Development of a Porcine Model to Assess the Effect of In Situ Knee Joint Loading on Site-Specific Cartilage Gene Expression
Bibliographic record
Abstract
Cyclic mechanical loading of cartilage induces stresses and fluid flow, which are thought to modulate chondrocyte metabolism. The uneven surface, plus the heterogeneity of cartilage within a joint, makes stress and fluid pressure distribution in the tissue nonuniform, and gene expression may vary at different sites as a function of load magnitude, frequency, and time. In previous studies, cartilage explants were used for loading tests to investigate biological responses of the cartilage to mechanical loading. In contrast, we used loading tests on intact knee joints, to better reflect the loading conditions in a joint, and thus provide a more physiologically relevant mechanical environment. Gene expression levels in loaded samples for a selection of relevant genes were compared with those of the corresponding unloaded control samples to characterize potential differences. Furthermore, the effects of load magnitude and duration on gene expression levels were investigated. We observed differences in gene expression levels between samples from different sites in the same joint and between corresponding samples from the same site in loaded and unloaded joints. Consistent with previous findings, our results indicate that there is a critical upper and lower threshold of loading for triggering the expression of certain genes. Variations in gene expression levels may reflect the effect of local loading, topography, and structure of the cartilage in an intact joint on the metabolic activity of the associated cells.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".