67 research outputs found

    Topical inflammasome inhibition with disulfiram prevents irritant contact dermatitis

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    BACKGROUND: The pathogenesis of contact dermatitis, a common inflammatory skin disease with limited treatment options, is held to be driven by inflammasome activation induced by allergens and irritants. We here aim to identify inflammasome-targeting treatment strategies for irritant contact dermatitis. METHODS: A high content screen with 41,184 small molecules was performed using fluorescent Apoptosis associated speck-like protein containing a CARD (ASC) speck formation as a readout for inflammasome activation. Hit compounds were validated for inhibition of interleukin (IL)-1β secretion. Of these, the approved thiuramdisulfide derivative disulfiram was selected and tested in a patch test model of irritant contact dermatitis in 25 healthy volunteers. Topical application of disulfiram, mometasone or vehicle was followed by application of sodiumdodecylsulfate (SDS) for 24 h each. Eczema induction was quantified by mexameter and laser speckle imaging. Corneocyte sampling of lesional skin was performed to assess inflammasome-mediated cytokines IL-1β and IL-18. RESULTS: Disulfiram induced a dose-dependent inhibition of ASC speck formation and IL-1β release in cellular assays in vitro. In vivo, treatment with disulfiram, but not with vehicle and less mometasone, inhibited SDS-induced eczema. This was demonstrated by significantly lower erythema and total perfusion values assessed by mexameter and laser speckle imaging for disulfiram compared to vehicle (p < 0.001) and/or mometasone (p < 0.001). Also, corneocyte IL-18 levels were significantly reduced after application of disulfiram compared to vehicle (p < 0.001). CONCLUSION: We show that disulfiram is a dose-dependent inhibitor of inflammasome pathway activation in vitro and inhibitor of SDS-induced eczema in vivo. Topical application of disulfiram represents a potential treatment option for irritant contact dermatitis

    The global impact of the COVID-19 pandemic on the management and course of chronic urticaria

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    Introduction: The COVID-19 pandemic dramatically disrupts health care around the globe. The impact of the pandemic on chronic urticaria (CU) and its management are largely unknown. Aim: To understand how CU patients are affected by the COVID-19 pandemic; how specialists alter CU patient management; and the course of CU in patients with COVID-19. Materials and Methods: Our cross-sectional, international, questionnaire-based, multicenter UCARE COVID-CU study assessed the impact of the pandemic on patient consultations, remote treatment, changes in medications, and clinical consequences. Results: The COVID-19 pandemic severely impairs CU patient care, with less than 50% of the weekly numbers of patients treated as compared to before the pandemic. Reduced patient referrals and clinic hours were the major reasons. Almost half of responding UCARE physicians were involved in COVID-19 patient care, which negatively impacted on the care of urticaria patients. The rate of face-to-face consultations decreased by 62%, from 90% to less than half, whereas the rate of remote consultations increased by more than 600%, from one in 10 to more than two thirds. Cyclosporine and systemic corticosteroids, but not antihistamines or omalizumab, are used less during the pandemic. CU does not affect the course of COVID-19, but COVID-19 results in CU exacerbation in one of three patients, with higher rates in patients with severe COVID-19. Conclusions: The COVID-19 pandemic brings major changes and challenges for CU patients and their physicians. The long-term consequences of these changes, especially the increased use of remote consultations, require careful evaluation

    Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

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    Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

    Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

    Get PDF
    Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

    Chronische Urtikaria – Was bringt die neue Leitlinie?

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    Interleukin-1-related cytokines as potential biomarkers in autoinflammatory skin diseases

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