A dermis-on-a-chip model for compound screening
Bibliographic record
Abstract
Dermal fibrosis is a significant barrier to effective wound healing, with excessive myofibroblast activation and extracellular matrix deposition leading to scar formation and compromised tissue function. Current in vitro models for studying dermal fibrosis, such as monolayer cultures and human skin equivalents (HSEs), have limited physiological relevance or scalability for drug screening. Here, we present a dermis-on-a-chip platform to enable screening of anti-fibrotic compounds in physiologically-relevant 3D dermal microtissues. Upon treatment with transforming growth factor beta (TGFβ), the tissues exhibited hallmarks of fibrosis, including impaired integrity, increased tensile forces, altered cellular morphology, and a pro-fibrotic cytokine profile. Conversely, incorporation of QHREDGS (Q-peptide), an angiopoietin-1 derived peptide with known regenerative properties, selectively modulated these fibrotic changes. Q-peptide was found to reduce TGFβ-induced tensile forces, suppress smooth muscle actin (SMA) expression, and upregulate certain cytokines associated with wound repair. Overall, these findings demonstrate the utility of our dermis-on-a-chip model in compound screening. • We present a dermis-on-a-chip platform for anti-fibrotic compound screening. • Our platform monitors key fibrotic readings in 3D microtissues over 2–4 days. • TGFβ induced several characteristics of a profibrotic phenotype in microtissues. • Soluble and conjugated QHREDGS peptide modulated aspects of the fibrotic effects. • This work demonstrates the potential of our platform for antifibrotic drug screening.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".