Primary Human Ligament Fibroblast Adhesion and Growth on 3D-Printed Scaffolds for Tissue Engineering Applications
Bibliographic record
Abstract
The current gold standard technique for the treatment of anterior cruciate ligament (ACL) injury is reconstruction with a tendon autograft. These treatments have a relatively high failure and re-rupture rate and are associated with early-onset osteoarthritis, developing within two decades of injury. Furthermore, both autografting and allografting come with several drawbacks. Tissue engineering and additive manufacturing present exciting new opportunities to explore 3D scaffolds as graft substitutes. We previously showed that 3D-printed scaffolds using low-cost equipment are suitable for tissue engineering approaches to regenerative medicine. Here, we hypothesize that Lay-Fomm 60, a commercially available nanoporous elastomer, may be a viable tissue engineering candidate for an ACL graft substitute. We first printed nanoporous thermoplastic elastomer scaffolds using low-cost desktop 3D printers and determined the mechanical and morphological properties. We then tested the impact of different surface coatings on primary human ACL fibroblast adhesion, growth, and ligamentous matrix deposition in vitro. Our data suggest that poly-L-lysine-coated Lay-Fomm 60 scaffolds increased ligament fibroblast activity and matrix formation when compared to uncoated scaffolds but did not have a significant effect on cell attachment and proliferation. Therefore, uncoated 3D printed Lay-Fomm 60 scaffolds may be viable standalone scaffolds and warrant further research as ligament tissue engineering and reconstruction grafts.
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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 | 0.001 |
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".