A Simple, Cross-linked Collagen Tissue Substitute for Corneal Implantation
Bibliographic record
Abstract
PURPOSE: To develop a simple corneal substitute from cross-linked collagen. METHODS: Porcine type I collagen (10%; pH 5), was mixed with 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) and N-hydroxysuccinimide (NHS). The final homogenous solution was molded to corneal dimensions, cured, and then implanted into rabbits and minipigs by lamellar keratoplasty. The implants were followed for up to 6 months after surgery. Clinical examinations of the cornea included detailed slit lamp biomicroscopy, in vivo confocal microscopy, topography and esthesiometry for nerve function. Histopathologic examinations were also performed on rabbit corneas harvested after 6 months. RESULTS: Cross-linked collagen (refractive index, 1.35) had optical clarity superior to human corneas. Implanted into rabbit and porcine corneas, only 1 of 24 of the surgical corneas showed a slight haze at 6 months after surgery. All other implants showed no adverse reactions and remained optically clear. Topography showed a smooth surface and a profile similar to that of the contralateral nonsurgical eye. The implanted matrices promoted regeneration of corneal cells, tear film, and nerves. Touch sensitivity was restored, indicating some restoration of function. The corneas with implants showed no significant loss of thickness and demonstrated stable host-graft integration. CONCLUSIONS: Collagen can be adequately stabilized, using water soluble carbodiimides as protein cross-linking reagents, in the fabrication of corneal matrix substitutes for implantation. The simple cross-linking methodology would allow for easy fabrication of matrices for transplantation in centers where there is a shortage of corneas, or where there is need for temporary patches to repair perforations in emergency situations.
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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) | 0.001 | 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 | 0.001 |
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".