Interaction between topography and coating in the formation of bone nodules in culture for hydroxyapatite- and titanium-coated micromachined surfaces
Bibliographic record
Abstract
Rat osteoblast cultures were maintained from 24 h to 6 weeks on hydroxyapatite (HA)- or titanium (Ti)-coated smooth and micromachined grooved substrata in medium supplemented with L-ascorbic acid-2-phosphate and beta-glycerophosphate to promote mineralization. The HA coatings, approximately 1 microm thick, were characterized using X-ray diffraction, surface roughness, and scanning electron microscopy (SEM). Osteoblasts elongated, aligned, and moved in the direction of the grooves on both Ti and HA grooved surfaces. HA surfaces produced significantly more bone-like nodules than Ti surfaces. All grooved substrata produced significantly more nodules than smooth surfaces. These results are consistent with the hypothesis that substrata can increase osteogenesis by formation of an appropriate microenvironment. There was also a statistically significant interaction between topography and chemistry in the formation of mineralized nodules. A strong correlation (r = 0.958) between alkaline phosphatase (Alk-P) at 2 weeks and nodule counts at 6 weeks was observed, suggesting that Alk-P may possibly be used as a leading indicator of osteogenesis on microfabricated surfaces. The results of this study indicate that surface topography and chemistry can affect osteogenesis, and that interactions between chemistry and topography can occur.
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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 | 0.001 |
| 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".