Mineralization of Bone Extracellular Matrix-like Scaffolds Fabricated as Silk Sericin-Functionalized Dense Collagen–Fibrin Hybrid Hydrogels
Bibliographic record
Abstract
The design of hydrogels that combine both the biochemical cues needed to direct seeded cellular functions and mineralization to provide the structural and mechanical properties approaching those of mineralized native bone extracellular matrix (ECM) represents a significant challenge in bone tissue engineering. While fibrous hydrogels constituting of collagen or fibrin (and their hybrids) can be considered as scaffolds that mimic to some degree native bone ECM, their insufficient mechanical properties limit their application. In the present study, an automated gel aspiration-ejection (automated GAE) method was used to generate collagen-fibrin hybrid gel scaffolds with micro-architectures and mechanical properties approaching those of native bone ECM. Moreover, the functionalization of these hybrid scaffolds with negatively charged silk sericin accelerated their mineralization under acellular conditions in simulated body fluid and modulated the proliferation and osteoblastic differentiation of seeded MC3T3-E1 pre-osteoblastic cells. In the latter case, alkaline phosphatase activity measurements indicated that the hybrid gel scaffolds with seeded cells showed accelerated osteoblastic differentiation, which in turn led to increased matrix mineralization. In summary, the design of dense collagen-fibrin hybrid gels through an automated GAE process can provide a route to tailoring specific biochemical and mechanical properties to different types of bone ECM-like scaffolds, and can provide a model to better understand cell-matrix interactions in vitro for bioengineering purposes.
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) | 0.000 | 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".