MUSCLE-INDUCED PATELLOFEMORAL JOINT LOADING RAPIDLY AFFECTS CARTILAGE mRNA LEVELS IN A SITE SPECIFIC MANNER
Bibliographic record
Abstract
Mechanical loading of articular cartilage affects the synthesis and degradation of matrix macromolecules. Much of the work in this area has involved mechanical loading of articular cartilage explants or cells in vitro and assessing biological responses at the mRNA and protein levels. In this study, we developed a new experimental technique to load an intact patellofemoral joint in vivo using muscle stimulation. The articular cartilages were cyclically loaded for one hour in a repeatable and measurable manner. Cartilage was harvested from central and peripheral regions of the femoral groove and patella, either immediately after loading or after a three hour recovery period. Total RNA was isolated from the articular cartilage and biological responses were assessed on the mRNA level using the reverse transcriptase-polymerase chain reaction. Articular cartilage from intact patellofemoral joints demonstrated heterogeneity at the mRNA level for six of the genes assessed independent of the loading protocol. Cyclical loading of cartilage in its native environment led to alterations in mRNA levels for a subset of molecules when assessed immediately after the loading period. However, the increases in TIMP-1 and decreases in bFGF mRNA levels were transient; being present immediately after load application but not after a three hour recovery period.
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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) | 0.002 | 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".