Alterations in fibrinolytic system proteins PAI-1, MMP-3, MMP-8, TIMP-1 and TIMP-2 in post-cholecystectomy bile duct injury
Bibliographic record
Abstract
Introduction: In bile duct injuries (BDI), cholestasis and cholangitis can alter the fibrinolytic system by promoting an increase of extracellular matrix depositions which favor an imbalance between metalloproteinases (MMPs) and their tissue inhibitors (TIMPs). Materials and Methods: Levels of PAI-1, MMP-3, MMP-8, TIMP-1 and TIMP-2 in 35 patients with post-cholecystectomy BDI by complete biliary obstruction were measured and compared to a healthy control group. Sirius red staining and immune staining for MMP-3 and MMP-8 were also undertaken in liver biopsies. Results: Levels of PAI-1, TIMP-1, TIMP-2 and MMP-8 were higher in BDI than healthy controls: 15 ± 2 ng/mL vs 7.1 ± 2 ng/mL (p 0.024); 539 ± 64 ng/mL vs 256 ± 13 ng/mL (p p p 2 vs. 22865.7 ± 3865 μm2 in healthy controls (p 2 vs. 30744.2 ± 5810.2 μm2 (p 2 vs. 116337.9 ± 24803.3 μm2 (p 0.55). These results suggest an imbalance between fibrogenic/fibrinolytic protein levels. Interestingly, expression of the fibrinolytic protein MMP-8 was increased in serum and liver biopsies in BDI. Conclusion: We found an imbalance of profibrogenic molecules which promote extracellular matrix deposition. The over-expression of fibrinolytic proteins such as MMP-8 could limit liver fibrosis, preventing hepatic dysfunction in post-cholecystectomy BDI.
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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 | 0.000 | 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".