Antisense Oligonucleotide Therapy in Diabetic Retinopathy
Bibliographic record
Abstract
Diabetic retinopathy is one of the leading causes of blindness in the United States and other parts of the world. Historically, laser photocoagulation and vitrectomy surgery have been used for the treatment of diabetic retinopathy, including diabetic macular edema. Both procedures have proven to be useful under certain conditions but have their limitations. New pathways and processes that promote diabetic retinopathy have been identified, and several new therapeutic approaches are under investigation. These new therapies may be beneficial in the treatment of diabetic retinopathy and include antivascular endothelial growth factor agents, corticosteroids, and therapies that may potentially target a number of additional diabetic retinopathy-related factors and processes, including antisense oligonucleotides. Second-generation antisense oligonucleotides, such as iCo-007, may offer a significant advantage in the treatment of diabetic retinopathy by downregulating the signal pathways of multiple growth factors that seem to play a critical role in the process of ocular angiogenesis and vascular leakage. Benefits of such molecules are expected to include the specificity of the kinase target and an extended half-life, resulting in less frequent intravitreal drug administration, resistance to molecule degradation, and a good safety profile.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".