Hydroxyapatite bioactivated bacterial cellulose promotes osteoblast growth and the formation of bone nodules
Bibliographic record
Abstract
The goal of this study was to investigate the feasibility of bacterial cellulose (BC) scaffold to support osteoblast growth and bone formation. BC was produced by culturing Acetobacter xylinum supplemented with hydroxyapatite (HA) to form BC membranes (without HA) and BC/HA membranes. Membranes were subjected to X-ray photoelectron spectroscopy (XPS) analysis to determine surface element composition. The membranes were further used to evaluate osteoblast growth, alkaline phosphatase activity and bone nodule formation. BC was free of calcium and phosphate. However, XPS analysis revealed the presence of both calcium (10%) and phosphate (10%) at the surface of the BC/HA membrane. Osteoblast culture showed that BC alone was non-toxic and could sustain osteoblast adhesion. Furthermore, osteoblast adhesion and growth were significantly (p ≤0.05) increased on BC/HA membranes as compared to BC alone. Both BC and BC/HA membranes improved osteoconductivity, as confirmed by the level of alkaline phosphatase (ALP) activity that increased from 2.5 mM with BC alone to 5.3 mM with BC/HA. BC/HA membranes also showed greater nodule formation and mineralization than the BC membrane alone. This was confirmed by Alizarin red staining (ARS) and energy dispersive X-ray spectroscopy (EDX). This work demonstrates that both BC and BC/HA may be useful in bone tissue engineering.
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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".