Emerging concepts in cardiac matrix biologyThis article is one of a selection of papers published in a special issue on Advances in Cardiovascular Research.
Bibliographic record
Abstract
The cardiac extracellular matrix, far from being merely a static support structure for the heart, is now recognized to play central roles in cardiac development, morphology, and cell signaling. Recent studies have better shaped our understanding of the tremendous complexity of this active and dynamic network. By activating intracellular signal cascades, the matrix transduces myocardial physical forces into responses by myocytes and fibroblasts, affecting their function and behavior. In turn, cardiac fibroblasts and myocytes play active roles in remodeling the matrix. Coupled with the ability of the matrix to act as a dynamic reservoir for growth factors and cytokines, this interplay between the support structure and embedded cells has the potential to exert dramatic effects on cardiac structure and function. One of the clearest examples of this occurs when cell-matrix interactions are altered inappropriately, contributing to pathological fibrosis and heart failure. This review will examine some of the recent concepts that have emerged regarding exactly how the cardiac matrix mediates these effects, how our collective vision of the matrix has changed as a result, and the current state of attempts to pharmacologically treat fibrosis.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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.
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".