The Effects of Electrical Stimulation on a 3D Osteoblast Cell Model
Bibliographic record
Abstract
Electrical stimulation has been used with tissue engineering-based models to develop three-dimensional (3D), dynamic, research models that are more physiologically relevant than static two-dimensional (2D) cultures. For bone tissue, the effect of electrical stimulation has focused on promoting healing and regeneration of tissue to prevent bone loss. However, electrical stimulation can also potentially affect mature bone parenchymal cells such as osteoblasts to guide bone formation and the secretion of paracrine or endocrine factors. Due to a lack of physiologically relevant models, these phenomena have not been studied in detail. In vitro electrical stimulation models can be useful for gaining an understanding of bone physiology and its effects on paracrine tissues under different physiological and pathological conditions. Here, we use a 3D, dynamic, in vitro model of bone to study the effects of electrical stimulation conditions on protein and gene expression of SaOS-2 human osteosarcoma osteoblast-like cells. We show that different stimulation regimens, including different frequencies, exposure times, and stimulation patterns, can have different effects on the expression and secretion of the osteoblastic markers alkaline phosphatase and osteocalcin. These results reveal that electrical stimulation can potentially be used to guide osteoblast gene and protein expression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".