Extracellular matrix turnover: phytochemicals target and modulate the dual role of matrix metalloproteinases (<scp>MMPs</scp>) in liver fibrosis
Bibliographic record
Abstract
Extracellular matrix (ECM) resolution by matrix metalloproteinases (MMPs) is a well-documented mechanism. MMPs play a dual and complex role in modulating ECM degradation at different stages of liver fibrosis, depending on the timing and levels of their expression. Increased MMP-1 combats disease progression by cleaving the fibrillar ECM. Activated hepatic stellate cells (HSCs) increase expression of MMP-2, -9, and -13 in different chemicals-induced animal models, which may alleviate or worsen disease progression based on animal models and the stage of liver fibrosis. In the early stage, elevated expression of certain MMPs may damage surrounding tissue and activate HSCs, promoting fibrosis progression. At the later stage, downregulation of MMPs can facilitate ECM accumulation and disease progression. A number of phytochemicals modulate MMP activity and ECM turnover, alleviating disease progression. However, the effects of phytochemicals on the expression of different MMPs are variable and may depend on the disease models and stage, and the dosage, timing and duration of phytochemicals used in each study. Here, we review the most recent advances in the role of MMPs in the effects of phytochemicals on liver fibrogenesis, which indicates that further studies are warranted to confirm and define the potential clinical efficacy of these phytochemicals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".