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

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 and facilitating advanced drug efficacy predictions.

Objectives: This study aimed to identify factors associated with facial erythema severity in dupilumab-treated AD patients and to establish a prediction model for drug response based on the identified factors.

Best practices for multimodal clinical data management and integration: An atopic dermatitis research case.

Background: In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science.

Methods: We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies.

Multiomics Investigation Revealing the Characteristics of HIV-1-Infected Cells In Vivo.

For eradication of HIV-1 infection, it is important to elucidate the detailed features and heterogeneity of HIV-1-infected cells in vivo. To reveal multiple characteristics of HIV-1-producing cells in vivo, we use a hematopoietic-stem-cell-transplanted humanized mouse model infected with GFP-encoding replication-competent HIV-1. We perform multiomics experiments using recently developed technology to identify the features of HIV-1-infected cells.