish models to our understanding of photoreceptor biology and disease, and discuss future directions for utilising and investigating these diverse models.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".