Application of multiple forms of mechanical loading to human osteoblasts reveals increased ATP release in response to fluid flow in 3D cultures and differential regulation of immediate early genes
Bibliographic record
Abstract
ATP is actively released into the extracellular environment from a variety of cell types in response to mechanical stimuli. This is particularly true in bone where mechanically induced ATP release leads to immediate early gene activation to regulate bone remodelling; however there is no consensus as to which mechanical stimuli stimulate osteoblasts the most. To elucidate which specific type(s) of mechanical stimuli induce ATP release and gene activation in human osteoblasts, we performed an array of experiments using different mechanical stimuli applied to both monolayer and 3D cultures of the same osteoblast cell type, SaOS-2. ATP release from osteoblasts cultured in monolayer significantly increased in response to turbulent fluid flow, laminar fluid flow and substrate strain. No significant change in ATP release could be detected in 3D osteoblast cultures in response to cyclic or static compressive loading of osteoblast-seeded scaffolds, whilst turbulent fluid flow increased ATP release from 3D cultures of osteoblasts to a greater degree than observed in monolayer cultures. Cox-2 expression quantified using real time PCR was significantly lower in cells subjected to turbulent fluid flow whereas c-fos expression was significantly higher in cells subjected to strain. Load-induced signalling via c-fos was further investigated using a SaOS-2 c-fos luciferase reporter cell line and increased in response to substrate strain and turbulent fluid flow in both monolayer and 3D, with no significant change in response to laminar fluid flow or 3D compressive loading. The results of this study demonstrate for the first time strain-induced ATP release from osteoblasts and that turbulent fluid flow in 3D up regulates the signals required for bone remodelling.
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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 | 0.001 |
| 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".