<i>Xenopus laevis</i>Tadpoles Can Regenerate Neural Retina Lost after Physical Excision but Cannot Regenerate Photoreceptors Lost through Targeted Ablation
Bibliographic record
Abstract
PURPOSE: To determine whether the Xenopus laevis retina is capable of regenerating photoreceptor cells lost through apoptotic cell death in an inducible transgenic X. laevis model of retinitis pigmentosa (RP). METHODS: Acute rod photoreceptor apoptosis was induced in transgenic X. laevis expressing drug-inducible caspase 9. We subsequently monitored the ability of the retina to regenerate lost photoreceptors in the absence of drug, and in combination with physical injury or ectopic supplementation of basic fibroblast growth factor (FGF2). RESULTS: Direct activation of caspase 9 in rod photoreceptors resulted in the initiation of apoptosis and complete removal of rod photoreceptors within 4 days. Photoreceptors lost by apoptosis were not replaced over a 4-week recovery time frame. In contrast, physical disruption of rod-ablated retina was repaired by the end of a 3-week time frame, but did not result in rod photoreceptor regeneration other than at the site of injury. Furthermore, ectopic supplementation of FGF2 did not stimulate regeneration of photoreceptors lost by apoptosis. However, FGF2 supplementation increased the rate of regeneration of retina (including rod photoreceptors) in eyes from which retinal tissue was surgically removed. CONCLUSIONS: In the X. laevis retina, rod photoreceptors that undergo drug-induced caspase-9-mediated apoptosis are permanently lost and do not regenerate. In contrast, the neural retina (including rod photoreceptors) can regenerate in injured or retinectomized eyes, and this regeneration is promoted by supplementation with FGF2. However, FGF2 does not promote regeneration of rod photoreceptors that are selectively lost by apoptosis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".