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Record W2028534230 · doi:10.1160/th04-08-0522

Myocardial extra-cellular matrix and its regulation by metalloproteinases and their inhibitors

2005· review· en· W2028534230 on OpenAlexaff
Zamaneh Kassiri, Rama Khokha

Bibliographic record

VenueThrombosis and Haemostasis · 2005
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer Research
Fundersnot available
KeywordsMatrix metalloproteinaseMedicineMatrix (chemical analysis)Cell biologyCardiologyComputational biologyBiologyInternal medicineChemistry

Abstract

fetched live from OpenAlex

Cardiovascular disease poses a major health care burden in the Western world. Following myocardial injuries, ventricular remodelling and dysfunction ensue, which can eventually culminate in heart failure. An important event in left ventricular (LV) remodelling is alteration of the extracellular matrix (ECM) integrity, the structural network that interconnects the myocardial components. The critical role of ECM remodelling in cardiac dilation and heart failure was recognized more than a decade ago, and the molecular factors responsible for this process are now being explored. Abnormal ECM turnover is primarily brought about by an imbalance in the activity of matrix metalloproteinases (MMPs) that degrade ECM components, and their endogenous inhibitors, tissue inhibitors of metalloproteinases (TIMPs). Here we provide an overview of composition of the cardiac ECM, and alterations in ECM regulatory proteins, MMPs and TIMPs, in human heart disease. We also discuss the role of TIMPs, MMPs, and a disintegrin and metalloproteinase (ADAMs) enzymes in cardiac development and function as learned through genetically altered mouse models.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.004

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 head0.056
GPT teacher head0.326
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations80
Published2005
Admission routes1
Has abstractyes

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