Stretch and interleukin‐1β induce matrix metalloproteinases in rabbit tendon cells in vitro
Bibliographic record
Abstract
Little is known about the factors that initiate and propagate tendon overuse injuries, but chronic inflammation and matrix destruction have been implicated. The purpose of this study was to evaluate the production of cyclooxygenase II (COX-2) and matrix metalloproteinases (MMPs) by tendon cells exposed to cyclic strain and inflammatory cytokines in vitro. Rabbit Achilles tendon cells were subjected to a stretching protocol with 5% elongation at 0.33 Hz for 6 h, or treated with 1000 pM interleukin-1beta (IL-1beta), or exposed to IL-1beta and stretching together. Gene expression was evaluated by RT-PCR and production of stromelysin was quantified with an ELISA. IL-1beta induced the expression of the collagenase-1 and stromelysin-1 genes. Production of stromelysin proenzyme by cells stimulated with IL-1beta was 17 times higher than production by control cells. Cells exposed to IL-1beta and stretching produced 20 times more stromelysin than control cells. Cells subjected to stretching alone did not produce more stromelysin than control cells. The synergistic effect of IL-1beta and stretching was observed at doses of IL-1beta ranging from 10 to 1000 pM. These data suggest that mechanical load and inflammatory cytokines can initiate a matrix destructive pathway in tendon that is more pronounced than with mechanical loading or inflammation alone.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".