Self-Assembled Rosette Nanotube/Hydrogel Composites for Cartilage Tissue Engineering
Bibliographic record
Abstract
Recently, hydrogels (alginate, agarose, polyethylene glycol, etc.) have been investigated as promising cartilage-healing materials. To further improve cell-material interactions or mechanical properties of such hydrogel scaffolds, many materials (such as ceramics or carbon nanotubes) have been added to produce composites with tailored properties. In this study, rosette nanotubes (RNTs, self-assembled nanotubes built from DNA base pairs), hydrogels, and cells (specifically, fibroblast-like type-B synoviocytes [SFB cells] and chondrocytes) were combined via a novel electrospinning technique to generate three-dimensional implantable scaffolds for cartilage repair. Importantly, results of this study showed that electrospun RNT/hydrogel composites improved both SFB cell and chondrocyte functions. RNT/hydrogel composites promoted SFB cell chondrogenic differentiation in 2 week culture experiments. Further, studies demonstrated that RNTs enhanced hydrogel adhesive strength to severed collagen. Results of this study thus provided a nanostructured scaffold that enhanced SFB cell adhesion, viability, and chondrogenic differentiation compared to nanosmooth hydrogels without RNTs. This study provided an alternative cartilage regenerative material derived from RNTs that could be directly electrospun into cartilage defects (with SFB cells and/or chondrocytes) to bond to severed collagen and promote cell adhesion, viability, and subsequent functions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".