Objective: The aim of this study was to construct a model used for the accurate diagnosis of Atopic dermatitis (AD) using pyroptosis related biological markers (PRBMs) through the methods of machine learning. Method: The pyroptosis related genes (PRGs) were acquired from molecular signatures database (MSigDB). The chip data of GSE120721, GSE6012, GSE32924, and GSE153007 were downloaded from gene expression omnibus (GEO) database. The data of GSE120721 and GSE6012 were combined as the training group, while the others were served as the testing groups. Subsequently, the expression of PRGs was extracted from the training group and differentially expressed analysis was conducted. CIBERSORT algorithm calculated the immune cells infiltration and differentially expressed analysis was conducted. Consistent cluster analysis divided AD patients into different modules according to the expression levels of PRGs. Then, weighted correlation network analysis (WGCNA) screened the key module. For the key module, we used Random forest (RF), support vector machines (SVM), Extreme Gradient Boosting (XGB), and generalized linear model (GLM) to construct diagnostic models. For the five PRBMs with the highest model importance, we built a nomogram. Finally, the results of the model were validated using GSE32924, and GSE153007 datasets. Results: Nine PRGs were significant differences in normal humans and AD patients. Immune cells infiltration showed that the activated CD4+ memory T cells and Dendritic cells (DCs) were significantly higher in AD patients than normal humans, while the activated natural killer (NK) cells and the resting mast cells were significantly lower in AD patients than normal humans. Consistent cluster analysis divided the expressing matrix into 2 modules. Subsequently, WGCNA analysis showed that the turquoise module had a significant difference and high correlation coefficient. Then, the machine model was constructed and the results showed that the XGB model was the optimal model. The nomogram was constructed by using HDAC1, GPALPP1, LGALS3, SLC29A1, and RWDD3 five PRBMs. Finally, the datasets GSE32924 and GSE153007 verified the reliability of this result. Conclusions: The XGB model based on five PRBMs can be used for the accurate diagnosis of AD patients.

Background: Zhengrepresents pattern differentiation in Traditional Chinese Medicine (TCM), as the basic unit and a key concept in TCM therapeutic theory, is based on the physiology and pathology of TCM. None of the outcome measurements of atopic dermatitis (AD) areZheng-specific. The effectiveness of TCM is likely to be underestimated without aZheng-related symptom-specific instrument. The aim of this study was to develop an instrument for measuring theZheng-related symptom-specific status of patients with AD.Methods: We followed standard methodology to develop the instrument, including item generation and selection, item reduction and presentation, and pretesting, and recruited 188 patients with AD involved in a six-center randomized-controlled trial (ChiCTR-TRC-08000156) to validate the questionnaire. We conducted construct validity, reliability, and responsiveness analysis. The standardized effect size (SES) and standardized response mean (SRM) were used to calculate the responsiveness of additional items and the total score for the rating items.Results: ZRADSQ has 15 items, with 12 rating items and 3 additional items. The 12 rating items fall within three domains: AD symptoms (n = 6 items); Heat (n = 4 items) and Mood (n = 2 items). Confirmatory factor analysis provided good support for a three-factor model (d.f. = 51,x²=97.11, RMSEA = 0.07, CFI = 0.96), and the Pearson’s correlation coefficient between ZRADSQ and Severity Scoring of Atopic Dermatitis (SCORAD) was 0.40 (P < 0.001). The reliability was also good, with a Cronbach’s alpha value for ZRADSQ of 0.84, a split-half coefficient of 0.75, and a test-retest reliability coefficient of 0.98. The standardized effect size and standardized response mean were close to or larger than 1, which indicated moderate to good responsiveness.Conclusions: The ZRADSQ demonstrates promising reliability, validity, and responsiveness. It can be used to determine whetherZheng-specific or symptom-specific treatments relieve the symptom that is most bothersome the patient.

Background: Atopic dermatitis (AD) is a complex, common inflammatory skin disease. The Chinese herbal formula Huoxiang Zhengqi (HXZQ) has been a common dermatosis treatment in China for many years, but there is no high-level evidence for its effect on AD/eczema. The aim of this trial is to examine the efficacy and safety of HXZQ treating AD/eczema patients. Methods: This is a double-blind, multi-center, randomized controlled trial comparing HXZQ to a placebo. It will consist of 4 weeks’ treatment and 4 weeks of follow-up. A total of 218 participants will be randomly allocated into two groups—an HXZQ group and a placebo group, from 7 hospitals in China. Patients diagnosed with AD will be enrolled if they are in accordance with CM dampness pattern, have body surface area (BSA) of 1–10%, have investigator’s global assessment (IGA) of 1–3, have age between 18 and 70 years, and provide signed informed consent. The Eczema Area and Severity Index (EASI) is the primary outcome. The secondary outcomes are the numerical itch rating scale, IGA, BSA, Skindex-29, and EQ-5D-5L score, from baseline to the end of the treatment. Analysis will be on intention-to-treat and per-protocol subject analysis principles. Discussion: The goal of this trial is to evaluate the efficacy and availability of HXZQ oral liquid in treating AD/eczema in terms of symptoms and eczematous lesions. It will also address whether it has positive effect on QoL. Trial registration: Chinese Clinical Trial Registry (http://www.chictr.org.cn/index.aspx): Chinese herbal formula Huoxiang Zhengqi for atopic dermatitis with dampness pattern (CHARM): a double-blinded randomized controlled trial, ChiCTR1900026700. Registered on 19 October 2019.

