A Review on the Molecular Mechanisms of Action of Natural Products in Preventing Bone Diseases
Bibliographic record
Abstract
The drugs used for treating bone diseases (BDs), at present, elicit hazardous side effects that include certain types of cancers and strokes, hence the ongoing quest for the discovery of alternatives with little or no side effects. Natural products (NPs), mainly of plant origin, have shown compelling promise in the treatments of BDs, with little or no side effects. However, the paucity in knowledge of the mechanisms behind their activities on bone remodeling has remained a hindrance to NPs’ adoption. This review discusses the pathological development of some BDs, the NP-targeted components, and the actions exerted on bone remodeling signaling pathways (e.g., Receptor Activator of Nuclear Factor κ B-ligand (RANKL)/monocyte/macrophage colony-stimulating factor (M-CSF)/osteoprotegerin (OPG), mitogen-activated protein kinase (MAPK)s/c-Jun N-terminal kinase (JNK)/nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), Kelch-like ECH-associated protein 1 (Keap-1)/nuclear factor erythroid 2–related factor 2 (Nrf2)/Heme Oxygenase-1 (HO-1), Bone Morphogenetic Protein 2 (BMP2)-Wnt/β-catenin, PhosphatidylInositol 3-Kinase (PI3K)/protein kinase B (Akt)/Glycogen Synthase Kinase 3 Beta (GSK3β), and other signaling pathways). Although majority of the studies on the osteoprotective properties of NPs against BDs were conducted ex vivo and mostly on animals, the use of NPs for treating human BDs and the prospects for future development remain promising.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".