Naringenin has anti‐inflammatory properties in macrophage and <i>ex vivo</i> human whole‐blood models
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Periodontitis is a chronic inflammatory disease of bacterial etiology, affecting tooth-supporting tissues. The host inflammatory response to periodontopathogens, notably the high and continuous production of cytokines, is considered a major factor causing the local tissue destruction observed in periodontitis. The aim of the present study was to investigate the effect of naringenin, a major flavanone in grapefruits and tomatoes, on the lipopolysaccharide-induced pro-inflammatory cytokine production by host cells, using two different models. MATERIAL AND METHODS: The effect of naringenin was characterized using macrophages stimulated with the lipopolysaccharide of either Aggregatibacter actinomycetemcomitans or Escherichia coli and using whole blood stimulated with A. actinomycetemcomitans lipopolysaccharide, in the presence or absence of naringenin. Lipopolysaccharide-induced interleukin-1 beta, interleukin-6, interleukin-8 and tumor necrosis factor-alpha production by macrophages and whole-blood samples treated with naringenin were evaluated using an enzyme-linked immunosorbent assay. Changes in the phosphorylation states of macrophage kinases induced by A. actinomycetemcomitans lipopolysaccharide and naringenin were characterized by immunoblot screening. RESULTS: Our results clearly indicated that naringenin is a potent inhibitor of the pro-inflammatory cytokine response induced by lipopolysaccharide in both macrophages and in whole blood. Naringenin markedly inhibited the phosphorylation on serines 63 and 73 of Jun proto-oncogene-encoded AP-1 transcription factor in lipopolysaccharide-stimulated macrophages. CONCLUSION: The results from the present study suggest that naringenin holds promise as a therapeutic agent for treating inflammatory 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) | 0.001 | 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) | 0.002 | 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".