Osteoblast Behavior on Silicon and Porous-Silicon Substrates
Bibliographic record
Abstract
Osteoblast viability, proliferation, protein expression and mineralization were studied on bare, micro- and nanoporous silicon (Si) substrates. Micro- and nano-porous-Si substrates were prepared by anodic etching of silicon in ethanolic hydrofluoric acid and characterized using scanning electron and atomic force microscopies. Mouse osteoblasts were cultured on these substrates and cellular response to these surfaces was assessed using the Live/Dead Cell Viability assay and the MTT assay for cell proliferation. Osteoblast functionality was assessed using immunohistochemistry for bone protein specific markers. Osteoblasts grew well on micro- and nanoporous silicon substrates over the twenty-one day experimental period supporting the assessment that these are suitable cell supportive surfaces. Cell proliferation rates on bare and nanoporous silicon were similar initially, however, nanoporous silicon displayed enhanced cell proliferation, in comparison to bare silicon, after 14 days in culture. Immunocytochemical assays, using bone specific markers, showed positive reactions for osteonectin and osteopontin expression on all substrates with staining intensity increasing over the 21-day experimental period. Calcium mineral deposits were quantified using the Alizarin Red histochemical assay and nanoporous silicon induced the highest level of calcium mineral production in comparison to bare and microporous silicon. The data supports the potential use of nanoporous silicon as a surface implant coating for dental and orthopedic applications. The ability to dope (and then release) drugs or growth factors from the silicon nanopores offers the potential for a multi-functional implant surface.
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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".