Cornea‐SELEX for aptamers targeting the surface of eyes and liposomal drug delivery
Bibliographic record
Abstract
Abstract Cornea is the major barrier to drug delivery to the eye, which results in low bioavailability and poor efficacy of topical eye treatment. In this work, we first select cornea‐binding aptamers using tissue‐SELEX on pig cornea. The top two abundant aptamers, Cornea‐S1 and Cornea‐S2, could bind to pig cornea, and their Kd values to human corneal epithelial cells (HCECs) were 361 and 174 nм, respectively. Aptamer‐functionalized liposomes loaded with cyclosporine A (CsA) were developed as a treatment for dry eye diseases. The Kd of Cornea‐S1‐ or Cornea‐S2‐functionalized liposomes reduces to 1.2 and 15.1 nм, respectively, due to polyvalent binding. In HCECs, Cornea‐S1 or Cornea‐S2 enhanced liposome uptake within 15 min and extended retention to 24 h. Aptamer CsA liposomes achieved similar anti‐inflammatory and tight junction modulation effects with ten times less CsA than a free drug. In a rabbit dry eye disease model, Cornea‐S1 CsA liposomes demonstrated equivalence in sustaining corneal integrity and tear break‐up time when compared to commercial CsA eye drops while utilizing a lower dosage of CsA. The aptamers obtained from cornea‐SELEX can serve as a general ligand for ocular drug delivery, suggesting a promising avenue for the treatment of various eye diseases and even other diseases.
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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) | 0.002 | 0.001 |
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".