Second Generation of Tissue-Engineered Ligament Substitutes for Torn ACL Replacement: Adaptations for Clinical Applications
Bibliographic record
Abstract
The anterior cruciate ligament (ACL) of the knee joint is one of the strongest ligaments of the body and is often the target of traumatic injuries. Unfortunately, its healing potential is limited, and the surgical options for its replacement are frequently associated with clinical issues. A bioengineered ACL (bACL) was developed using a collagen matrix, seeded with autologous cells and successfully grafted and integrated into goat knee joints. We hypothesize that, in order to reduce the cost and simplify the model, an acellular bACL can be used as a substitute for a torn ACL, and bone plugs can be replaced by endobuttons to fix the bACL in situ. First, acellular bACLs were successfully grafted in the goat model with 18% recovery of ultimate tensile strength 6 months after implantation (94 N/mm2 vs. 520). Second, a bACL with endobuttons was produced and tested in an exvivo bovine knee model. The natural collagen scaffold of the bACL contributes to supporting host cell migration, growth and differentiation in situ post-implantation. Bone plugs were replaced by endobuttons to design a second generation of bACLs that offer more versatility as biocompatible grafts for torn ACL replacement in humans. A robust collagen bACL will allow solving therapeutic issues currently encountered by orthopedic surgeons such as donor-site morbidity, graft failure and post-traumatic osteoarthritis.
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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 | 0.001 | 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".