Involvement of circRNA_0007059 in the regulation of postmenopausal osteoporosis by promoting the microRNA‐378/BMP‐2 axis
Bibliographic record
Abstract
Increasing evidence suggests that postmenopausal osteoporosis (PMO), a severe disturbance, imposes heavy physical, psychosocial, and financial burdens and dramatically influences the quality of life of postmenopausal women. Circular RNAs (circRNAs) and microRNAs (miRs) play important roles in the occurrence and development of PMO. However, the roles of circRNAs and miRs in osteoporosis regulation still need to be further investigated. circRNAs with different expression levels in patients with PMO were screened via RNA-seq and bioinformatics analysis. We found that circ_0007059 was upregulated in patients with PMO and during osteoclastogenesis of human bone marrow stromal cells (hBMSCs). Next, we investigated the effect of circ_0007059 overexpression during osteoclastogenesis of hBMSCs. circ_0007059 overexpression attenuated hBMSC differentiation into osteoclasts in vitro. This was demonstrated by downregulated bone morphogenetic protein 2 (BMP-2) expression, upregulated osteoclast-specific gene expression, and TRAP staining. circ_0007059 was demonstrated to directly target miR-378, which in turn targeted BMP-2 via bioinformatics analysis and the dual-luciferase reporter assay. Transfection of the miR-378 mimic reversed the effect of circ_0007059 on the osteoclastogenesis of hBMSCs. These results suggest that circ_0007059 plays an important role in osteoclastogenesis via the miR-378/BMP-2 signaling pathway. Targeting the circ_0007059/miR-378/BMP-2 axis is possibly a novel idea in osteoporosis treatment.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".