Cytocompatibility of Biodentine and <scp>iR</scp>oot <scp>FS</scp> with human periodontal ligament cells: an <i>in vitro</i> study
Bibliographic record
Abstract
AIM: To evaluate the cytocompatibility of Biodentine and iRoot FS with human periodontal ligament cells (hPDLCs). METHODOLOGY: Human periodontal ligament cells were characterized by flow cytometry and immunocytochemical analysis. Human periodontal ligament cell adhesion was assessed by scanning electron microscopy at day 3; proliferation by live/dead assay at days 1, 3 and 7; and osteogenic differentiation by alkaline phosphatase (ALP) activity staining, ALP quantification analysis and qRT-PCR at days 7 and 14. Data were analysed with anova and independent sample t-tests with SPSS 21.0. RESULTS: Both iRoot FS and Biodentine increased the adhesion of hPDLCs at day 3. Compared to Biodentine, iRoot FS positively increased hPDLC proliferation on days 3 (P = 0.03) and 7 (P = 0.00). Osteogenic marker ALP was observed consistently in all samples, with iRoot FS having significantly higher ALP activity at day 14 (P = 0.00). Compared with Biodentine, iRoot FS significantly increased the mRNA level of ALP, COL1 and Runx2, and OCN increased only on day 14 (P < 0.05). CONCLUSIONS: iRoot FS had a positive effect on the adhesion, proliferation and biomineralization of hPDLCs compared with Biodentine.
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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) | 0.002 | 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".