XIAP Protection of Photoreceptors in Animal Models of Retinitis Pigmentosa
Bibliographic record
Abstract
BACKGROUND: Retinitis pigmentosa (RP) is a blinding genetic disorder that is caused by the death of photoreceptors in the outer nuclear layer of the retina. To date, 39 different genetic loci have been associated with the disease, and 28 mutated genes have been identified. Despite the complexity of the underlying genetic basis for RP, the final common pathway is photoreceptor cell death via apoptosis. METHODOLOGY/PRINCIPAL FINDINGS: In this study, P23H and S334ter rhodopsin transgenic rat models of RP were used to test the neuroprotective effects of anti-apoptotic gene therapy. Adeno-associated viruses (AAV) carrying the X-linked inhibitor of apoptosis (XIAP) or green fluorescent protein (GFP) were delivered subretinally into the eye of transgenic rat pups. Histological and functional measures were used to assess neuroprotection. XIAP is known to block apoptosis by inhibiting the action of caspases-3, -7 and -9. The results show that XIAP gene therapy provides long-term neuroprotection of photoreceptors at both structural and functional levels. CONCLUSIONS/SIGNIFICANCE: Our gene therapy strategy targets the apoptotic cascade, which is the final common pathway in all forms of retinitis pigmentosa. This strategy holds great promise for the treatment of RP, as it allows for the broad protection of photoreceptors, regardless of the initial disease causing mutation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".