Inhibition of Rac and ROCK Signalling Influence Osteoblast Adhesion, Differentiation and Mineralization on Titanium Topographies
Bibliographic record
Abstract
Reducing the time required for initial integration of bone-contacting implants with host tissues would be of great clinical significance. Changes in osteoblast adhesion formation and reorganization of the F-actin cytoskeleton in response to altered topography are known to be upstream of osteoblast differentiation, and these processes are regulated by the Rho GTPases. Rac and RhoA (through Rho Kinase (ROCK)). Using pharmacological inhibitors, we tested how inhibition of Rac and ROCK influenced osteoblast adhesion, differentiation and mineralization on PT (Pre-treated) and SLA (sandblasted large grit, acid etched) topographies. Inhibition of ROCK, but not Rac, significantly reduced adhesion number and size on PT, with adhesion size consistent with focal complexes. After 1 day, ROCK, but not Rac inhibition increased osteocalcin mRNA levels on SLA and PT, with levels further increasing at 7 days post seeding. ROCK inhibition also significantly increased bone sialoprotein expression at 7 days, but not BMP-2 levels. Rac inhibition significantly reduced BMP-2 mRNA levels. ROCK inhibition increased nuclear translocation of Runx2 independent of surface roughness. Mineralization of osteoblast cultures was greater on SLA than on PT, but was increased by ROCK inhibition and attenuated by Rac inhibition on both topographies. In conclusion, inhibition of ROCK signalling significantly increases osteoblast differentiation and biomineralization in a topographic dependent manner, and its pharmacological inhibition could represent a new therapeutic to speed bone formation around implanted metals and in regenerative medicine applications.
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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".