Fibrin Glues in Combination with Mesenchymal Stem Cells to Develop a Tissue-Engineered Cartilage Substitute
Bibliographic record
Abstract
Damage of cartilage due to traumatic or pathological conditions results in disability and severe pain. Regenerative medicine, using tissue engineering-based constructs to enhance cartilage repair by mobilizing chondrogenic cells, is a promising approach for restoration of structure and function. Fresh fibrin (FG) and platelet-rich fibrin (PR-FG) glues produced by the CryoSeal(®) FS System, in combination with human bone marrow-derived mesenchymal stem cells (BM-hMSCs), were evaluated in this study. We additionally tested the incorporation of heparin-based delivery system (HBDS) into these scaffolds to immobilize endogenous growth factors as well as exogenous transforming growth factor-β(2). Strongly, CD90+ and CD105+ hMSCs were encapsulated into FG and PR-FG with and without HBDS. Encapsulation of hMSCs in PR-FG led to increased expression of collagen II gene at 2.5 weeks; however, no difference was observed between FG and PR-FG at 5 weeks. The incorporation of HBDS prevented the enhancement of collagen II gene expression. BM-hMSCs in FG initially displayed enhanced aggrecan gene expression and increased accumulation of Alcian blue-positive extracellular matrix; incorporation of HBDS into these glues did not improve aggrecan gene expression and extracellular matrix accumulation. The most significant effect on cartilage marker gene expression and accumulation was observed after encapsulation of hMSCs in FG. We conclude that FG is more promising than PR-FG as a scaffold for chondrogenic differentiation of hMSCs; however, immobilization of growth factors inside these fibrin scaffolds with the HBDS system has a negative impact on this process. In addition, BM-hMSCs are valid and potentially superior alternatives to chondrocytes for tissue engineering of articular cartilage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".