Toward the Treatment of Inherited Diseases of the Retina Using CRISPR-Based Gene Editing
Bibliographic record
Abstract
Inherited retinal dystrophies [IRDs] are a common cause of severe vision loss resulting from pathogenic genetic variants. The eye is an attractive target organ for testing clinical translational approaches in inherited diseases. This has been demonstrated by the approval of the first gene supplementation therapy to treat an autosomal recessive IRD, RPE65-linked Leber congenital amaurosis (type 2), 4 years ago. However, not all diseases are amenable for treatment using gene supplementation therapy, highlighting the need for alternative strategies to overcome the limitations of this supplementation therapeutic modality. Gene editing has become of increasing interest with the discovery of the CRISPR-Cas9 platform. CRISPR-Cas9 offers several advantages over previous gene editing technologies as it facilitates targeted gene editing in an efficient, specific, and modifiable manner. Progress with CRISPR-Cas9 research now means that gene editing is a feasible strategy for the treatment of IRDs. This review will focus on the background of CRISPR-Cas9 and will stress the differences between gene editing using CRISPR-Cas9 and traditional gene supplementation therapy. Additionally, we will review research that has led to the first CRISPR-Cas9 trial for the treatment of CEP290-linked Leber congenital amaurosis (type 10), as well as outline future directions for CRISPR-Cas9 technology in the treatment of IRDs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".