Functional fabrication of recombinant human collagen–phosphorylcholine hydrogels for regenerative medicine applications
Bibliographic record
Abstract
The implant-host interface is a critical element in guiding tissue or organ regeneration. We previously developed hydrogels comprising interpenetrating networks of recombinant human collagen type III and 2-methacryloyloxyethyl phosphorylcholine (RHCIII-MPC) as substitutes for the corneal extracellular matrix that promote endogenous regeneration of corneal tissue. To render them functional for clinical application, we have now optimized their composition and thereby enhanced their mechanical properties. We have demonstrated that such optimized RHCIII-MPC hydrogels are suitable for precision femtosecond laser cutting to produce complementing implants and host surgical beds for subsequent tissue welding. This avoids the tissue damage and inflammation associated with manual surgical techniques, thereby leading to more efficient healing. Although we previously demonstrated in clinical testing that RHCIII-based implants stimulated cornea regeneration in patients, the rate of epithelial cell coverage of the implants needs improvement, e.g. modification of the implant surface. We now show that our 500μm thick RHCIII-MPC constructs comprising over 85% water are suitable for microcontact printing with fibronectin. The resulting fibronectin micropatterns promote cell adhesion, unlike the bare RHCIII-MPC hydrogel. Interestingly, a pattern of 30μm wide fibronectin stripes enhanced cell attachment and showed the highest mitotic rates, an effect that potentially can be utilized for faster integration of the implant. We have therefore shown that laboratory-produced mimics of naturally occurring collagen and phospholipids can be fabricated into robust hydrogels that can be laser profiled and patterned to enhance their potential function as artificial substitutes of donor human corneas.
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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 | 0.000 |
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".