Cornea-SELEX-derived DNA aptamers for preparing spherical nucleic acids and corneal staining
Bibliographic record
Abstract
Topical eye drops suffer from poor bioavailability due to rapid tear clearance and corneal barriers, which limit their therapeutic efficacy. Aptamers offer a promising solution for targeted ocular delivery and extended drug retention time. In this study, we employed gold nanoparticles (AuNPs) functionalized with aptamers previously selected to bind to porcine corneal tissues, forming spherical nucleic acids (SNAs). The binding of the SNAs to corneal tissues and human corneal epithelial cells (HCECs) was investigated. A total of six different aptamers were tested, each with an extended poly-adenine tail to attach to AuNPs using the thermal evaporation method. Among the six SNAs, the one prepared using the Cornea-S5 aptamer exhibited the best colloidal stability and optimal binding to both corneal tissues and HCECs, and it can specifically stain scarred regions. Flow cytometry determined the dissociation constant of Cornea-S5 to HCECs to be 169 nM. These findings highlight the potential of aptamer-AuNP conjugates for precise ocular drug delivery and diagnostics.
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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 | 0.001 |
| 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".