Preparation of Fibrous Scaffolds Containing Calcium and Silicon Species
Bibliographic record
Abstract
Materials for bone defect filling should have 3D macroporous structure and be flexible to be packed into complex defects with limited entrance space. Tissue engineering scaffolds should also mimic the structure and morphology of the host tissue. Electrospinning is a versatile technique to produce materials with micro/nanofibrous structure, large surface area and high porosity. Electrospun materials are very promising for tissue engineering due to the possibility of mimicking the fibrous structure of natural extra cellular matrix (ECM). Siloxane-containing vaterite (SiV)/poly (L-lactic acid) (PLLA) hybrids (SiPVH) with controlled silicate and calcium ions releasing ability has been produced in our group. They have also demonstrated good cell infiltration into the electrospun hybrid materials that had fiber diameters greater than 10 μm. However, these electrospun hybrid materials were planar (2D) and are not suitable for large defect regeneration. In this work, the development of a fabrication technique for the production of 3D cotton wool-like structures with fiber diameter in the range of 10 μm was performed. SiPVH cotton wool-like structure containing 0, 30 and 60 wt % SiV were prepared by blowing air in the direction perpendicular to fiber spinning. Si-vaterite particles and small pores were found on the surface of the fibers. The fiber diameter of the samples were found to be in the range of 10 ~ 20 μm. Stretch tests showed more than 50 % extension for the SiPVH cotton wool-like material containing 30 wt % SiV (SiPVH30). This extension was similar to that observed for the PLLA cotton wool-like material. The results suggest that the SiPVH30 cotton wool-like material are good candidates for bone tissue engineering scaffolds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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; 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".