Circulating levels of matrix metalloproteinases and tissue inhibitors of metalloproteinases in patients with incisional hernia
Bibliographic record
Abstract
Incisional hernia formation is a common complication to laparotomy and possibly associated with alterations in connective tissue metabolism. Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) are closely involved in the metabolism of the extracellular matrix. Our aim was to study serum levels of multiple MMPs and TIMPs in patients with and without incisional hernia. Out of 305 patients who underwent laparotomy, 79 (25.9%) developed incisional hernia over a median follow-up period of 3.7 years. Pooled sera from a subset (n = 72) of these patients were screened for MMP-1, MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, MMP-10, MMP-12, MMP-13, TIMP-1, TIMP-2, and TIMP-4 using a multiplex sandwich fluorescent immunoassay supplemented with gelatin zymography. The screening indicated differences in serum MMP-9 and TIMP-1 levels. Consequently, MMP-9 and TIMP-1 levels were measured in serum in the whole patient cohort with enzyme-linked immunosorbent assay. There were no significant differences in either MMP-9 (p = 0.411) or TIMP-1 (p = 0.679) levels between hernia and hernia-free patients. MMP-9 was significantly increased in smokers compared with nonsmokers (p = 0.016). In conclusion, a possible involvement of MMPs and TIMPs in the pathogenesis of incisional hernia formation was not reflected systemically.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".