AIMC Topic: Dermatitis, Atopic

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Microorganisms, Microbial Metabolites and Precision Nutrition: Targeting the Gut-Skin Axis for Immune Microenvironment Remodeling in Atopic Dermatitis.

Clinical reviews in allergy & immunology
Atopic dermatitis (AD), characterized by skin barrier dysfunction and microbiota dysbiosis, is closely linked to immune microenvironment imbalance. Growing evidence highlights the crucial role of microorganisms and their metabolites in immune regulat...

Ethical challenges in biomarker research and precision medicine - a qualitative study in dermatology.

BMC medical ethics
BACKGROUND: Over 300 million individuals worldwide live with Atopic Dermatitis and Psoriasis, which are among the most common chronic inflammatory skin diseases. Multimodal biomarkers are currently being developed using large-scale data and artificia...

Digital health framework for the predictive surveillance and diagnosis of atopic dermatitis.

Water research
Atopic dermatitis (AD) is an inflammatory skin disease with immunological and environmental triggers that reduces the quality of life and increases the burden on health services. It is thus important to establish effective surveillance and diagnosis ...

Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis.

Frontiers in immunology
BACKGROUND: The "gut-skin axis" has been proposed to play an important role in the development and symptoms of atopic dermatitis. Therefore, we have constructed an interpretable machine learning framework to quantitatively screen key gut flora.

Exploring the role of breastfeeding, antibiotics, and indoor environments in preschool children atopic dermatitis through machine learning and hygiene hypothesis.

Scientific reports
The increasing global incidence of atopic dermatitis (AD) in children, especially in Western industrialized nations, has attracted considerable attention. The hygiene hypothesis, which posits that early pathogen exposure is crucial for immune system ...

Potential shared mechanisms in atopic dermatitis and type 2 diabetes identified via transcriptomic and machine learning approaches.

Scientific reports
Although atopic dermatitis (AD) and type 2 diabetes mellitus (T2DM) may appear clinically and pathophysiologically unrelated, AD is a common skin disease characterized by chronic inflammation and skin barrier dysfunction, whereas T2DM is a metabolic ...

Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy.

Sensors (Basel, Switzerland)
Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such meas...

Discovery of biomarkers in the psoriasis through machine learning and dynamic immune infiltration in three types of skin lesions.

Frontiers in immunology
INTRODUCTION: Psoriasis is a chronic skin disease characterized by unique scaling plaques. However, during the acute phase, psoriatic lesions exhibit eczematous changes, making them difficult to distinguish from atopic dermatitis, which poses challen...

Screening mitochondria-related biomarkers in skin and plasma of atopic dermatitis patients by bioinformatics analysis and machine learning.

Frontiers in immunology
BACKGROUND: There is a significant imbalance of mitochondrial activity and oxidative stress (OS) status in patients with atopic dermatitis (AD). This study aims to screen skin and peripheral mitochondria-related biomarkers, providing insights into th...

Residual facial erythema in atopic dermatitis patients treated with dupilumab stratified by machine learning.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Persistent facial erythema represents a significant complication in atopic dermatitis (AD) patients undergoing treatment with dupilumab. Stratifying patients based on the erythema course is crucial for elucidating heterogeneous phenotypes...