Matrix Metalloproteinase and Tissue Inhibitor of Matrix Metalloproteinase mRNA Levels are Specifically Altered in Torn Rotator Cuff Tendons
Bibliographic record
Abstract
BACKGROUND: Rotator cuff tears are a cause of pain and disability in the shoulder. The molecular changes associated with rotator cuff tearing are unclear. A subset of matrix metalloproteinases and tissue inhibitors of metalloproteinase, which are involved in extracellular matrix remodeling and degradation, were evaluated. HYPOTHESIS: There would be an increase in the mRNA level of specific matrix metalloproteinase and a decrease in the mRNA level of specific tissue inhibitors of metalloproteinase in rotator cuff tendon tissue obtained from patients with rotator cuff tears. STUDY DESIGN: Controlled laboratory study. METHODS: Tissue was obtained from 10 patients undergoing rotator cuff repair for full-thickness rotator cuff tears. Also, tissue was obtained from cadaveric specimens with no gross evidence of rotator cuff tearing. Reverse transcription polymerase chain reaction was performed for the collagenases (MMP-1, MMP-8, MMP-13), the stromelysins (MMP-3, MMP-10, MMP-11), and the tissue inhibitors of metalloproteinase (TIMP-1, TIMP-2, TIMP-3, TIMP-4). Western blotting was performed to confirm the mRNA changes demonstrated in collagenase-3 (MMP-13). RESULTS: There was a significant increase in collagenase-3 (MMP-13) mRNA levels, a decrease in stromelysin-1 (MMP-3) mRNA levels, and a decrease in tissue inhibitor of metalloproteinase-2, -3, and -4 mRNA levels. Western blotting demonstrated an increase in the active form of collagenase-3 (MMP-13) in rotator cuff tendon tears. CONCLUSIONS: The mRNA levels of specific matrix metalloproteinases and tissue inhibitors of metalloproteinase are altered in torn rotator cuff tendons. CLINICAL RELEVANCE: With the known action of the matrix metalloproteinases and tissue inhibitors of metalloproteinase in extra-cellular matrix remodeling, these findings suggest that their roles in remodeling of rotator cuff tears should be further investigated.
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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 | 0.001 |
| 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) | 0.003 | 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".