Flowable Polyethylene Glycol Hydrogels Support the in Vitro Survival and Proliferation of Dermal Progenitor Cells in a Mechanically Dependent Manner
Bibliographic record
Abstract
Cell-based therapies have garnered considerable interest largely because of their potential utility for tissue regeneration in a variety of organs, including skin. Designing vehicles that enable optimal delivery and purposeful integration of donor cells within tissues will be critical for their success. Here, we investigate the utility of an injectable, self-polymerizing, fully synthetic hydrogel in supporting the survival, proliferation, and function of cultured adult dermal progenitor cells (DPCs) which may serve as a source of renewable cells to repair severe skin injuries or restore hair growth. We show that modifying the stiffness of these transglutaminase cross-linked poly(ethylene glycol) (TG-PEG) hydrogels significantly alters DPC behavior and phenotype; increasing stiffness promotes their differentiation and migration whereas softer gels maintained them in a proliferative state. We found that 2-3% TG-PEG was optimal to promote cell expansion and survival. Unexpectedly, DPCs grown in all conditions maintained their inductive function and thus generated de novo hair follicles. Our data suggests that TG-PEG hydrogels may be a versatile platform for stem and progenitor cell transplantation and fate specification while maintaining functional competence.
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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) | 0.000 | 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) | 0.001 | 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 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".