Challenges in Neuroprotective Nanomedicine Development: Progress Towards Noninvasive Gene Therapy of Glaucoma
Bibliographic record
Abstract
Over the past decade the application of gene therapy of retinal diseases such as glaucoma has produced promising results. However, optic nerve regeneration and restoration of vision in patients with glaucoma is still far from reality. Neuroprotective approaches in the form of gene therapy may provide significant advantages, but are still limited by many factors both at the organ and cellular levels. In general, gene delivery systems for eye diseases range from simple eye drops and ointments to more advanced bio- and nanotechnology-based systems such as muco-adhesive systems, polymers, liposomes and ocular inserts. Most of these technologies were developed for front-of-the-eye ophthalmic therapies and are not applicable as back-of-the-eye delivery systems. Currently, only the invasive intravitreal injections are capable of successfully delivering genes to the retina. Here we review the challenges and possible strategies for the noninvasive gene therapy of glaucoma including the barriers in the eye and in neural cells, and present a cross-sectional view of gene delivery as it pertains to the prevention and treatment of glaucoma.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".