Duloxetine suppresses BMP-4-induced release of osteoprotegerin via inhibition of the SMAD signaling pathway in osteoblasts
Bibliographic record
Abstract
Duloxetine, a selective serotonin-norepinephrine reuptake inhibitor, is currently recommended for the treatment of chronic painful disorders such as fibromyalgia, chronic musculoskeletal pain, and diabetic peripheral neuropathy. We previously demonstrated that bone morphogenetic protein-4 (BMP-4) stimulates osteoprotegerin (OPG) production in osteoblast-like MC3T3-E1 cells, and that p70 S6 kinase positively regulates OPG synthesis. The present study aimed to investigate the effect of duloxetine on BMP-4-stimulated OPG synthesis in these cells. Duloxetine dose-dependently suppressed OPG release stimulated by BMP-4. Fluvoxamine, a selective serotonin reuptake inhibitor (SSRI), reduced BMP-4-stimulated OPG release, whereas a selective and specific norepinephrine reuptake inhibitor, reboxetine, failed to affect OPG release. In addition, another SSRI sertraline also inhibited BMP-4-stimulated OPG release. On the other hand, siRNA of SMAD1 reduced the OPG release stimulated by BMP-4, indicating the involvement of the SMAD1/5/8 pathway in OPG release. Rapamycin inhibited BMP-4-stimulated p70 S6 kinase phosphorylation, and compound C suppressed the SMAD1/5/8 phosphorylation stimulated by BMP-4. Duloxetine did not affect BMP-4-induced phosphorylation of p70 S6 kinase but suppressed SMAD1/5/8 phosphorylation. Both fluvoxamine and sertraline also inhibited BMP-4-elicited phosphorylation of SMAD1/5/8. These results strongly suggest that duloxetine suppresses BMP-4-stimulated OPG release via inhibition of the Smad1/5/8 signaling pathway in osteoblasts.
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 | 0.000 | 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".