Preparation of Scaffold-Free Tissue-Engineered Constructs Derived from Human Synovial Mesenchymal Stem Cells Under Low Oxygen Tension Enhances Their Chondrogenic Differentiation Capacity
Bibliographic record
Abstract
Low oxygen tension (LOT) has been reported to promote chondrogenic differentiation and prevent cellular senescence of stem cells. Therefore, the introduction of LOT conditions into conventional tissue engineering processes could further improve the potential of the constructs generated for cartilage repair. The purpose of this study was to elucidate the feasibility of LOT preparation on the chondrogenic differentiation of a scaffold-free tissue-engineered construct (TEC) derived from synovial mesenchymal stem cells (MSCs), construct whose feasibility for cartilage repair has been demonstrated in previous preclinical and clinical studies. Culture of MSCs under LOT conditions prevented cellular senescence and promoted the proliferative capacity of human synovial MSCs. In addition, TEC prepared from human synovial MSCs under LOT conditions (5% O2; LOT-TEC) showed superior in vitro chondrogenic differentiation capacity compared to that prepared under the usual 20% O2 (normal oxygen tension [NOT]; NOT-TEC), with elevated glycosaminoglycan production and elevated levels of chondrogenic marker gene expression. Notably, LOT-TEC differentiated into a hyaline-like cartilaginous tissue of approximately 1 cm in diameter without the detectable presence of fibrous tissue, while conventional NOT-TEC differentiated into a mixture of hyaline-like and fibrocartilaginous tissues. This is the first demonstration of in vitro development of a hyaline-like cartilaginous tissue of an implantable size to chondral lesion that was derived from human MSCs without the use of an exogenous scaffold. The manipulation of oxygen tension is a safe procedure with low cost and, thus, may be a clinically relevant option to improve the quality of TEC-mediated cartilage repair.
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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".