Naringenin inhibits human osteoclastogenesis and osteoclastic bone resorption
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Naringenin, a naturally occurring flavonoid, possesses a wide range of pharmacological properties. The purpose of this study was to investigate the effect of naringenin on human osteoclastogenesis and osteoclastic bone resorption. MATERIAL AND METHODS: Naringenin was tested in a human osteoclastogenesis model using primary osteoclast precursor cells activated by receptor activator of nuclear factor-kappaB ligand (RANKL) and macrophage colony-stimulating factor (M-CSF) for 6 days. Osteoclastogenesis was assessed by determining the number of tartrate-resistant acid phosphatase (TRAP)-stained multinuclear cells, while the secretion of factors involved in osteoclastogenesis was assessed using enzyme-linked immunosorbent assays. The effect of naringenin on bone resorption was investigated using an OsteoAssay human bone plate coupled with an immunoassay to evaluate the release of helical peptide 620-633 from the alpha1 chain of type I collagen. RESULTS: Naringenin was non-toxic at the highest concentration used (50 microg/ml). Naringenin (10, 25 and 50 microg/ml) significantly inhibited osteoclastogenesis (by 29 +/- 5, 57 +/- 8 and 96 +/- 1%, respectively). Naringenin also markedly inhibited the secretion of interleukin (IL)-1alpha (by 59%), IL-23 (by 87%) and monocyte chemoattractant protein-1 (by 58%). Lastly, naringenin (10, 25 and 50 microg/ml) significantly decreased the release of helical peptide 620-633, an indicator of bone resorption activity (by 44 +/- 0.5, 73 +/- 0.5 and 86 +/- 1%, respectively). CONCLUSIONS: Naringenin can inhibit human osteoclastogenesis and osteoclastic bone resorption. It thus holds promise as a therapeutic or preventive agent for bone-related diseases such as periodontitis.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".