Session III – Young Scientists Forum (K2, V11-V25)
Bibliographic record
Abstract
Osteoconductive biomaterials such as calcium phosphate bioceramics have been widely used as scaffolds for bone tissue engineering.However, this kind of materials usually lacks osteoinductivity and is not able to stimulate osteogenic differentiation of stem cells.In addition, angiogenesis plays an important role in tissue regeneration and tissue engineering, and insufficient angiogenesis may result in failure of regeneration of large sized bone defect or reconstruction of bone tissue by tissue engineering approach.Therefore, it is meaningful to develop biomaterials which can enhance both osteogenesis and angiogenesis simultaneously.Previsous studies have shown that the chemical composition and nano-structure are two factors which could affect cell behavior and bone regeneration.In our recent studies, we have designed and fabricated calcium phosphate and silicate based bioactive ceramics and composites, and found that some silicate based bioceramics have the potential to stimulate osteogenesis, and this effect is dependent on the chemical composition of the materials.Furthermore, some of the silicate bioceramics even showed the activity to stimulate angiogenesis in vitro and in vivo.In addition, our studies also showed that the surface nano/micro-structure also affected osteogenesis and angiogenesis.Our results suggest that biomaterials with certain chemical composition and surface structure may be used to design bioactive scaffolds for tissue engineering applications.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.510 | 0.424 |
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".