The myofibroblast matrix: implications for tissue repair and fibrosis
Bibliographic record
Abstract
Myofibroblasts, and the extracellular matrix (ECM) in which they reside, are critical components of wound healing and fibrosis. The ECM, traditionally viewed as the structural elements within which cells reside, is actually a functional tissue whose components possess not only scaffolding characteristics, but also growth factor, mitogenic, and other bioactive properties. Although it has been suggested that tissue fibrosis simply reflects an 'exuberant' wound-healing response, examination of the ECM and the roles of myofibroblasts during fibrogenesis instead suggest that the organism may be attempting to recapitulate developmental programmes designed to regenerate functional tissue. Evidence of this is provided by the temporospatial re-emergence of embryonic ECM proteins by fibroblasts and myofibroblasts that induce cellular programmatic responses intended to produce a functional tissue. In the setting of wound healing (or physiological fibrosis), this occurs in a highly regulated and exquisitely choreographed fashion which results in cessation of haemorrhage, restoration of barrier integrity, and re-establishment of tissue function. However, pathological tissue fibrosis, which oftentimes causes organ dysfunction and significant morbidity or mortality, likely results from dysregulation of normal wound-healing processes or abnormalities of the process itself. This review will focus on the myofibroblast ECM and its role in both physiological and pathological fibrosis, and will discuss the potential for therapeutically targeting ECM proteins for treatment of fibrotic disorders.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".