Differential Inhibition of T Lymphocyte Proliferation and Cytokine Synthesis by [6]‐Gingerol, [8]‐Gingerol, and [10]‐Gingerol
Bibliographic record
Abstract
[6]-Gingerol, [8]-gingerol, and [10]-gingerol are pungent components of fresh ginger, extracts of which inhibit various components of the inflammatory response. Because little is known regarding the effect of gingerols with different unbranched alkyl side chain lengths on the activation and effector function of T lymphocytes, we compared the effects of [6]-gingerol, [8]-gingerol, and [10]-gingerol on murine T lymphocyte proliferation, expression of CD25 and CD69 activation markers, cytokine synthesis, and interleukin (IL)-2 receptor signaling. All three gingerols inhibited DNA synthesis by T lymphocytes, as well as interferon-γ synthesis. In contrast, only [8]-gingerol and [10]-gingerol inhibited CD25 and CD69 expression, and IL-2 synthesis. None of the gingerols affected IL-4 synthesis. Exogenous IL-2 enhanced T lymphocyte proliferation in the presence of [6]-gingerol but did not significantly increase T lymphocyte proliferation in the presence of [8]-gingerol or [10]-gingerol. In line with this finding, [8]-gingerol and [10]-gingerol impaired IL-2-induced proliferation of CTLL-2 cells, but constitutive CD25 expression was unaffected, indicating inhibition of IL-2 receptor signaling. In general, [10]-gingerol and [8]-gingerol were more potent inhibitors of T lymphocytes than [6]-gingerol. Suppression of T lymphocyte responses by gingerols suggests that these phytochemicals may be beneficial in chronic inflammatory conditions associated with excessive or inappropriate T lymphocyte activation.
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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) | 0.001 | 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".