Pharmacological Enhancement of ex vivo Gene Therapy Neuroprotection in a Rodent Model of Retinal Degeneration
Bibliographic record
Abstract
AIMS: We have previously shown the benefits of cell-based delivery of neuroprotection in a rodent model of retinitis pigmentosa (RP). In order to maximise the effectiveness of this approach, we hypothesised that this could be augmented by combination with an aminoglycoside known to limit the abnormal RNA translation seen in this model. METHODS: A rhodopsin TgN S334ter-4 rat model of RP underwent daily subcutaneous injection of 12.5 μg/g gentamicin from postnatal day 5 (P5). At P21, selected rats also underwent intravitreal injection of cells genetically engineered to oversecrete glial cell-derived neurotrophic factor. Histological imaging was undertaken to evaluate photoreceptor survival at P70 and compared with images from untreated TgN S334ter-4 rats and control Sprague-Dawley rats. RESULTS: Statistically significant (p < 0.05) improvements in outer retinal indices were seen with this combination strategy when compared with results in rats treated with individual therapies alone. This improvement was most apparent in the peripheral retina, where the greatest degeneration was observed. CONCLUSIONS: We have shown that the combination of neuroprotection plus aminoglycoside read-through in an animal model of retinal degeneration improved the histological appearance of the retina such that it was statistically indistinguishable from unaffected controls. Further functional and longitudinal studies of this approach are warranted.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".