Sustained Subconjunctival Delivery of Infliximab Protects the Cornea and Retina Following Alkali Burn to the Eye
Bibliographic record
Abstract
Purpose: Tumor necrosis factor (TNF)-α is upregulated in eyes following corneal alkali injury and contributes to corneal and also retinal damage. Prompt TNF-α inhibition by systemic infliximab ameliorates retinal damage and improves corneal wound healing. However, systemic administration of TNF-α inhibitors carries risk of significant complications, whereas topical eye-drop delivery is hindered by poor ocular bioavailability and the need for patient adherence. This study investigates the efficacy of subconjunctival delivery of TNF-α antibodies using a polymer-based drug delivery system (DDS). Methods: The drug delivery system was prepared using porous polydimethylsiloxane/polyvinyl alcohol composite fabrication and loaded with 85 μg of infliximab. Six Dutch-belted pigmented rabbits received ocular alkali burn with NaOH. Immediately after the burn, subconjunctival implantation of anti-TNF-α DDS was performed in three rabbits while another three received sham DDS (without antibody). Rabbits were followed with photography for 3 months. Results: After 3 months, the device was found to be well tolerated by the host and the eyes exhibited less corneal damage as compared to eyes implanted with a sham DDS without drug. The low dose treatment suppressed CD45 and TNF-α expression in the burned cornea and inhibited retinal ganglion cell apoptosis and optic nerve degeneration, as compared to the sham DDS treated eyes. Immunolocalization revealed drug penetration in the conjunctiva, cornea, iris, and choroid, with residual infliximab in the DDS 3 months after implantation. Conclusions: This reduced-risk biologic DDS improves corneal wound healing and provides retinal neuroprotection, and may be applicable not only to alkali burns but also to other inflammatory surgical procedures such as penetrating keratoplasty and keratoprosthesis implantation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".