Riboflavin-sensitized photo-crosslinking of collagen using a dental curing light
Bibliographic record
Abstract
BACKGROUND: Photo-crosslinking of biomolecules such as collagen and fibrinogen is an emerging area of research interest. The use of a small dental curing light with a non-toxic photosensitizer represents a novel, practical approach to periodontal wound treatment. OBJECTIVE: This study evaluated the effects of riboflavin-sensitized photo-oxidation using a dental curing light on two collagenous biomaterials, as a preliminary step towards developing a medical technology for wound closure/healing. METHODS: A collagenous biomaterial (DBP) and type I collagen gels were treated by this photo-oxidative technique and characterized by hydrothermal isometric tension (HIT) analysis, amino acid analysis, SDS-PAGE, and rheology. RESULTS: HIT analysis suggested that dental curing light exposure for 300 s with riboflavin produced heavily crosslinked DBP. Dental curing light exposure for 300 s with riboflavin also showed a reduction in lysine concentration of DBP. SDS-PAGE showed that dental curing light exposure for 30 or 300 s with riboflavin resulted in crosslinked collagen gels. Dental curing light exposure for 30 s with riboflavin yielded a collagen gel with the strongest rheological characteristics. CONCLUSIONS: This novel approach to wound treatment has potential for wide adoption and clinical use, particularly because dental curing lights, riboflavin, and collagen biomaterials are all used clinically, but not yet combined together as one technology for broad application.
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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".