Photodynamically Crosslinked and Chitosan-incorporated Dentin Collagen
Bibliographic record
Abstract
A lingering concern with restored root-filled teeth is the loss of structural integrity of the dentin and dentin-sealer interface over time. We hypothesized that crosslinking of dentin collagen with simultaneous incorporation of a biopolymer into collagen matrix would improve its structural stability. This study aimed to investigate the effects of combining chemical/photodynamic crosslinking of dentin collagen with the incorporation of carboxymethyl-chitosan (CMCS) on the resistance to enzymatic degradation and mechanical properties of dentin collagen. Ninety-six demineralized dentin collagen specimens (human, n = 72; and bovine, n = 24) were prepared and crosslinked chemically/ photodynamically, with/without CMCS. Glutaraldehyde and carbodiimides were used for chemical crosslinking, while rose Bengal activated with a non-coherent light (540 nm) at 20 J/cm(2) was applied for photodynamic crosslinking. The crosslinked human dentin collagen was subjected to chemical characterization, 7 days enzymatic degradation, and transmission electron microscopy (TEM), while the bovine dentin collagen was used for tensile-testing. Crosslinked collagen showed significantly higher resistance to enzymatic degradation (p < 0.01), stable ultrastructure, and increased tensile strength (p < 0.05). Crosslinking CMCS with collagen matrix as observed in the TEM further improved the mechanical properties of dentin collagen (p < 0.01). This study highlighted the possibility of improving the resistance and toughness of dentin collagen by chemically/photodynamically crosslinking collagen matrix with CMCS.
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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".