Hypoxia and TGF-β3 Synergistically Mediate Inner Meniscus-Like Matrix Formation by Fibrochondrocytes
Bibliographic record
Abstract
Objective: To investigate hypoxia and TGF-β3 effects on inner meniscus-like extracellular matrix (ECM) formation and related gene expression by meniscus fibrochondrocytes (MFCs). Design: Aggregates of human MFCs were cultured for 3 weeks under hypoxia (3% O 2 ) or normoxia (atmospheric O 2 ) with or without TGF-β3 supplementation (10 ng/mL). Inner meniscus-like ECM formation was assessed by biochemistry, histology, and immunofluorescence. mRNA expression of ECM-related genes, TGF-β isoforms 1–3, and hypoxia-inducible factor-1 (HIF-1) targets were assessed by quantitative real-time polymerase chain reaction. Results: Hypoxia and TGF-β3 supplementation synergistically induced inner meniscus-like ECM formation at the protein level with similar effects on ECM-related gene expression. Hypoxia alone did not induce an inner meniscus-like ECM-forming response nor upregulate mRNA of TGF-β isoforms. Expression of HIF-1α and HIF-1 target genes suggested that HIF-1 was a possible contributor to the observed synergistic interactions of hypoxia and TGF-β3 supplementation. Conclusion: Hypoxia and TGF-β3 supplementation synergistically induced inner meniscus ECM formation by adult human MFCs. Hypoxia alone is insufficient to induce an inner meniscus ECM-forming response in this culture model. Impact Statement The interactions of hypoxia and TGF-β3 in aggregates of human meniscus fibrochondrocytes are synergistic in nature, suggesting combinatorial strategies using these factors are promising for tissue engineering the inner meniscus regions. Hypoxia alone in the absence of TGF-β supplementation may be insufficient to initiate an inner meniscus-like extracellular matrix-forming response in this model.
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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".