Modulatory effects of thymol and carvacrol on inflammatory transcription factors in lipopolysaccharide-treated macrophages
Bibliographic record
Abstract
Inflammation is a crucial factor in the pathogenesis of numerous diseases. This study sought to evaluate the effects of thymol and carvacrol, the main components of Thymus vulgaris (thyme) essential oil, on transcription factors regulating inflammation. Lipopolysaccharide (LPS)-stimulated J774.1 mouse macrophages were examined by real time-PCR for interleukin (IL)-1β and tumor necrosis factor (TNF)-α gene expression in the presence of these compounds. Levels of inducible phospho-nuclear factor-κB (pNF-κB) p65, activator protein-1 [AP-1(c-Fos/c-Jun)], and nuclear factors of activated T-cells (NFATs) were also measured using Western blots. Levels of phosphorylation of stress-activated protein kinases (SAPKs)/c-Jun N-terminal kinase (SAPK/JNK), signal transducer, and activator of transcription (STAT-3), p38, IκBα, and NF-κB p65, as well as total levels of IL-1β and TNFα were determined. The results indicated carvacrol significantly reduced both IL-1β and TNFα at the protein and mRNA levels; thymol also significantly reduced IL-1β expression. Western blot analyses of nuclear cell extracts revealed both agents caused significantly decreased expression of c-Fos, NFAT-1, and NFAT-2; decreased expression of c-Jun was only caused by carvacrol. Neither agent inhibited p-NF-κB p65 expression. At the protein level, carvacrol and thymol each caused decreases in inducible phospho-SAPK/JNK and phospho-STAT3 levels, whereas only carvacrol resulted in increased p-p38 levels in the total cell extract. Despite the reduction of phospho-IκBα caused by both agents, p-NF-κB p65 still increased in the presence of carvacrol. Based on these findings, it is concluded that carvacrol and thymol could contribute to reduction of inflammatory responses through modulation of the expression of JNK, STAT-3, AP-1, and NFATs.
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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".