Matrix Metalloproteinase and Tissue Inhibitor of Matrix Metalloproteinase mRNA Levels are Specifically Altered in Torn Rotator Cuff Tendons
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it