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Record W4384625975 · doi:10.1002/ptr.7959

Extracellular matrix turnover: phytochemicals target and modulate the dual role of matrix metalloproteinases (<scp>MMPs</scp>) in liver fibrosis

2023· review· en· W4384625975 on OpenAlexafffund
Usman Sabir, Hongmei Gu, Dawei Zhang

Bibliographic record

VenuePhytotherapy Research · 2023
Typereview
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchInstitute of Nutrition, Metabolism and DiabetesUniversity of Alberta
KeywordsMatrix metalloproteinaseExtracellular matrixFibrosisHepatic stellate cellDownregulation and upregulationCell biologyCancer researchBiologyImmunologyChemistryMedicinePathologyBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.097
GPT teacher head0.414
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations30
Published2023
Admission routes2
Has abstractyes

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