Immunosuppressive Effect of Silymarin on Mitogen‐Activated Protein Kinase Signalling Pathway: the Impact on T Cell Proliferation and Cytokine Production
Bibliographic record
Abstract
Silymarin, a polyphenolic flavonoid derived from milk thistle (Silybum marianum), is known to have anti-inflammatory, hepatoprotective and anticarcinogenic effects. In this study, the in vitro immunomodulatory effect of silymarin was investigated using human CD4+ T cells. Peripheral blood mononuclear cells (PBMC) from healthy individuals were activated with anti-CD3 (5 μg/ml) plus anti-CD28 (2 μg/ml) and treated with 10, 50 and 100 μM silymarin. Cells were incubated 72 hr for proliferation assay using MTT and for viability analysis using PI staining and flow cytometry. Naive CD4+ T cell was also isolated from PBMC, activated with PHA/anti-CD28 and treated with 100 μM silymarin for 72 hr. MAPKs' activity of cell lysate from activated naive CD4+ T cells was assessed using an ELISA-based MAPKinase activity kit, and Th1/Th2/Th17-related cytokine expression was determined by Multi-analyte ELISA array kit. Results indicated a significant inhibition in proliferation of activated PBMC after 48-hr incubation with 100 μM silymarin without causing cell death. Moreover, MAPKs' activity (ERK1/2 and P38) and Th1-related cytokines (IL-2, TNF-α, IFN-γ) were significantly reduced in silymarin-treated cells compared with control after 72 hr. This study shows that silymarin has the ability to inhibit T cell proliferation and pro-inflammatory cytokine secretion in vitro. Furthermore, silymarin is able to inhibit ERK1/2 and P38 pathway activation in T cells stimulated through TCR engagement, a property that is likely associated with its ability to inhibit T cell proliferation and cytokine secretion. Therefore, silymarin, as an immune-response modifier, might be a valuable drug in therapeutic situations in which immunosuppression is required.
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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".