Tissue Engineered Meniscus Repair
Bibliographic record
Abstract
Objective. Studies have shown that meniscal repairs have better outcomes over both partial and total meniscectomies. Tissue engineering strategies to repair meniscus tears have been explored using cell sources that involve a donor as well as a period of in vitro cell expansion before use. This study explored cell sources that could be easily harvested and rapidly isolated by enzymatic digestion and cannulated delivery. Methods. Bovine menisci were used to create a bucket handle tear. Cell lines were established from meniscus, synovium, and adipose tissue and fluorescently labeled. At passages P2, P4, and P8, cells were added to the defect from the following experimental groups: cells alone, collagen gel, collagen scaffold, or hyaluronic acid. Menisci constructs were xenografted subcutaneously onto the dorsum of athymic rats and incubated for 3, 6, and 9 weeks, at which time they were retrieved and processed for histology. Results. Meniscal cells were able to repair defects faster and significantly better than adipose or synovium derived cells. Adipose cells were the least effective in comparison. Repair was significantly better at 9 weeks compared with 6 and 3 weeks. Macroscopic examination of menisci that received cell implants showed the thickest tissue in menisci that had collagen implants, and the thinnest fill occurred in menisci treated with cells alone. Histology confirmed no cells or integrative repair in the control specimens. Conclusions. Delivery of cells alone outperformed the additional use of biomaterials. Our results suggest a strategy that would use both meniscus and synovial cells for arthroscopic meniscal repair.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".