Evaluation of the Dentinal Shear Bond Strength and Resin Interface in Primary Molars after Pre-Treatment with Various Dentin Bio-Modifiers: An In Vitro Study
Bibliographic record
Abstract
Dentine adhesives have demonstrated great success with permanent teeth. Though the results in primary teeth are not well documented, some studies have demonstrated lower values of bond strength in primary teeth than those found in permanent teeth. The aim of this study was to compare and evaluate the effect of grape seed extract (6.5%) (Herbal Bio Solutions, Delhi, India), glutaraldehyde (5%) (Loba Chemie PVT. LTD., Mumbai), hesperidin (0.5%) (Herbal Bio Solutions, Delhi, India), and casein phosphopeptide-amorphous calcium phosphate (tooth mousse) (GC Corporation, Alsip, IL, USA) on the shear bond strength of dentine of primary teeth and to evaluate the resin tags at the resin tooth interface. Seventy-five caries-free human primary molars were collected, and their occlusal surfaces were ground flat. Dentin surfaces were etched using phosphoric acid. Then teeth were randomly assigned in sequential order to five groups according to the dentinal treatment method: Group I (Control group) (no treatment), Group II (5% glutaraldehyde), Group III (6.5% grape seed extract), Group IV (0.5% hesperidin), and Group V (CPP-ACP). Ten teeth from each group were assigned for Shear Bond Strength and five for SEM analysis. ANOVA and a post hoc least significant difference test (p < 0.05) were used for statistical analysis of the collected data. The grape seed extract group showed significantly increased shear bond strength than the control group (p < 0.05), and the mean length of resin tags in different dentine bio modifiers groups was also statistically significant (p < 0.05). The use of dentin bio modifiers such as 5% glutaraldehyde, 6.5% grape seed extract, 0.5% hesperidin, and CPP-ACP in the bonding process for primary teeth did not improve the dentinal bond strength.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".