Enhanced Osteoblast Adhesion on Self-Assembled Nanostructured Hydrogel Scaffolds
Bibliographic record
Abstract
The objective of the current in vitro study was to improve properties of a commonly used hydrogel for implant applications by incorporating novel self-assembled helical rosette nanotubes (HRNs). Since traditional methods (such as autografts and allografts) used to treat bone defects present various disadvantages (such as donor tissue shortage, extensive inflammation, possible disease transmission, and poor new bone growth), which may lead to implant failure, much effort has been devoted to creating a novel bone substitute that biomimics the nanoscale features of natural bone in order to improve bone growth. HRNs (formed by chemically immobilizing two DNA base pairs) are a novel type of soft nanomaterial that biomimics natural nanostructured components of bone (such as collagen) since they are 3.5 nm in diameter and self-assemble into a helical structure in aqueous solutions. Because HRNs undergo a phase transition from a liquid to a viscous gel when heated to slightly above body temperatures or when added directly to serum-supplemented or serum-free media at body temperatures, they may provide an exciting therapy to heal bone fractures in situ. In this study, HRN-K1 (HRNs functionalized with lysine amino acids) was embedded in and coated on a model hydrogel [specifically, poly(2-hydroxyethyl methacrylate) or pHEMA]. The results of this study showed, for the first time, enhanced osteoblast (bone-forming cell) adhesion on HRN-K1 embedded in and coated on hydrogels compared to hydrogels without HRN-K1. Moreover, the results showed that embedding HRN-K1 into hydrogels can greatly decrease the polymerization time of pHEMA (especially at low temperatures). The presence of lysine in HRN-K1/hydrogels was shown to be one, but not only, property of HRN-K1 that enhanced osteoblast adhesion. In summary, the present results demonstrated that HRNs can improve properties of one particular hydrogel (pHEMA) and, thus, should be further investigated as a bone-healing material.
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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".