<i>Fructus Ligustri Lucidi</i> preserves bone quality through induction of canonical Wnt/β‐catenin signaling pathway in ovariectomized rats
Bibliographic record
Abstract
Fructus Ligustri Lucidi (FLL) has been preclinically and clinically used to treat musculoskeletal diseases. However, whether and how FLL affect the canonical Wnt/β-catenin signaling in the management of osteoporosis remains largely unknown. To this end, ovariectomized (OVX) rats and primary osteoblasts were administrated with FLL aqueous extract and medicated serum, respectively. Supplement of FLL to OVX rats maintains bone quality by attenuating the reduction in bone mineral density, strength and microstructure. The maintenance may be associated with upregulating the expression of insulin-like growth factor-1, osteoprotegerin, phospho (p)-low-density lipoprotein receptor-related protein 6, p-glycogen synthase kinase 3 beta (GSK3β), β-catenin, Runx2 and c-Myc, and downregulating the expressions of sclerostin (SOST), dickkopf-related protein 1 (DKK1), GSK3β and p-β-catenin in rat femurs and tibias. In addition, the medicated serum promotes osteoblastic bone formation through activation of Wnt/β-catenin signaling via inhibition of DKK1 and SOST overexpression. Salidroside may be one of the active ingredients in FLL that are beneficial for bone homeostasis. In summary, our results suggest that FLL may preserve bone quality through induction of canonical Wnt/β-catenin signaling via inhibition of DKK1 and SOST overexpression. And FLL may offer a new source of the DKK1 or SOST inhibitors in protection against osteoporosis.
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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) | 0.001 | 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".