Modulation of Cytokine Production and Transcription Factors Activities in Human Jurkat T Cells by Thymol and Carvacrol
Bibliographic record
Abstract
PURPOSE: Thymol and carvacrol, two main components of thyme, have shown anti-inflammatory effects. The aim of this study was to assess the effects of these components on Jurkat leukemia cells as an in vitro T cell model and their molecular mechanisms of activity. METHODS: Cells were cultured in the presence of components and subsequently stimulated with phorbol-12-myristate-13-acetate (PMA)/calcium ionophore for evaluating interleukin (IL)-2 and interferon (IFN)-γ production. The activation of T cell transcription factors that included nuclear factors of activated T cells (NFATs), activator protein-1 (AP-1; c-Jun/c-Fos), and nuclear factor (NF)-κB were examined by Western blot analysis. RESULTS: Thymol and carvacrol at 25 µg/ml significantly reduced IL-2 levels from 119.4 ± 8pg/ml in control cells treated only with PMA/Calcium ionophore and the solvent to 66.9 ± 6.4pg/ml (thymol) and 32.3 ± 3.6pg/ml (carvacrol) and IFN-γ from 423.7 ± 19.7pg/ml in control cells to 311.9 ± 11.6pg/ml (thymol) and 293.5 ± 16.7pg/ml (carvacrol). Western blot analyses of nuclear extracts showed that the same concentrations of components significantly reduced NFAT-2 to 44.2 ± 2.7% (thymol) and 91.4 ± 2.3% (carvacrol) of the control (p<0.05), and c-Fos to 31.2 ± 6.2% (thymol) and 27.6 ± 3.1% (carvacrol) of the control (p<0.01). No effects on NFAT-1, c-Jun and phospho-NF-κBp65 levels were observed. CONCLUSION: Thymol and carvacrol could contribute to modulation of T cell activity by reducing IL-2 and IFN-γ production possibly through down regulation of AP-1 and NFAT-2 transcription factors suggesting their potential usefulness for reduction of T cell overactivity in immune-mediated diseases.
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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".