A story of fibers and stress: <scp>Matrix‐embedded</scp> signals for fibroblast activation in the skin
Bibliographic record
Abstract
Our skin is continuously exposed to mechanical challenge, including shear, stretch, and compression. The extracellular matrix of the dermis is perfectly suited to resist these challenges and maintain integrity of normal skin even upon large strains. Fibroblasts are the key cells that interpret mechanical and chemical cues in their environment to turnover matrix and maintain homeostasis in the skin of healthy adults. Upon tissue injury, fibroblasts and an exclusive selection of other cells become activated into myofibroblasts with the task to restore skin integrity by forming structurally imperfect but mechanically stable scar tissue. Failure of myofibroblasts to terminate their actions after successful repair or upon chronic inflammation results in dysregulated myofibroblast activities which can lead to hypertrophic scarring and/or skin fibrosis. After providing an overview on the major fibrillar matrix components in normal skin, we will interrogate the various origins of fibroblasts and myofibroblasts in the skin. We then examine the role of the matrix as signaling hub and how fibroblasts respond to mechanical matrix cues to restore order in the confusing environment of a healing wound.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| 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".