New ligament healing model based on tissue‐engineered collagen scaffolds
Bibliographic record
Abstract
The anterior cruciate ligament (ACL) is often the target of knee trauma. This ligament does not heal very well, leading to joint instability. Long-term instability of the knee can lead to early arthritis and loss of function. To develop efficient strategies to stimulate posttraumatic ACL regeneration in vivo, a good healing model is needed in vitro. Such a model must remain as simple as possible, but should include key features to provide relevant answers to precise questions about the clinical problem addressed. Here, we report tissue-engineered type I collagen scaffolds developed to establish an ACL healing model in vitro and a potential ACL substitute in vivo. Such scaffolds were used to evaluate ACL cell growth, migration, and the capacity to synthesize and assemble collagen fibers for up to 40 days in vitro and up to 180 days in vivo. They were anchored with two bone plugs to allow their static stretching in culture and to facilitate their surgical implantation in knee joints. Our results have shown that living ACL fibroblasts can attach, migrate, and colonize this type of scaffold. In vitro, the cells populated the scaffolds and expressed mRNAs coding for the prolyl-4-hydroxylase, involved in collagen fibers' assembly. In vivo, acellular implants were vascularized and populated with caprine cells that migrated from the osseous insertions toward the center of the grafts. This model is a very good tool to study ACL repair and identify the factors that could accelerate its healing postsurgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".