Short-term response of primary human meniscus cells to simulated microgravity
Bibliographic record
Abstract
BACKGROUND: Mechanical unloading of the knee articular cartilage results in cartilage matrix atrophy, signifying the osteoarthritic-inductive potential of mechanical unloading. In contrast, mechanical loading stimulates cartilage matrix production. However, little is known about the response of meniscal fibrocartilage, a major mechanical load-bearing tissue of the knee joint, and its functional matrix-forming fibrochondrocytes to mechanical unloading events. METHODS: In this study, primary meniscus fibrochondrocytes isolated from the inner avascular region of human menisci from both male and female donors were seeded into porous collagen scaffolds to generate 3D meniscus models. These models were subjected to both normal gravity and mechanical unloading via simulated microgravity (SMG) for 7 days, with samples collected at various time points during the culture. RESULTS: RNA sequencing unveiled significant transcriptome changes during the 7-day SMG culture, including the notable upregulation of key osteoarthritis markers such as COL10A1, MMP13, and SPP1, along with pathways related to inflammation and calcification. Crucially, sex-specific variations in transcriptional responses were observed. Meniscus models derived from female donors exhibited heightened cell proliferation activities, with the JUN protein involved in several potentially osteoarthritis-related signaling pathways. In contrast, meniscus models from male donors primarily regulated extracellular matrix components and matrix remodeling enzymes. CONCLUSION: These findings advance our understanding of sex disparities in knee osteoarthritis by developing a novel in vitro model using cell-seeded meniscus constructs and simulated microgravity, revealing significant sex-specific molecular mechanisms and therapeutic targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".