Bibliographic record
Abstract
The extracellular matrix (ECM) plays important roles in maintaining physiological structure and functions of various tissues and organs. Cardiac fibrosis is the excess deposition of ECM, including both fibrillar (collagens I and III) and non-fibrillar proteins. Characteristics of fibrosis can vary depending on the pathology, with focal fibrosis occurring following myocardial infarction (MI), and diffuse interstitial and perivascular fibrosis mainly in non-ischemic heart diseases. Compliance of the fibrotic tissue is significantly lower than the normal myocardium, and this can compromise the diastolic, as well as systolic dysfunction. Therefore, strategies to combat cardiac fibrosis have been investigated. Upon injury or inflammation, activated cardiac fibroblasts (myofibroblasts) produce more ECM proteins and cause fibrosis. The activation could be inhibited or the myofibroblasts could be ablated by targeting their specific expressed proteins. Modulation of tissue inhibitors of metalloproteinases (TIMPs) and moderate exercise can also suppress cardiac fibrosis. More recently, sex differences in cardiac fibrosis have come to light with differential fibrotic response in heart diseases as well as in fibroblast functions in vitro . This mini-review discusses recent progress in cardiac fibroblasts, TIMPs, sex differences and exercise in modulation of cardiac 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".