Corneal bioprinting utilizing collagen‐based bioinks and primary human keratocytes
Bibliographic record
Abstract
Corneal transplantation is the treatment of choice for patients with advanced corneal diseases. However, the outcome may be affected by graft rejection, high associated costs, surgical expertise, and most importantly the worldwide donor shortage. In recent years, bioprinting has emerged as an alternative method for fabricating tissue equivalents using autologous cells with architecture resembling the native tissue. In this study, we propose a freeform and cell-friendly drop-on-demand bioprinting strategy for creating corneal stromal 3D models as suitable implants. Corneal stromal keratocytes (CSK) were bioprinted in collagen-based bioinks as 3D biomimetic models and the geometrical outcome as well as the functionality of the bioprinted specimens were evaluated after in vitro culture. We showed that our bioprinting method is feasible to fabricate translucent corneal stromal equivalents with optical properties similar to native corneal stromal tissue, as proved by optical coherence tomography. Moreover, the bioprinted CSK were viable after the bioprinting process and maintained their native keratocyte phenotypes after 7 days in in vitro culture, as shown by immunocytochemistry. The proposed bioprinted human 3D corneal models can potentially be used clinically for patients with corneal stromal 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) | 0.001 | 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".