Inhibitory effects of green tea and a constituent of green tea, epigallocatechin‐3‐gallate (EGCG) on nitric oxide production in macrophages
Bibliographic record
Abstract
Green tea has been widely consumed as a healthful beverage and its active constituent, EGCG, is a potent antioxidant and anti‐inflammatory agent. This study aimed to elucidate the effects of green tea or EGCG (0.39, 0.78, 1.56, 3.13, 6.25, 12.5, 25, or 50μg/mL) on nitric oxide production in murine macrophage cells following lipopolysaccharide (LPS) challenge. Different concentrations of green tea in media (0.15, 0.3, 0.63, 1.25, 2.5, 5, 10, or 20%) were prepared from a stock (2.12 g of green tea leaves brewed in 240mL of H 2 O). RAW 264.7 murine macrophages were cultured in DMEM supplemented with 10% fetal bovine serum and 1% penicillin. Following a 24‐hour treatment with green tea or EGCG, cells were challenged with LPS (500ng/mL) for the next 24 hours and cell viability (resazurin assay) and nitrite concentrations (Griess reaction) were measured. In comparison to the control (LPS), 10% and 20% green tea caused a significant reduction in nitrite levels (49.7±5.1, 27.3±4.3, & 13.1±2.3 μg/mL, respectively; P ≤ 0.05). In case of EGCG, significant inhibitions were noted at 25 & 50μg/mL, versus control (22.4±2.6, 3.4±0.64, & 47.7±4.93 μg/mL, respectively; P ≤ 0.05). Both green tea and EGCG at their highest concentrations (20% & 50μg/mL, respectively) affected cell viability versus controls, whereas, no differences were noted among the other groups. Thus, green tea and EGCG inhibit nitric oxide production at higher doses, which may explain their anti‐inflammatory effects, and warrants further research as a therapeutic strategy in clinical practice. This study was funded by CHES, OSU.
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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".