Investigation to test potential stereolithography materials for development of an <i>in vitro</i> root canal model
Bibliographic record
Abstract
The aims were to compare the physico-chemical properties (zeta-potential, wettability, surface free energy) of stereolithography materials (STL) (Photopolymer, Accura) to dentine and to evaluate the potential of each material to develop Enterococcus faecalis biofilm on their respective surfaces. Eighteen samples of each test material (Photopolymer, Accura, dentine) were employed (total n = 54) and sectioned to 1 mm squares (5 mm x 5 mm) (n = 15) or ground into a powder to measure zeta-potential (n = 3). The zeta-potential of the powder was measured using the Nano-Zetasizer technique. The contact angle (wettability, surface free energy tests) were measured on nine samples using goniometer. The biofilm attachment onto the substrate was assessed on the samples of each material using microscope and image processing software. The data were compared using one-way ANOVA with Dunnett post-hoc tests at a level of significance P ≤ 0.05. Both STL materials showed similar physico-chemical properties to dentine. The materials and dentine had negative charge (Accura: -23.7 mv, Photopolymer: -18.8 mv, dentine: -9.11 mv). The wettability test showed that all test materials were hydrophilic with a contact angle of 47.5°, 39.8°, 36.1° for Accura, Photopolymer and dentine respectively, and a surface free energy of 46.6, 57.7, 59.6 mN/m for Accura, Photopolymer and dentine, respectively. The materials and dentine proved suitable for attachment and growth of E. faecalis biofilm with no statistical differences (P > 0.05). Stereolithography materials show similar physico-chemical properties and growth of E. faecalis biofilm to dentine. Therefore, they may be an alternative to tests requiring dentine.
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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 | 0.001 |
| 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 | 0.001 | 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".