Bioinspired mineralization of a functionalized injectable dense collagen hydrogel through silk sericin incorporation
Bibliographic record
Abstract
Collagen based hydrogels are frequently used as templates to mimic the native biomineralization process. However, a lack of structural control and their inherently poor mineralization capability represent challenges when used as bone-extracellular-matrix mimicking constructs. The aspiration-ejection of highly-hydrated collagen gels allows for their densification and fibrillar remodelling, leading to the production of injectable dense collagen (I-DC) gel scaffolds characterized by an osteoid-like structure. In this study, silk-extracted sericin (SS), a negatively-charged protein that is rich in anionic amino-acids such as Asp and Glu, was hybridized into I-DC gels to induce hydroxyapatite deposition and stimulate the osteoblastic differentiation of seeded mesenchymal stem cells (MSCs). The effect of SS content on the acellular mineralization of I-DC gels in simulated body fluid (SBF) and on modulating the proliferation and osteogenesis of seeded MSCs, in vitro, were investigated. Methylene blue staining indicated increasingly negatively charged gels through SS incorporation. Attributable to the carboxyl groups provided by the acidic SS amino-acids, serving as calcium-phosphate nucleation sites, there was a time dependent increase in hydroxyapatite deposition, approaching 90 wt% by day 14 in SBF. Three dimensionally seeded MSCs attached and proliferated in all gel types and SS-incorporation led to an increase in their metabolic activity. Relative to neat I-DC gels, alkaline phosphatase (at day 7), runt related transcription factor 2 (at day 21) and osteocalcin (at days 14 and 21) expression was higher in MSCs when seeded in SS-incorporated I-DC gels. Cell-induced mineralization was accelerated in SS-incorporated I-DC gels suggesting its osteostimulative potential. In sum, SS incorporation into clinically relevant I-DC gels can provide a strategy to design scaffolds with potential applications in bone tissue engineering.
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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".