Rapidly dissolving bilayer microneedles enabling minimally invasive and efficient protein delivery to the posterior segment of the eye
Bibliographic record
Abstract
The discovery of proteins that neutralise vascular endothelial growth factors, such as pegaptanib, ranibizumab and aflibercept, can inhibit the process of angiogenesis, thereby restoring eyesight in individuals with retinal vascular disorders. However, due to the posterior location and chronic nature of retinal diseases, a safe and effective intraocular protein delivery system is currently lacking. Thus, dissolving bilayer microneedles (MNs) with the potential to deliver proteins to the back of the eye in an efficient and minimally invasive manner were developed in this study. A model protein, ovalbumin (OVA), was incorporated into MNs fabricated from different polymers, including hyaluronic acid (HA), polyvinyl alcohol (PVA) and polyvinylpyrrolidone (PVP). Optimised PVA/PVP MNs were demonstrated to be robust enough to pierce porcine sclera with > 75% of the needle length penetrating the sclera and dissolving within 150 s. SDS-PAGE and OVA-specific ELISA revealed that the bioactivity of the model protein was maintained during the manufacture of MNs. In hen's egg-chorioallantoic membrane test, MNs fabricated from all chosen polymers were classified as non-irritants. Furthermore, ex vivo permeation studies showed that optimised MNs could permeate 86.99 ± 7.37% of OVA through the sclera, twice that of the needle-free patch (42.16 ± 3.95%), highlighting the capability of MNs to circumvent physical barriers and promote protein delivery to the posterior segment of the eye. In this work, a novel, efficient and safe intraocular protein delivery system was successfully established.
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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) | 0.000 | 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".