The Role of Collagen Rheology in Human Keratinocyte Differentiation: Implications for Skin Substitute Development
Bibliographic record
Abstract
Type I collagen hydrogels are widely employed as scaffolds in tissue engineering due to their biocompatibility and ability to mimic the extracellular matrix (ECM). ECM viscoelasticity plays a critical role in regulating key cellular functions such as adhesion, proliferation, and differentiation. This study evaluates how collagen source and quality influence hydrogel architecture, mechanical properties, and keratinocyte behavior. Hydrogels were prepared at a concentration of 2.3 mg/mL using collagen from Advanced Biomatrix (AB, GLP grade) and Collagen Solutions (CS, GMP grade), and assessed for fibrillogenesis, rheological performance, and their ability to support stratified HaCaT keratinocyte cultures. AB-derived hydrogels exhibited higher porosity but lower mechanical resilience, characterized by a linear viscoelastic region (LVER) of 2.54%. In contrast, CS-derived hydrogels showed reduced porosity, denser fiber networks, and a higher LVER of 9.96%, indicating enhanced strain tolerance. HaCaT cells cultured on AB hydrogels showed diminished proliferation, metabolic activity, stratification, and expression of differentiation markers compared to those on CS hydrogels, which supported a more robust epidermal architecture. These findings highlight the critical role of collagen quality and mechanical characteristics on scaffold performance and epidermal tissue formation, emphasizing the need to optimize biomaterial properties for effective regenerative outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".