In the era of asthma remission, quality of life (QOL) in daily activities is increasingly valued in addition to exacerbation control. However, the value of the Asthma Control Test (ACT) for assessing QOL remains unclear. This study compared the use of the ACT with the Asthma Quality of Life Questionnaire (AQLQ), with a focus on activity limitations.

Anaphylaxis is a life threatening complication of allergy and one of the treatment approaches for allergy is immunotherapy. Allergen immunotherapy is the only treatment that can alter the natural history of allergic disease. Oral immunotherapy is a viable therapeutic route in patients with food allergies, during which peripheral plasmablasts, which are activated B cells, expand, and allergen-specific IgG4 is induced. We aimed to characterize the profile of plasmablasts undergoing oral immunotherapy.

Clinical remission in asthma has gained prominence as both a therapeutic goal and a research endpoint, although its operational definitions have varied. To harmonize Japanese practice with emerging global frameworks, the Japanese Society of Allergology (JSA) conducted a two-round modified Delphi survey to establish a consensus definition for inclusion in the 2024 Asthma Prevention and Management Guidelines (JGL 2024). In Round 1 (January 2024), 81 JGL 2024 guideline committee members representing adult and pediatric specialties were invited. Seventy-four percent agreed that clinical remission should be defined, and 50 % supported including both on- and off-treatment remission. Four core components emerged: absence of exacerbations, well-controlled symptoms, no continuous oral corticosteroid use, and optimization of pulmonary function. Round 2 refined operational thresholds for symptom control, adopting ACT ≥23 (C-ACT ≥23 for children) and ACQ ≤0.75, consistent with JGL's long-standing goal of achieving a truly symptom-free state without reliever use. Pulmonary function was defined as "optimization," encompassing normalization where achievable and stabilization when normalization is unlikely (e.g., airway remodeling), which received strong agreement. Collaboration between adult and pediatric experts affirmed clinical remission as a milestone toward off-treatment remission and potential cure, broadening its applicability across severities and age groups. This review further summarizes evidence supporting remission as an outcome of biologic therapy, its key predictors (e.g., smoking, obesity, disease duration), pediatric perspectives, and future directions. JGL 2024 formally adopts these criteria, providing a rigorous and pragmatic framework to advance patient-centered asthma care and reframe management toward disease modification and eventual cure.

Because different immunosuppressive therapies have their own characteristic properties to inhibit/enhance the production of various cytokines in Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN), cytokine profile dynamics could be used to predict and monitor the therapeutic response across different treatments, when assessed at baseline and following therapy.

Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare systemic vasculitis for which achieving remission is the primary therapeutic goal. Historically, remission in EGPA was defined by the absence of active vasculitis, typically a Birmingham Vasculitis Activity Score (BVAS) of 0. However, this definition is insufficient as it overlooks the significant morbidity associated with long-term glucocorticoid (GC) therapy. Recent evidence highlights that even low-dose GCs carry substantial risks, challenging the traditional acceptance of remission on GC. This review summarizes the evolution of the remission concept in EGPA, highlighting the paradigm shift seen in recent pivotal clinical trials for biologics, which have incorporated stringent GC dose thresholds (e.g., prednisone ≤4 mg/day) into their primary endpoints. This reflects a growing consensus that minimizing GC exposure is a crucial component of a successful treatment outcome. Further, this review explores potential future components for remission criteria, such as organ-specific activity measures and patient-reported outcomes.

Allergic diseases are highly prevalent chronic inflammatory conditions. They often co-occur because of shared immunological pathways. However, population-level studies covering a broad range of allergic diseases across the lifespan remain limited. The objective of this study was to examine the age-specific trends in allergic disease prevalence, patterns of multimorbidity, and longitudinal interrelationships among nine allergic conditions using a nationwide cohort.

Inducible co-stimulator (ICOS) regulates the proliferation and differentiation of a variety of T cells. It is considered to be a potential immunotherapy target and marker for many diseases. Its dual role is worthy of further study in allergic rhinitis (AR).

Protein language models (pLMs) are rapidly emerging as revolutionary artificial intelligence technologies that bring transformative changes to drug discovery and therapeutic research. pLMs acquire rich representational capabilities from large-scale sequence datasets, enabling the solution of various biological problems that were difficult with conventional methods. In this review, we provide a comprehensive overview of various pLMs and their implementations, exploring their potential utility in drug discovery and therapeutic research. First, we systematically classify pLMs based on their architectures and information sources while discussing their development to the present. We also explain recent trends in multimodal approaches that integrate co-evolutionary information, structural information, and functional information, as well as domain-specific models specialized for particular domains such as antibodies and T-cell receptors. We then provide a comprehensive overview of various therapeutic applications of pLMs, including mutation effect prediction, function prediction, and structure prediction. Finally, we discuss future prospects of pLMs toward therapeutic applications and challenges for transforming them into technologies that contribute to actual diseases.

