Bioactive Rosette Nanotube–Hydroxyapatite Nanocomposites Improve Osteoblast Functions
Bibliographic record
Abstract
Inspired from biological systems, small synthetic organic molecules expressing the hydrogen bonding arrays of the DNA bases guanine and cytosine were prepared, and their self-assembly into rosette nanotubes (RNTs) was investigated. Due to their unique biological, physicochemical, and mechanical properties, RNTs could serve as the next generation of injectable orthopedic materials. In this study, a self-assembling module (termed twin base linkers or TBL) was synthesized, and the corresponding RNTs were used as bioactive components in composites of poly (2-hydroxyethyl methacrylate) (pHEMA) and hydroxyapatite (HA) nanoparticles (termed TBL/HA/pHEMA). The properties of these composites were characterized for solidification time, surface morphology, mechanical properties, and cytocompatibility. The experimental conditions were optimized to achieve solidification within 2-40 min, offering a range of properties for orthopedic applications. Composites with 20 wt% HA nanoparticles had a compressive strength (37.1 MPa) and an ultimate tensile stress (14.7 MPa) similar to that of a natural vertebral disc (5-30 MPa). Specifically, the TBL (0.01 mg/mL)/HA(20 wt%)/pHEMA composites improved long-term functions of osteoblasts (or bone-forming cells) in terms of collagen synthesis, alkaline phosphatase activity, and calcium deposition. Moreover, this composite inhibited fibroblast adhesion, thus decreasing the potential for undesirable fibrous tissue formation. In summary, this in vitro study provided evidence that TBL/HA/pHEMA composites are promising injectable orthopedic implant materials that warrant further mechanistic and in vivo studies.
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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) | 0.000 | 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".