Effects of Anthocyanins and Other Phenolic Compounds on the Production of Tumor Necrosis Factor α in LPS/IFN-γ-Activated RAW 264.7 Macrophages
Bibliographic record
Abstract
Flavonoids have been reported to demonstrate their benefits in lowering oxidative stress and beneficial effects on cardiovascular and chronic inflammatory diseases. Common phenolic compounds, including phenolic acids, flavonols, isoflavones, and anthocyanins, present in fruits, vegetables, and grains were investigated for their effects on the production of tumor necrosis factor alpha (TNF-alpha) in LPS/IFN-gamma-activated RAW 264.7 macrophages. Gallic acid and (+)-catechin showed small but significant effects, whereas chlorogenic acid had no effect on TNF-alpha production. The flavonol quercetin inhibited TNF-alpha production, but kaempferol and myricetin induced the secretion of TNF-alpha. The isoflavone genistein was an inhibitor of TNF-alpha, whereas daidzein induced TNF-alpha production. Glycosylation of genistein changed its inhibitory effects to TNF-alpha induction, and glycosylation of daidzein had no effect on its activity. Anthocyanidins/anthocyanins and anthocyanin-rich extracts induced TNF-alpha production and acted as modulators of the immune response in activated macrophages. This is the first study to report the effects of anthocyanins and berry extracts on TNF-alpha production.
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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".