Effect of bioactive Biosilicate<sup>®</sup>/F18 glass scaffolds on osteogenic differentiation of human adipose stem cells
Bibliographic record
Abstract
Abstract This study evaluated the gene expression profile of the human adipose‐derived stem cells (hASCs) grown on the Biosilicate®/F18 glass (BioS‐2P/F18) scaffolds. hASCs were cultured using the osteogenic medium (control), the scaffolds, and their ionic extract. We observed that ALP activity was higher in hASCs grown on the BioS‐2P/F18 scaffolds than in hASCs cultured with the ionic extract or the osteogenic medium on day 14. Moreover, the dissolution product group and the control exhibited deposited calcium, which peaked on day 21. Gene expression profiles of cell cultured using the BioS‐2P/F18 scaffolds and their extract were evaluated in vitro using the RT2 Profiler polymerase chain reaction (PCR) microarray on day 21. Mineralizing tissue‐associated proteins, differentiation factors, and extracellular matrix enzyme expressions were measured using quantitative PCR. The gene expression of different proteins involved in osteoblast differentiation was significantly up‐regulated in hASCs grown on the scaffolds, especially BMP1, BMP2, SPP1, BMPR1B, ITGA1, ITGA2, ITGB1, SMAD1, and SMAD2, showing that both the composition and topographic features of the biomaterial could stimulate osteogenesis. This study demonstrated that gene expression of hASCs grown on the scaffold surface showed significantly increased gene expression related to hASCs cultured with the ionic extract or the osteogenic medium, evidencing that the BioS‐2P/F18 scaffolds have a substantial effect on cellular behavior of hASCs.
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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) | 0.001 | 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".