Biologically inspired rosette nanotubes and nanocrystalline hydroxyapatite hydrogel nanocomposites as improved bone substitutes
Bibliographic record
Abstract
Today, bone diseases such as bone fractures, osteoporosis and bone cancer represent a common and significant public health problem. The design of biomimetic bone tissue engineering materials that could restore and improve damaged bone tissues provides exciting opportunities to solve the numerous problems associated with traditional orthopedic implants. Therefore, the objective of this in vitro study was to create a biomimetic orthopedic hydrogel nanocomposite based on the self-assembly properties of helical rosette nanotubes (HRNs), the osteoconductive properties of nanocrystalline hydroxyapatite (HA), and the biocompatible properties of hydrogels (specifically, poly(2-hydroxyethyl methacrylate), pHEMA). HRNs are self-assembled nanomaterials that are formed from synthetic DNA base analogs in water to mimic the helical nanostructure of collagen in bone. In this study, different geometries of nanocrystalline HA were controlled by either hydrothermal or sintering methods. 2 and 10 wt% nanocrystalline HA particles were well dispersed into HRN hydrogels using ultrasonication. The nanocrystalline HA and nanocrystalline HA/HRN hydrogels were characterized by x-ray diffraction, transmission electron microscopy, and scanning electron microscopy. Mechanical testing studies revealed that the well dispersed nanocrystalline HA in HRN hydrogels possessed improved mechanical properties compared to hydrogel controls. In addition, the results of this study provided the first evidence that the combination of either 2 or 10 wt% nanocrystalline HA and 0.01 mg ml(-1) HRNs in hydrogels greatly increased osteoblast (bone-forming cell) adhesion up to 236% compared to hydrogel controls. Moreover, this study showed that HRNs stimulated HA nucleation and mineralization along their main axis in a way that is very reminiscent of the HA/collagen assembly pattern in natural bone. In summary, the presently observed excellent properties of the biomimetic nanocrystalline HA/HRN hydrogel composites make them promising candidates for further study for bone tissue engineering applications.
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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".