Evaluation of photochemically cross-linked collagen/gold nanoparticle composites as potential skin tissue scaffolds
Bibliographic record
Abstract
Collagen type I is the main structural unit in skin tissue and is therefore used preferentially in skin tissue scaffolds. However, collagen-based 3D scaffolds have weak aqueous stability and degradation profiles in their uncross-linked states and chemical cross-linking reagents arise toxicity concerns, which generally restrict the spectrum of their biomedical applicability. Here, the research goal is to photochemically cross-link collagen type I with rose bengal (RB) when subjected to green laser light and to investigate the effect of silk sericin-capped gold nanoparticles (S-AuNP) when incorporated into scaffolds on the cross-linking process and thus on the scaffold properties. All the collagen scaffolds, that is plain collagen (C), collagen/S-AuNP (C-Au), cross-linked collagen (C-RBL), and cross-linked collagen/S-AuNP (C-AuRBL) were characterized for their potential as skin tissue scaffolds. C-AuRBL group had the best thermal stability, resistance to enzymatic degradation, and more uniform pore size distribution. None of the groups had cytotoxicity (cell viability > 70%) regarding the microscopic observations and MTT cell viability assays for L929 fibroblasts. L929 fibroblasts and primary adult human epidermal keratinocytes (HEKa) were also separately seeded on C-AuRBL scaffolds and according to microscopy results, they could support the stimulation of adhesion, morphological changes, and spreading of both cells, thereby encouraging the usage of this fabrication strategy for prospective skin tissue scaffolds.
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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) | 0.000 | 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 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".