Use of Gene Therapy in Retinal Ganglion Cell Neuroprotection: Current Concepts and Future Directions
Bibliographic record
Abstract
We systematically reviewed published translational research on gene-based therapy for retinal ganglion cell (RGC) neuroprotection. A search was conducted on Entrez PubMed on 23 December 2020 using the keywords "gene therapy", "retinal ganglion cell" and "neuroprotection". The initial search yielded 82 relevant articles. After restricting publications to those with full text available and in the English language, and then curating for only original articles on gene-based therapy, the final yield was 18 relevant articles. From the 18 papers, 17 of the papers utilized an adeno-associated viral (AAV) vector for gene therapy encoding specific genes of interest. Specifically, six of the studies utilized an AAV vector encoding brain-derived neurotrophic factor (BDNF), two of the studies utilized an AAV vector encoding erythropoietin (EPO), the remaining 10 papers utilized AAV vectors encoding different genes and one microRNA study. Although the literature shows promising results in both in vivo and in vitro models, there is still a significant way to go before gene-based therapy for RGC neuroprotection can proceed to clinical trials. Namely, the models of injury in many of the studies were more acute in nature, unlike the more progressive and neurodegenerative pathophysiology of diseases, such as glaucoma. The regulation of gene expression is also highly unexplored despite the use of AAV vectors in the majority of the studies reviewed. It is also expected that with the successful launch of messenger ribonucleic acid (mRNA)-based vaccinations in 2020, we will see a shift towards this technology for gene-based therapy in glaucoma neuroprotection.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".