The Use of Autologous Fibroblasts for the Reconstruction of the Anterior Cruciate Ligament Tears
Bibliographic record
Abstract
Surgical reconstruction using an autologous or allogenic allograft is the treatment of choice for anterior cruciate ligament (ACL) tear repair. We have checking the treatment of ACL tears with in vitro cultured autologous fibroblasts on porcine type I/III collagen membranes, in 12 sheep randomly assigned to be treated with collagen membrane without cells or with porcine membrane plus 5 million autologous fibroblasts per cm2. The animals underwent a first surgery in which a biopsy of the ACL was taken, and a second one in which the membrane was implanted alone (N=6) or with the fibroblasts (N=6) isolated from the biopsy. After 16 weeks, the animals were sacrificed and samples of healthy and repaired ACL were taken for histological and gene expression of type I collagen (COL1), MMP-13 (matrix metallopeptidase 13), and tenascin-C (TNC) studies. The architecture of normal ACL was not conserved either in the ACL treated with the membrane with or without cells. However, a higher degree of peripheral vascularization was observed in the ACL treated with membrane plus fibroblasts than in those treated with membrane alone. Cellularity and nucleus volume were significantly higher in the center or the periphery of the neo-formed ACL than in the control ACL. No differences between the ACL treated with membrane alone or with membrane plus cells were observed in the relative COL1, MMP-13 and tenascin-C expression. Fibroblasts embedded in porcine type I/III membranes probably enhance vascularization of the graft, so it could be a promising tool for the treatment of ACL tears.
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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".