Background: In the continuous endeavor to find safe and efficient treatments for Atopic Dermatitis (AD), there remains a considerable focus on dietary adjustments. Nevertheless, the limited availability of research and conflicting findings in the academic literature pose a hurdle in establishing conclusive recommendations. Method: Mendelian randomization (MR) was applied to the most comprehensive genome‐wide association studies (GWAS) data on tea intake (447 485), green tea intake ( n = 64 949), flavored milk intake ( n = 64 941), never eat eggs, dairy, wheat, sugar: Wheat products( n = 461 046), never eat eggs, dairy, wheat, sugar: Sugar or foods/drinks containing sugar ( n = 461 046), never eat eggs, dairy, wheat, sugar: I eat all of the above ( n = 461 046) and atopic dermatitis ( n = 218 467). We used the inverse‐variance weighted method (IVW) as the primary method. Results: The IVW analyses have demonstrated an increased tea intake was genetically associated with a reduced risk of AD (odds ratio [OR]: 0.646, 95% confidence interval [CI]: 0.430–0.968, p = 0.034). Furthermore, green tea intake was significantly negatively associated with AD (IVW OR: 0.986, 95% CI: 0.975–0.998; p = 0.024) in the IVW model. AD risk could be reduced by never eating wheat products (IVW OR: 8.243E‐04, 95% CI: 7.223E‐06–9.408E‐02, p = 0.003). There was no association between never eating eggs, dairy, wheat, sugar: Sugar, or foods/drinks containing sugar, I eat all of the above and AD. Conclusions: Our MR study suggests a causal relationship between tea intake, green tea intake, and the avoidance of eating wheat products with atopic dermatitis. Our findings recommend that preventing and managing atopic dermatitis may be achieved by never eating wheat products while increasing tea and green tea intake.

Background: Atopic dermatitis (AD) is a common chronic skin inflammation, which affects 15-20% of children worldwide. Gut microbiota and its metabolites are crucial modulators of the “gut-skin axis” in atopic dermatogenesis. However, systematic investigations integrating microbiome and metabolome profiling in mild-to-moderate pediatric AD remain limited. Objectives: To characterize gut microbiota and metabolic profiles in children with mild-to-moderate AD versus healthy controls, and to identify potential biomarkers and mechanistic pathways involved in disease pathogenesis. Methods: This single-center case-control study investigated 53 children diagnosed with AD and 16 healthy participants, and collected their fecal samples for microbial and metabonomic analysis. Results: Mild-moderate pediatric AD patients exhibited significantly increased gut microbial richness and distinct β-diversity compared to controls (PERMANOVA, R²=0.025, P = 0.017). Bacteroidota was enriched while Actinomycetota was depleted in AD patients ( P < 0.05). At genus level, Parabacteroides and Klebsiella increased, whereas Bifidobacterium decreased in AD. Species-level analysis revealed enrichment of bacteroides_plebeius , bacteroides_thetaiotaomicron , bacteroides_xylanisolvens , and parabacteroides_merdae in AD. A combined biomarker panel (Bacteroidota, Parabacteroides, and four key species) demonstrated promising exploratory diagnostic potential (AUC = 0.941, accuracy 84.6%), although these results require external validation in larger independent cohorts. Spearman analysis showed correlations between gut microbiome and clinical severity indicators. Thermodesulfobacteriota, Actinomycetota, Bifidobacterium , and specific ruminococcus strains positively correlated with the severity of AD. Metabolomics identified 68 differentially accumulated metabolites, primarily involved in lipid metabolism and nucleotide metabolism. Bacteroides species showed significant positive correlations with isovaleric acid levels in microbiota-metabolite analyses. Conclusion: Mild-to-moderate pediatric AD is characterized by distinct gut microbiota dysbiosis and metabolic alterations involving lipid metabolism. Cross-sectionally identified microbial features show exploratory associations with AD status, but causal inference is not possible. These hypothesis-generating findings support further investigation of the gut-skin axis in AD development and provide a rationale for future interventional studies targeting the microbiome and metabolome.

Objective: The presence of skin flora (SF) has been identified as a significant factor in the onset and progression of cutaneous melanoma (CM). However, the vast diversity and abundance of SF present challenges to fully understanding the causal relationship between SF and CM.Methods: A Two Sample Mendelian Randomization (TSMR) analysis was conducted to investigating the causal relationship between SF and CM. The Inverse-Variance Weighted (IVW) method was utilized as the primary approach to assess the causal relationship under investigation. Furthermore, an independent external cohort was employed to validate the initial findings, followed by a meta-analysis of the consolidated results. To address potential confounding factors related to the influence of SF on CM, a Multivariate Mendelian Randomization (MVMR) analysis was also conducted. Finally, a Reverse Mendelian Randomization (RMR) was conducted to further validate the causal association.Results: TSMR results showed that 9 SF have a causal relationship with CM in the training cohort. Although these 9 SF weren’t confirmed in the testing cohort, 4 SF remained significant in the meta-analysis after integrating results from both cohorts. MVMR analysis indicated that 3 SF were still significantly associated with CM after accounting for the interactions between different SF in the training cohort. No reverse causal relationship was identified in RMR analysis.Conclusion: A total of 9 SF were identified as having a potential causal relationship with CM; however, a large randomized controlled trial is needed to verify these results.