Self‐assembled rosette nanotubes and poly(2‐hydroxyethyl methacrylate) hydrogels promote skin cell functions
Bibliographic record
Abstract
The next generation skin of wound healing materials should stimulate skin regeneration by actively promoting appropriate cellular adhesion and proliferation. As materials with novel self-assembling and solidification properties when transitioning from room to body temperatures, rosette nanotubes (RNTs) may be such a proactive material. RNTs resemble naturally occurring nanostructures in the skin (such as collagen and keratin) assembling with noncovalent forces in physiological environments. Presenting desirable bioactive properties, RNTs have been used for various tissue engineering applications including increasing in vivo bone and cartilage regeneration. The objective of the current in vitro study was, for the first time, to improve properties of a commonly used hydrogel (poly(2-hydroxyethyl methacrylate) or pHEMA) for skin regeneration by incorporating one type of novel self-assembled RNTs, called TBL. Results showed for the first time increased keratinocyte and fibroblast proliferation on hydrogels coated with TBLs compared to those not coated with TBL. In this manner, this study provides the first evidence that TBL RNTs are promising for wound healing applications due to their optimal cytocompatibility, solidification, and mechanical properties and, thus, should be further studied for such applications.
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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".