Engineered human meniscus’ matrix-forming phenotype is unaffected by low strain dynamic compression under hypoxic conditions
Bibliographic record
Abstract
Low oxygen and mechanical loading may play roles in regulating the fibrocartilaginous phenotype of the human inner meniscus, but their combination in engineered tissues remains unstudied. Here, we investigated how continuous low oxygen ("hypoxia") combined with dynamic compression would affect the fibrocartilaginous "inner meniscus-like" matrix-forming phenotype of human meniscus fibrochondrocytes (MFCs) in a porous type I collagen scaffold. Freshly-seeded MFC scaffolds were cultured for 4 weeks in either 3 or 20% O2 or pre-cultured for 2 weeks in 3% O2 and then dynamically compressed for 2 weeks (10% strain, 1 Hz, 1 h/day, 5 days/week), all with or without TGF-β3 supplementation. TGF-β3 supplementation was found necessary to induce matrix formation by MFCs in the collagen scaffold regardless of oxygen tension and application of the dynamic compression loading regime. Neither hypoxia under static culture nor hypoxia combined with dynamic compression had significant effects on expression of specific protein and mRNA markers for the fibrocartilaginous matrix-forming phenotype. Mechanical properties significantly increased over the two-week loading period but were not different between static and dynamic-loaded tissues after the loading period. These findings indicate that 3% O2 applied immediately after scaffold seeding and dynamic compression to 10% strain do not affect the fibrocartilaginous matrix-forming phenotype of human MFCs in this type I collagen scaffold. It is possible that a delayed hypoxia treatment and an optimized pre-culture period and loading regime combination would have led to different outcomes.
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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".