Hypoxic ADSCs-derived EVs promote the proliferation and chondrogenic differentiation of cartilage stem/progenitor cells
Bibliographic record
Abstract
Cartilage tissue engineering is a promising option for repairing cartilage defects, although harvesting a large number of seeding cells remains a major challenge. Cartilage stem/progenitor cells (CSPCs) seem to be a promising cell source. Hypoxic extracellular vesicles (EVs) may play a major role in cell-cell and tissue-tissue communication. In the current study, we aimed to evaluate the effect of hypoxic adipose-derived stem cells (ADSCs)-derived EVs on CSPCs proliferation and differentiation. The characteristics of ADSCs-derived EVs were identified, and proliferation, migration, and cartilage-related gene expression of CSPCs were measured with or without the presence of hypoxic ADSCs-derived EVs. SEM, histological staining, biochemical and biomechanical analysis was performed to evaluate the effect of hypoxic ADSCs-derived EVs on CSPCs in alginate hydrogel culture. The results indicated that the majority of ADSC-derived EVs exhibited a round-shaped or cup-shaped morphology with a diameter of 40–1000 nm and expressed CD9, CD63, and CD81. CSPCs migration and proliferation were enhanced by hypoxic ADSCs-derived EVs, which also increased the expression of cartilage-related genes. The hypoxic ADSCs-derived EVs induce CSPCs to produce significantly more cartilage matrix and proteoglycan. In conclusion, hypoxic ADSCs-derived EVs improved the proliferation and chondrogenic differentiation of CSPCs for cartilage tissue engineering.
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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) | 0.003 | 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".