Atopic dermatitis is a chronic, recurrent inflammatory condition with a current estimated global prevalence rate of 10% to 20% among children. While issues such as pruritus and allergic comorbidity have garnered significant attention, the risk of infection may be underappreciated. Recent advancements in atopic dermatitis treatment have yielded considerable progress but have also presented challenges. Infections in atopic dermatitis patients are classified into pre-treatment and post-treatment infections, which can be grouped into skin infections and extra-skin infections according to the site of infection. It is recognized that alterations in the skin microbiome may serve as potential triggers. However, the temporal relationship between altered skin microbiome and disease onset and recurrence remains incompletely understood. In the era of immunomodulatory therapy, which intervenes systemically in the immune status of children with atopic dermatitis, the magnitude of infection risk and strategies for systemic infection prevention warrant further exploration. Moreover, mounting evidence has substantiated the efficacy of microbiome-based therapy in ameliorating inflammatory diseases. Could this modality potentially emerge as a novel therapeutic approach for atopic dermatitis? Artificial intelligence and machine learning technology are gaining ground in the diagnosis and management of inflammatory diseases. What can we learn from other inflammatory conditions that can be applied to the management of atopic dermatitis?
In this Research Topic, we encourage the submission of clinical, translational, and basic research aiming to investigate the interaction between atopic dermatitis and infections. Submissions are welcome for the following article types: original research, systematic review, methods, review, mini review, hypothesis, and perspective. We particularly welcome contributions that include, but are not limited to, the following topics:
● The relationship between clinical phenotype, endophenotype, microbiome, and infections
● Skin infections and systemic infections in children with atopic dermatitis (e.g. cytomegalovirus infection, infected dermatitis, eczema herpeticum, bacterial endocarditis, molluscum contagiosum, sepsis, septic embolus, conjunctivitis, eczema impetiginous, and folliculitis)
● Temporal patterns of atopic dermatitis and infections
● Prediction of the risk of infections in children with atopic dermatitis
● Prevention of infections in children with moderate to severe atopic dermatitis
● An emerging pathogen in atopic dermatitis
● AI-powered analysis in the diagnosis and precision treatment of atopic dermatitis
Keywords:
Atopic Dermatitis, Child, Infections, Microbiota, Artificial Intelligence
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Atopic dermatitis is a chronic, recurrent inflammatory condition with a current estimated global prevalence rate of 10% to 20% among children. While issues such as pruritus and allergic comorbidity have garnered significant attention, the risk of infection may be underappreciated. Recent advancements in atopic dermatitis treatment have yielded considerable progress but have also presented challenges. Infections in atopic dermatitis patients are classified into pre-treatment and post-treatment infections, which can be grouped into skin infections and extra-skin infections according to the site of infection. It is recognized that alterations in the skin microbiome may serve as potential triggers. However, the temporal relationship between altered skin microbiome and disease onset and recurrence remains incompletely understood. In the era of immunomodulatory therapy, which intervenes systemically in the immune status of children with atopic dermatitis, the magnitude of infection risk and strategies for systemic infection prevention warrant further exploration. Moreover, mounting evidence has substantiated the efficacy of microbiome-based therapy in ameliorating inflammatory diseases. Could this modality potentially emerge as a novel therapeutic approach for atopic dermatitis? Artificial intelligence and machine learning technology are gaining ground in the diagnosis and management of inflammatory diseases. What can we learn from other inflammatory conditions that can be applied to the management of atopic dermatitis?
In this Research Topic, we encourage the submission of clinical, translational, and basic research aiming to investigate the interaction between atopic dermatitis and infections. Submissions are welcome for the following article types: original research, systematic review, methods, review, mini review, hypothesis, and perspective. We particularly welcome contributions that include, but are not limited to, the following topics:
● The relationship between clinical phenotype, endophenotype, microbiome, and infections
● Skin infections and systemic infections in children with atopic dermatitis (e.g. cytomegalovirus infection, infected dermatitis, eczema herpeticum, bacterial endocarditis, molluscum contagiosum, sepsis, septic embolus, conjunctivitis, eczema impetiginous, and folliculitis)
● Temporal patterns of atopic dermatitis and infections
● Prediction of the risk of infections in children with atopic dermatitis
● Prevention of infections in children with moderate to severe atopic dermatitis
● An emerging pathogen in atopic dermatitis
● AI-powered analysis in the diagnosis and precision treatment of atopic dermatitis
Keywords:
Atopic Dermatitis, Child, Infections, Microbiota, Artificial Intelligence
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.