Production of ex vivo lipopolysaccharide-induced tumor necrosis factor-alpha, interleukin-1beta, and interleukin-6 is suppressed by trans-resveratrol in a concentration-dependent manner.
Bibliographic record
Abstract
Trans-resveratrol is a biologically active compound present in certain foods that has anti-inflammatory and anticancer properties. These beneficial effects are derived from both the immune system and cytokines. The purpose of this study was to determine the immunomodulatory effect of trans-resveratrol on the ex vivo production of inflammatory and anti-inflammatory cytokines stimulated by lipopolysaccharides (LPS). Trans-resveratrol (0, 0.01, 0.1, 1, and 10 microM) was added to blood samples from male Sprague-Dawley rats (n = 6) along with 100 U of LPS (Escherichia coli serotype, 055B5). The samples were then incubated for 4 h at 37 degrees C and centrifuged. Finally, concentrations of tumor necrosis factor-alpha (TNF-alpha), interleukin-1beta (IL-1beta), and IL-6 in the plasma were analyzed using an enzyme-linked immunosorbent assay (ELISA). The production of inflammatory (TNF-alpha and IL-1beta) and anti-inflammatory (IL-6) cytokines was suppressed by trans-resveratrol in a concentration-dependent manner. These results support the hypothesis that the immunomodulatory effect of trans-resveratrol plays an important role in disease conditions that involve an overproduction of inflammatory cytokines.
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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".