Helical rosette nanotubes as a biomimetic tissue engineering scaffold material
Bibliographic record
Abstract
It is widely known that mimicking the nanometric features of natural tissues in biomaterials is very useful for improving cell adhesion, proliferation and differentiation. Helical rosette nanotubes (HRN) are one type of such organic nanomaterials which self-assemble when added to water. Through non-covalent interactions such as H-bonding, base stacking interactions and hydrophobic interactions, the building blocks of HRN when self-assembled form a stable nanotube with a hollow core 11 Å across. Since the chemical properties and nanometric structures of HRN are very similar to those of collagen and hydroxyapatite (the nanostructured constituent components in bone), it is anticipated that HRN would be more well-suited for orthopaedic applications compared to conventional implant materials such as titanium which do not mimic the nanometer features of bone. For this reason, the current study is focused on investigating HRN as a potential orthopaedic tissue engineering scaffold. Compared to uncoated titanium, in vitro studies clearly showed that osteoblasts (bone-forming cells) adhered more on specialized versions of HRN, specifically HRN functionalized with lysine (K) and arginine (Arg) when coated on titanium surfaces. This phenomenon may be attributed to the presence of amino acids side chains (such as arginine and lysine) as well as the biologically-inspired nanometric features that HRN form when coated on titanium. Moreover, HRN can undergo a phase transition from liquid to a viscous gel when heated to 60 °C or when added directly to serum-free media at body temperatures. Transmission electron microscopy (TEM) showed that a densely-packed nanotube network is formed in the viscous gel. Further in vitro studies including measuring osteoblast adhesion and subsequent functions when cultured in the viscous HRN tissue engineering scaffold will be presented. In this manner, this study introduces a new self-assembled nanomaterial, HRN, that is showing promising in various orthopaedic 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".