<i>Allium cepa</i>L. and Quercetin Inhibit RANKL/<i>Porphyromonas gingivalis</i>LPS-Induced Osteoclastogenesis by Downregulating NF-<i>κ</i>B Signaling Pathway
Post-publication record
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Bibliographic record
Abstract
Objectives. We evaluated the in vitro modulatory effects of Allium cepa L. extract (AcE) and quercetin (Qt) on osteoclastogenesis under inflammatory conditions (LPS-induced). Methods. RAW 264.7 cells were differentiated with 30 ng/mL of RANKL, costimulated with PgLPS (1 µg/mL), and treated with AcE (50-1000 µg/mL) or Qt (1.25, 2.5, or 5 µM). Cell viability was determined by alamarBlue and protein assays. Nuclei morphology was analysed by DAPI staining. TRAP assays were performed as follows: p-nitrophenyl phosphate was used to determine the acid phosphatase activity of the osteoclasts and TRAP staining was used to evaluate the number and size of TRAP-positive multinucleated osteoclast cells. Von Kossa staining was used to measure osteoclast resorptive activity. Cytokine levels were measured on osteoclast precursor cell culture supernatants. Using western blot analysis, p-IκBα and IκBα degradation, inhibitor of NF-kappaB, were evaluated. Results. Both AcE and Qt did not affect cell viability and significantly reduced osteoclastogenesis compared to control. We observed lower production of IL-6 and IL-1α and an increased production of IL-3 and IL-4. AcE and Qt downregulated NF-κB pathway. Conclusion. AcE and Qt may be inhibitors of osteoclastogenesis under inflammatory conditions (LPS-induced) via attenuation of RANKL/PgLPS-induced NF-κB activation.
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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".