Bibliographic record
Abstract
Abstract In current clinical practice, corneas with perforations that are repaired by cyanoacrylate gluing and those with recurrent ulcerations are often treated by corneal grafting as the treatment to restore vision. However, there is a severe shortage of donor corneas worldwide. More importantly, when someone has a toothache and visits the dentist, the dentist removed the pathologic portions and fills the tooth. Corneal transplantation for a perforation or ulcer would be similar to extracting the tooth and replacing with an implant. Our objective was to develop a method for restoring pathologic corneas such as those with ulcers or perforations that avoids corneal transplantation. We have developed a Liquid Cornea Patch (LCP) that is based on a collagen‐like peptide conjugated to a polyethylene glycol backbone. The LCP is applied as a viscous liquid that polymerises in situ to form a hydrogel. We previously reported its efficacy in sealing large perforation in vitro (Samarawickrama et al. 2018). We also showed that the crosslinking agent used was non‐toxic to corneal epithelial and endothelial cells at doses used for hydrogel formation. Here, we report on a second‐generation patch which we successfully tested in vivo in rabbit and mini‐pig corneas. The LCP withstands bursting pressures up to 170 mm Hg. Although not as strong as cyanoacrylate glue, the bursting pressure is much higher than would be encountered in the human eye (where normal intraocular pressure is between 12 and 22 mm Hg). Over 12 months in mini‐pig corneas, the LCPs promoted regeneration of cornea tissue and nerves. We show that nerve in‐growth proceeded as per solid versions of collagen and collagen‐like peptide‐based implants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".