Longitudinal fluorescent observation of retinal degeneration and regeneration in zebrafish using fundus lens imaging.
Bibliographic record
Abstract
PURPOSE: Longitudinal observation of retinal degeneration and regeneration in animal models is time-consuming and expensive. To address this challenge, we used a custom fundus lens and zebrafish transgenic lines with cell-specific fluorescent reporters to document the state of individual retinal neurons in vivo. METHODS: We empirically tested several versions of a custom fundus lens and assessed its capabilities under a stereomicroscope to image retinal neurons in transgenic zebrafish lines expressing fluorescent reporters. Vascular branch points provided spatial references enabling determination of whether changes induced by ablating photoreceptors were repaired over the course of several days. RESULTS: Individual ultraviolet- and blue-sensitive cone photoreceptors were readily visualized in vivo, and green fluorescent protein-labeled blood vessels were used as landmarks to facilitate orientation. Sequential imaging of the same retinal areas over several weeks permitted documentation of photoreceptor reappearance in individual animals. Photoreceptor regeneration in these regions was evidenced by the reappearance of individual fluorescent cells. CONCLUSIONS: This technique permits real-time in vivo serial examination of individual fish, permitting temporal analysis of changes to the retinal mosaic. The key benefits this technique offers include that the same retinal locations can be recovered and viewed at multiple time points, that in vivo observations are comparable to those made ex vivo, and that fewer animals need to be euthanized over the course of an experiment. Our results promise the ability to detect individual cells, including reappearing cone photoreceptors, and to monitor disease progression during screening of therapies in an adult animal model of late onset 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".