Synergistic strategy of riboflavin and lipoids to bioengineer resin dentin hybrid layer
Bibliographic record
Abstract
The study aimed to synthesize and evaluate the effects of an experimental adhesive system containing different concentrations of riboflavin in combination with Lipoid/phosphatidylcholine solutions on the dentin-bonding interface. Lipoid solutions were prepared in riboflavin (RF) experimental self-etching adhesive, (Ct 0 , RF 0.5% , RFLi 0.5%/0.25% , RFLi 0.5%/0.5% ). Resin-dentin slabs were prepared for hybrid layer evaluation and microtensile strength was tested. Interfacial leakage was also determined using silver tracer nanoleakage. The mechanical properties of collagen fibrils were evaluated. The adhesive was assessed for contact angle and degree of conversion. A biofilm based on Streptococcus mutans, Actinomyces naeslundii, and Streptococcus sanguis was used to evaluate fluorescence in-situ hybridization for antimicrobial analysis . Collagen was examined using a transmission electron microscope with in-situ hybridization. Human macrophages were stained for immunofluorescence. Resin tags were detectable in all adhesive specimens. RFLi preserved adhesive bond strength after long-term aging. Sizes and dispersion of fibrils in RF 0.5% , RFLi 0.5%/0.25% , RFLi 0.5%/0.5% were substantially larger (p < 0.05). Contact angle values exhibited significant differences (p < 0.05). Both Ei and Hi were impacted by different adhesives. RFLi 0.5%/0.5% group exhibited an increase in the degree of conversion. RFLi 0.5%/0.5% displayed well-preserved collagen fibrils. Confocal images showed the presence of dead bacteria amongst RFLi 0.5%/0.25% /RFLi 0.5%/0.5% groups. CD80 + markers on macrophages were detected in the RFLi 0.5%/0.25% /RFLi 0.5%/0.5% groups. RFLi 0.5%/0.5% modified adhesives show enhanced bonding to dentin and can be expected to prolong the long-term integrity of the resin-dentin interface.
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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) | 0.001 | 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".