Microtissue engineering root canal dentine with crosslinked biopolymeric nanoparticles for mechanical stabilization
Bibliographic record
Abstract
AIM: To evaluate the functional strain distribution pattern in root dentine following canal preparation and root canal surface engineering with crosslinked biopolymeric nanoparticles using digital moiré interferometry (DMI). METHODOLOGY: Root dentine specimens were prepared, grating material replicated and tested for 10-50 N, compressive loads in a customized high-resolution, whole-field moiré interferometry set-up. Digital moiré fringes were acquired to determine the strain distribution pattern at specific regions of interest before and after canal enlargement, and dentine surface engineering with a chitosan nanoparticle crosslinker solution. Fringe patterns were acquired, and strain distribution pattern in the direction perpendicular to dentinal tubules (U-field) and parallel to dentinal tubules (V-field) was analysed with custom digital image-processing software. Data were analysed with a statistical method on trend analysis at 0.05 significant level. RESULTS: Distinct deformation patterns perpendicular to the dentinal tubules were observed in root dentine. Root canal dentine removal following instrumentation resulted in an increase in strain distribution, which increased with an increase in applied loads (P < 0.01). The root canal dentine engineered with crosslinked nanoparticles demonstrated a conspicuous decrease in previously increased strain distribution in both coronal and apical root dentine (P < 0.01). A significant increase in tensile strain in root dentine was observed subsequent to instrumentation in the direction parallel to dentinal tubules (P < 0.01). There was a significant reduction in the tensile strain formed at the apical region of the instrumented root dentine following crosslinked nanoparticle treatment (P < 0.05). CONCLUSIONS: This study highlighted the potential of root canal dentine microtissue engineering with crosslinked chitosan nanoparticle to improve radicular strain distribution patterns in instrumented canals.
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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".