Resveratrol attenuates stimulated T-cell activation and proliferation: potential therapy against cellular rejection in organ transplantation.
Bibliographic record
Abstract
BACKGROUND: Pharmaceuticals to inhibit mammalian target of rapamycin (mTOR) protein, which plays an integral role in T cell survival and function, have been used to prevent complications associated with organ transplantation. Although studies have individually shown that resveratrol can inhibit mTOR and that inhibiting mTOR leads to attenuated immune function, no studies to date have examined these two functions conjointly under one study. Therefore, we hypothesize that resveratrol will decrease mTOR activation and expression as well as attenuate stimulated T cell activation and proliferation in peripheral blood mononuclear cells (PBMC). METHODS AND MATERIALS: Human PBMC were isolated and cultured. The cells were pre-treated with resveratrol (50 μM) overnight (18 hrs) before stimulation. The cells were collected for subsequent biochemical analysis after 1, 3, and 5 days. Additionally, the cells were stained with proliferation dye and cultured for 24 hours in PMA/Ionomycin with resveratrol for flow cytometry analysis. RESULTS: Resveratrol treated stimulated PBMCs displayed a significant decrease in activated phosphorylation of mTOR at days 1, 3, and 5 (P < 0.0329). Markers of T cell activation, tumour necrosis factor-alpha (TNF-α) and interferon-gamma (INF-γ), were also significantly reduced along with T cell proliferation following stimulated PBMC resveratrol treatment when compared to vehicle-treated controls (P < 0.01). CONCLUSION: Taken together, our data suggest that resveratrol can decrease the immune response of stimulated T-cells and inhibit the expression and activation of mTOR mediated cellular signalling under the same study setting. Therefore, resveratrol proposes a possible adjunctive therapy option for patients undergoing organ transplantation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".