Self-Assembled Rosette Nanotube/Hydrogel Composites for Cartilage Tissue Engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it