The anionic surfactant Sodium Dodecyl Benzene Sulfonate alleviates inflammatory responses in atopic dermatitis via NFκB/MAPK pathways in a STAT3-dependent manner
Bibliographic record
Abstract
Atopic dermatitis (AD) is a common chronic inflammatory skin disease characterized by chronic inflammation, barrier impairment, and immunoglobulin E-mediated sensitization. Sodium dodecyl benzene sulfonate (SDBS) is a widely used anionic surfactant which are believed to exacerbate AD according to the hygiene hypothesis. However, recent studies have shown that SDBS does not significantly upregulate the expression of key proinflammatory cytokines, which is inconsistent with previous hypotheses. Furthermore, it is not known whether SDBS affect the inflammatory response to AD. Herein, we used mice with MC903-induced AD-like dermatitis and tumor necrosis factor (TNF)-α/interferon (IFN)-γ (T/I)-treated HaCaT cells to investigate the effects of SDBS on AD. We found that topical use of 0.1% and 1% SDBS did not exacerbate dermatitis in mice. Instead, clinical and histological remission of MC903-induced AD-like dermatitis were observed following the administration of both 0.1% and 1% SDBS, where 1% SDBS treatment showed a greater degree of relief compared to 0.1% SDBS. SDBS also reduced the expression of major cytokines involved in the pathogenesis of AD both in vivo and in vitro. Furthermore, our results showed that SDBS alleviated AD via the nuclear factor kappa B/mitogen-activated protein kinase pathways in a signal transducer and activator of transcription 3-dependent manner. In conclusion, our findings suggest for the first time that the surfactant SDBS has anti-inflammatory properties, which raises new possibilities for detergent use among patients with AD.
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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".