Novel biologically-inspired rosette nanotube PLLA scaffolds for improving human mesenchymal stem cell chondrogenic differentiation
Bibliographic record
Abstract
Cartilage defects are a persistent issue in orthopedic tissue engineering where acute and chronic tissue damage stemming from osteoarthritis, trauma, and sport injuries, present a common and serious clinical problem. Unlike bone, cartilage repair continues to be largely intractable due to the tissue's inherently poor regenerative capacity. Thus, the objective of this study is to design a novel tissue engineered nanostructured cartilage scaffold via biologically-inspired self-assembling rosette nanotubes (RNTs) and biocompatible non-woven poly (l-lactic acid) (PLLA) for enhanced human bone marrow mesenchymal stem cell (hMSC) chondrogenic differentiation. Specifically, RNTs are a new class of biomimetic supramolecular nanomaterial obtained through the self-assembly of low-molecular-weight modified guanine/cytosine DNA base hybrids (the G∧C motif) in an aqueous environment. In this study, we synthesized a novel twin G∧C-based RNT (TB-RGDSK) functionalized with cell-favorable arginine-glycine-aspartic acid-serine-lysine (RGDSK) integrin binding peptide and a twin G∧C based RNT with an aminobutane linker molecule (TBL). hMSC adhesion, proliferation and chondrogenic differentiation were evaluated in vitro in scaffold groups consisting of biocompatible PLLA with TBL, 1:9 TB-RGDSK:TBL, and TB-RGDSK, respectively. Our results show that RNTs can remarkably increase total glycosaminoglycan, collagen, and protein production when compared to PLLA controls without nanotubes. Furthermore, the TB-RGDSK with 100% well-organized RGDSK peptides achieved the highest chondrogenic differentiation of hMSCs. The current in vitro study illustrated that RNT nanotopography and surface chemistry played an important role in enhancing hMSC chondrogenic differentiation thus making them promising for cartilage regeneration.
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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".