Investigation of cytotoxicity and antibacterialeffect of boron-containing nano-hydroxyapatite
Bibliographic record
Abstract
Introduction: Different regenerative materials are used to protect and stimulate pulp tissue.Objectives: This in vitro study aimed to examine the cytotoxicity of different doses of boron-containing nanohydroxyapatite (B-nHAp) and its antibacterial effect.Material and methods: B-nHAp was synthesized with microwave-assisted chemical precipitation method at different rates, such as 1%, 5%, and 10%.The extracts were prepared in separate tubes: 1%, 5%, 10% B-nHAp, and nHAp 50 mg/ml.Cytotoxicity test was performed on L929 mouse fibroblasts for 24 and 72 hours.Antibacterial analysis was performed with Streptococcus mutans bacteria using micro-dilution method.Cell viability values were evaluated with two-way analysis of variance (ANOVA) test (p < 0.05).Results: B-nHAp was successfully synthesized in XRD and SEM analyses.Nano-hydroxyapatite showed more cell viability than control group at 24 and 72 hours.All B-nHAp extracts demonstrated less cell viability at the end of 24 hrs.than control group.However, 1% B-nHAp cell viability decreased more in the B-nHAp group compared with the control group, while 5% and 10% B-nHAp showed high cell viability after 72 hours of incubation.B-nHAp demonstrated less antibacterial effect on Streptococcus mutans compared with nHAp.Conclusions: According to the results, the addition of boron to nano-hydroxyapatite does not make a significant contribution to increase the regenerative and antibacterial properties of the material.
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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 | 0.001 |
| 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 | 0.000 | 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".