Coordinate expression of matrix-degrading proteinases and their activators and inhibitors in bovine skeletal muscle.
Bibliographic record
Abstract
Matrix metalloproteinases (MMP) responsible for degradation of connective tissue are found in most tissues. The MMP are regulated at the levels of transcription, zymogen activation by plasmin or membrane-type- (MT) MMP, and control of enzyme activity by tissue inhibitors of metalloproteinases (TIMP). Whole bovine skeletal muscle showed multiple MMP activities on gelatin zymography and also expressed mRNA encoding MMP-1, -2, -9, -14, and -16, tissue inhibitors of metalloproteinase (TIMP)-1, -2, and -3 and plasminogen activator and its receptor. Purified intramuscular fibroblasts and myogenic cell culture derived from satellite cells expressed most or all of these elements. Statistical analysis (n = 35) revealed a strong positive correlation among the mRNA levels of several elements of the MMP system, including MMP-2, MMP-14, TIMP-1, -2, and -3 (r = 0.614 to 0.930, P < 0.0001). Our results provide an extensive profile of an extracellular proteolytic cascade involving MMP in skeletal muscle and suggest that 1) the activation cascades of muscle MMP may be initiated by both plasmin and membrane-type MMP; 2) a group of genes involved in the same "arm" of zymogen activation are coexpressed in this tissue; and 3) skeletal muscle cells, in addition to the intramuscular fibroblasts, express an extensive complement of MMP and related proteins.
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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 | 0.001 | 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 | 0.000 |
| 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 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".