The Non-Fibrillar Side of Fibrosis: Contribution of the Basement Membrane, Proteoglycans, and Glycoproteins to Myocardial Fibrosis
Bibliographic record
Abstract
The extracellular matrix (ECM) provides structural support and a microenvironmentfor soluble extracellular molecules. ECM is comprised of numerous proteins which can be broadly classified as fibrillar (collagen types I and III) and non-fibrillar (basement membrane, proteoglycans, and glycoproteins). The basement membrane provides an interface between the cardiomyocytes and the fibrillar ECM, while proteoglycans sequester soluble growth factors and cytokines. Myocardial fibrosis was originally only linked to accumulation of fibrillar collagens, but is now recognized as the expansion of the ECM including the non-fibrillar ECM proteins. Myocardial fibrosis can be reparative to replace the lost myocardium (e.g., ischemic injury or myocardial infarction), or can be reactive resulting from pathological activity of fibroblasts (e.g., dilated or hypertrophic cardiomyopathy). Contribution of fibrillar collagens to fibrosis is well studied, but the role of the non-fibrillar ECM proteins has remained less explored. In this article, we provide an overview of the contribution of the non-fibrillar components of the extracellular space of the heart to highlight the potential significance of these molecules in fibrosis, with direct evidence for some, although not all of these molecules in their direct contribution to fibrosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".