Bioactivity of novel carboxymethyl chitosan scaffold incorporating MTA in a tooth model
Bibliographic record
Abstract
AIM: To characterise the bioactivity of a novel carboxymethyl chitosan (CMCS) scaffold with and without incorporating mineral trioxide aggregate (MTA) in a tooth model. METHODOLOGY: Cross-linked CMCS scaffold (CaC) and MTA-coated CaC (CaMT) scaffold were prepared by freeze-drying. The bioactivity of the scaffolds was tested in vitro in four different mineralisation solutions (bulk system) and ex vivo in simulated body fluid (SBF) in the tooth model. After mineralisation, the mineral deposits on the scaffolds were analysed using scanning electron microscopy, energy dispersive X-ray, and inductively coupled plasma mass spectroscopy. All data were statistically analysed using the two-sample t-test (P < 0.05). RESULTS: Hydroxyapatite (HAP) deposition was observed on CaC and CaMT scaffolds after 1 week of mineralisation in the tooth model and in the bulk system. The deposition was significantly higher (P < 0.05) on CaMT scaffold than that on CaC scaffold. The amount of HAP formed in the tooth model was significantly lower (P < 0.05) than that in the bulk solution. CONCLUSIONS: The CMCS scaffolds are bioactive and capable of biomineralisation by forming HAP within a tooth model ex vivo. The bioactivity of the CMCS scaffold can be enhanced by incorporating MTA.
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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 | 0.001 | 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".