Potential skin antiinflammatory effects of 4‐methylthiobutylisothiocyanate (MTBI) isolated from rocket (<i>Eruca sativa</i>) seeds
Bibliographic record
Abstract
Isothiocyanates (ITCs), which are organosulfur compounds present in cruciferous vegetables, have anticarcinogenic, antiinflammatory, and antiproliferative activities. These biological activities, and the knowledge that rocket seed (Eruca sativa) extract is used in skin disorders in traditional Middle Eastern medicine, led to the isolation and assessment of 4-methylthiobutylisothiocyanate (MTBI), the major ITC in rocket seeds, for its potential in the prevention of inflammatory skin diseases, such as psoriasis. MTBI was found to depress the growth of activated keratinocytes and to arrest the activated THP-1 monocytes in the G2 stage. Both MTBI and its oxidized derivative sulforaphane (SFN), which was found in the rocket seed at a low concentration, downregulated the expression of the proinflammatory genes, tumor necrosis factor (TNF)-alpha and interleukin (IL)-12/23 p40, as well as that of intercellular adhesion molecule-1, in activated THP-1 cells. These results demonstrate that MTBI may deter the inflammation process, as has been reported for SFN. Furthermore, pretreatment with MTBI hindered the induction of the inflammatory state in the THP-1 cells, as shown by the inhibition of cytokine mRNA expression of IL-1beta, IL-12/23 p40, and TNF-alpha. Overall, our results imply that MTBI may represent a new family of natural compounds possessing significant skin inflammation-preventive activities.
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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".