Characterization of a Conjugate between Rose Bengal and Chitosan for Targeted Antibiofilm and Tissue Stabilization Effects as a Potential Treatment of Infected Dentin
Bibliographic record
Abstract
Bacterial biofilms and dentin structural changes are some of the major challenges in the management of infected dentin tissue. This study characterized a photosensitizer-conjugated chitosan with enhanced photodynamic efficacy against dental biofilms, as well as the ability to reinforce the postinfected dentin matrix in order to improve its mechanical and chemical stability. Rose Bengal-conjugated chitosan (CSRB) was synthesized using a chemical cross-linking method and characterized for photophysical, photobiological, and cytotoxicity properties. Its potential as an antibacterial and matrix-reinforcing agent on dentin collagen was also evaluated. Enterococcus faecalis as planktonic and in vitro biofilms was treated with CSRB and photodynamically activated with 5 to 60 J/cm(2) green light. Dentin collagen was used for the CSRB cross-linking experiments and evaluated for chemical changes, resistance to enzymatic degradation, and mechanical properties. CSRB was a photosensitizer with efficient singlet oxygen yield. In vitro photoactivation gave higher fibroblast cell survival than did RB alone. CSRB showed significant antibiofilm photoinactivation (P < 0.01). The CSRB-cross-linked dentin collagen showed higher resistance to collagenase degradation and superior mechanical properties (P < 0.05). In summary, the photoactivated CSRB particles synthesized in this study may be a synergistic multifunctional treatment approach with lower cytotoxicity and effective antibiofilm activity as well as the ability to reinforce the dentin collagen to enhance resistance to degradation and improve mechanical properties. This may be a targeted treatment strategy to deal with infected dentin hard tissues in a clinical scenario, where both disinfection and structural integrity need to be addressed concomitantly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".