Oxidative Nanopatterning of Titanium Surface Influences mRNA and MicroRNA Expression in Human Alveolar Bone Osteoblastic Cells
Bibliographic record
Abstract
Titanium implants have been extensively used in orthopedic and dental applications. It is well known that micro- and nanoscale surface features of biomaterials affect cellular events that control implant-host tissue interactions. To improve our understanding of how multiscale surface features affect cell behavior, we used microarrays to evaluate the transcriptional profile of osteoblastic cells from human alveolar bone cultured on engineered titanium surfaces, exhibiting the following topographies: nanotexture (N), nano+submicrotexture (NS), and rough microtexture (MR), obtained by modulating experimental parameters (temperature and solution composition) of a simple yet efficient chemical treatment with a H2SO4/H2O2 solution. Biochemical assays showed that cell culture proliferation augmented after 10 days, and cell viability increased gradually over 14 days. Among the treated surfaces, we observed an increase of alkaline phosphatase activity as a function of the surface texture, with higher activity shown by cells adhering onto nanotextured surfaces. Nevertheless, the rough microtexture group showed higher amounts of calcium than nanotextured group. Microarray data showed differential expression of 716 mRNAs and 32 microRNAs with functions associated with osteogenesis. Results suggest that oxidative nanopatterning of titanium surfaces induces changes in the metabolism of osteoblastic cells and contribute to the explanation of the mechanisms that control cell responses to micro- and nanoengineered surfaces.
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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".