Effect of Cu2+ ion on Biological Performance of Nanostructured Fluorapatite Doped with Copper
Bibliographic record
Abstract
Nanostructured copper-doped fluorapatite (Cux.Ca(10-x).(PO4)6.F2) having crystallite sizes of 19, 29 and 34 nm at x = 0.9, 0.4 and 0.0, respectively, was synthesized by planetary ball milling of CaO, P2O5, CaF2 and CuO powders. Specifications of the products were determined by Fourier transform infrared spectroscopy, field emission scanning electron microscopy, transmission electron microscopy and X-ray diffraction analysis. In-vitro studies and Mossman's Tetrazole Test (MTT) assays were also conducted by incubating Cux.Ca(10-x).(PO4)6.F2 powder into Kokubo’s simulated body fluid (SBF) and against BT-20 cell, respectively, to determine bioactivity and biocompatibility of the material. Antibacterial effects toward Staphylococcus aureus was assessed by the disc diffusion test method. Measurements showed that the rate of formation of fluorapatite was lowered by Cu content. Besides, in vitro experiments showed the same SBF interacted apatite precipitation for all samples. In contrast, MTT assays revealed different behavior for pure fluorapatite and apatite with x=0.9 Cu against BT-20 cell after 24 h of incubation. This highlights increase of fluorapatite cytotoxicity when Cu ion is present in the apatite structure. Copper-doped fluorapatite was, however, desirably antibacterial. This stemmed from copper ions interactions with the bacterial metabolism which resulted in enzymes neutralization and copper-doped fluorapatite antibacterial behavior
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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) | 0.001 | 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".