Focal adhesion mediated intracellular signaling, Stat3 translocation and osteoblast differentiation: regulation by substratum topography
Bibliographic record
Abstract
Abstract Osseointegration is a necessary process for stabilization of implants that contact bone. Implant substratum topography has been identified as an important modulator of osteoblast differentiation, although the molecular processes involved are poorly understood. The aim of this study was to assess adhesion mediated molecular events induced in rat calvarial osteoblasts by topographies produced using microfabrication techniques. Specifically, we investigated the activation of tyrosine phosphorylation, focal adhesion kinase (FAK), extracellular regulated kinase 1/2 (ERK‐1/2), janus kinase‐1 and 2 (JAK‐1 and 2), and the transcription factor Stat3. Microfabricated topographies stimulated altered focal adhesion (FA) arrangements, which correlated with regions of increased tyrosine phosphorylation. FAK, and ERK 1/2. Inhibition of JAK‐1 using piceatannol attenuated the phosphorylation of FAK and ERK 1/2 on 30μm deep grooves, but not smooth, but inhibited proliferation on all surfaces tested. Inhibition of microtubule nucleation, JAK‐1, JAK‐2 and phospholipase‐C had no effect on nuclear translocation of Stat3 irrespective of topography. We conclude that nuclear translocation of Stat3 is independent of substratum topography, but JAK‐1 is involved in focal adhesion mediated signal transduction. Further understanding of the molecular regulation of osteoblast differentiation by substratum topography will allow the design of more suitable biomaterials for orthopaedic and dental 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".