Influence of a Physiologically Formed Blood Clot on Pre-Osteoblastic Cells Grown on a BMP-7-Coated Nanoporous Titanium Surface
Bibliographic record
Abstract
Titanium (Ti) nanotopography modulates the osteogenic response to exogenous bone morphogenetic protein 7 (BMP-7) in vitro, supporting enhanced alkaline phosphatase mRNA expression and activity, as well as higher osteopontin (OPN) mRNA and protein levels. As the biological effects of OPN protein are modulated by its proteolytic cleavage by serum proteases, this in vitro study evaluated the effects on osteogenic cells in the presence of a physiological blood clot previously formed on a BMP-7-coated nanostructured Ti surface obtained by chemical etching (Nano-Ti). Pre-osteoblastic MC3T3-E1 cells were cultured during 5 days on recombinant mouse (rm) BMP-7-coated Nano-Ti after it was implanted in adult female C57BI/6 mouse dorsal dermal tissue for 18 h. Nano-Ti without blood clot or with blood clot at time 0 were used as the controls. The presence of blood clots tended to inhibit the expression of key osteoblast markers, except for Opn, and rmBMP-7 functionalization resulted in a tendency towards relatively greater osteoblastic differentiation, which was corroborated by runt-related transcription factor 2 (RUNX2) amounts. Undetectable levels of OPN and phosphorylated suppressor of mothers against decapentaplegic (SMAD) 1/5/9 were noted in these groups, and the cleaved form of OPN was only detected in the blood clot immediately prior to cell plating. In conclusion, the strategy to mimic in vitro the initial interfacial in vivo events by forming a blood clot on a Ti nanoporous surface resulted in the inhibition of pre-osteoblastic differentiation, which was minimally reverted with an rmBMP-7 coating.
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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".