Adapting the Scar-in-a-Jar to Skin Fibrosis and Screening Traditional and Contemporary Anti-Fibrotic Therapies
Bibliographic record
Abstract
Skin fibrosis still constitutes an unmet clinical need. Although pharmacological strategies are at the forefront of scientific and technological research and innovation, their clinical translation is hindered by the poor predictive capacity of the currently available in vitro fibrosis models. Indeed, customarily utilised in vitro scarring models are conducted in a low extracellular matrix milieu, which constitutes an oxymoron for the in-hand pathophysiology. Herein, we coupled macromolecular crowding (enhances and accelerates extracellular matrix deposition) with transforming growth factor β1 (TGFβ1; induces trans-differentiation of fibroblasts to myofibroblasts) in human dermal fibroblast cultures to develop a skin fibrosis in vitro model and to screen a range of anti-fibrotic families (corticosteroids, inhibitors of histone deacetylases, inhibitors of collagen crosslinking, inhibitors of TGFβ1 and pleiotropic inhibitors of fibrotic activation). Data obtained demonstrated that macromolecular crowding combined with TGFβ1 significantly enhanced collagen deposition and myofibroblast transformation. Among the anti-fibrotic compounds assessed, trichostatin A (inhibitors of histone deacetylases); serelaxin and pirfenidone (pleiotropic inhibitors of fibrotic activation); and soluble TGFβ receptor trap (inhibitor of TGFβ signalling) resulted in the highest decrease of collagen type I deposition (even higher than triamcinolone acetonide, the gold standard in clinical practice). This study further advocates the potential of macromolecular crowding in the development of in vitro pathophysiology models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".