Functionalization of silk fibroin through anionic fibroin derived polypeptides
Bibliographic record
Abstract
While silk fibroin (SF)-based fibrous matrices are often considered as templates to mimic the native biomineralization process, their limited ability to induce apatite deposition hinders their potential applications in bone tissue engineering. In this study, it was hypothesized that the incorporation of anionic fibroin derived polypeptides (Cs), generated through the α-chymotrypsin digestion of SF, into SF would induce apatite deposition. The effect of Cs incorporation and content on the mineralization of fibrous, electrospun (ES) SF matrices, was assessed in simulated body fluid (SBF). Moreover, the potential role of Cs in mediating the proliferation and osteoblastic differentiation of seeded mesenchymal stem cells (MSCs), in vitro, was also investigated. Methylene blue staining indicated that the ES SF matrices became increasingly negatively charged with an increase in Cs content. Furthermore, the mechanical properties of the ES SF matrices were modulated through variations in Cs content. Their subsequent immersion in SBF demonstrated rapid mineralization, attributable to the carboxyl groups provided by the negatively charged Cs polypeptides, which served as nucleation sites for apatite deposition. Seeded MSCs attached on all scaffold types with differences observed in metabolic activities when cultured in osteogenic medium. Relative to basal medium, there was an up-regulation of alkaline phosphatase, runt related transcription factor 2 and osteocalcin in osteogenic medium (at days 14 and 21). Cell-induced mineralized matrix deposition appeared to be accelerated on Cs incorporated ES SF suggesting an osteoinductive potential of these polypeptides. In sum, the ability to incorporate Cs into SF scaffolds offers promise in bone tissue engineering applications.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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; both teacher heads agree on what is shown here.
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".