Inhibition of T cell activation by the phytochemical piperine (50.35)
Bibliographic record
Abstract
Abstract Piperine, a major alkaloid of the fruits of black and long pepper plants, exhibits anti-inflammatory activity both in vitro and in vivo. Although the effect of piperine on T cell function is not yet known, we hypothesized that piperine might inhibit T cell processes involved in inflammation. We therefore examined the effect of piperine on T cell proliferation, activation marker expression, and cytokine production. Cellular proliferation was determined by tritiated-thymidine incorporation and Oregon Green® 488 staining while activation marker expression was assessed by flow cytometry. Cytokine production and intracellular signalling pathways were examined by ELISA and Western blot analysis, respectively. Piperine inhibited T cell proliferation in a dose-dependent manner without affecting T cell viability. Piperine also inhibited expression of the T cell activation markers CD25, CD69, and CTLA-4, as well as production of the cytokines IFN-γ, IL-2, IL-4, and IL-17. Phosphorylation of extracellular signal-regulated kinase (ERK) and inhibitor of κBα (IκBα) was diminished in T cells stimulated in the presence of piperine, indicating that piperine affects signalling pathways involved in T cell activation. Collectively, these data suggest that piperine warrants further investigation as a possible immunosuppressive agent for the treatment of T cell-mediated inflammation. Supported by NSERC and CIHR.
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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) | 0.003 | 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".