Allergic diseases are characterized by heterogeneity driven by complex interactions between genetic, environmental, and immunological factors. Conventional classifications based solely on clinical phenotypes often fails to capture the underlying molecular diversity, thereby limiting therapeutic precision and patient outcomes. Integrative omics-encompassing genomics, transcriptomics, proteomics, metabolomics, and microbiomics-has emerged as a powerful approach to redefine disease mechanisms and advance precision medicine. By integrating high-dimensional molecular data with clinical phenotyping, omics approaches enable the identification of disease endotypes, biomarker discovery, and patient stratification. This review highlights recent developments in clinical-omics integration, with a focus on atopic dermatitis (AD) as a prototypical allergic disease. Drawing from our studies, we illustrate how tissue-level transcriptomic profiling, combined with unbiased computational analysis, can uncover immunological heterogeneity and treatment-response patterns in AD. Additional examples in asthma and food allergy demonstrate how integrated multi-omics can uncover gene-environment interactions and elucidate mechanisms behind disease severity and health disparities. We also address practical and ethical challenges in data harmonization, privacy, and interoperability, and underscore the critical role of computational methods and infrastructure development in enabling clinically meaningful interpretation. Importantly, successful translation of multi-omics data into clinical practice requires iterative, interdisciplinary collaboration between clinicians, data scientists, and basic researchers. By bridging molecular complexity and clinical heterogeneity, integrative omics is reshaping the landscape of allergy research. As technologies evolve, this framework will be crucial for developing predictive models and personalized therapeutic strategies, ultimately bringing us closer to individualized, data-driven care in allergic diseases.

Single-cell RNA sequencing (scRNA-seq) has revolutionized biology through high-throughput quantification of gene expression at individual cell resolution. However, standard scRNA-seq provides only static cellular snapshots, obscuring dynamic processes that unfold temporally, such as differentiation, reprogramming, and disease progression. RNA Velocity, introduced in 2018, offers a groundbreaking solution. By leveraging unspliced pre-mRNA and spliced mRNA information, RNA Velocity models infer instantaneous gene expression change rates and effectively predict future transcriptional states over hour-long timescales. This review charts the evolution of this powerful concept, beginning with foundational principles and mathematical models of transcriptional dynamics. We explore Velocyto's pioneering implementation, discuss successes and limitations, and then examine second-generation advanced computational tools that generalize the framework, including scVelo, dynamo, and CellRank. A dedicated section highlights growing applications in allergy and immunology research, where these methods reveal novel disease mechanisms in asthma, atopic dermatitis, and chronic inflammation by analyzing immune cell differentiation and state transitions. We explored modern frontiers, including RNA Velocity integration with spatial and multimodal data, and the latest deep learning-based methods. Finally, we addressed the current challenges and remaining limitations of RNA Velocity analysis, offering insights into best practices and future directions. Throughout, we emphasize applications to allergic and immune-mediated diseases-including asthma, atopic dermatitis, and prurigo nodularis-to guide researchers and clinicians in allergy and immunology. RNA Velocity is becoming indispensable for navigating the complex, dynamic cellular world and transforming our understanding of temporal biological processes from static observations to predictive, dynamic insights that illuminate cellular fate decisions and disease mechanisms.

The immune system presents some of the most complex challenges in biology, encompassing nonlinear interactions, high-dimensional regulatory mechanisms, and substantial variability across individuals and contexts. As a result, traditional model-driven approaches often fall short in optimizing experimental conditions or therapeutic strategies. Black-box optimization methods-particularly Bayesian optimization (BO) and evolutionary algorithms (EAs)-offer powerful tools for guiding biological discovery when mechanistic understanding is incomplete or intractable. These algorithms iteratively propose informative experiments by learning from noisy, expensive, and sparse data, enabling efficient exploration of vast experimental spaces. In this review, we provide a comprehensive overview of black-box optimization methodologies and their applications in life science, with a particular focus on immunology and allergy research. We detail how black-box optimization is transforming various stages of biomedical R&D, from molecular design (e.g., antibodies, peptides) and gene circuit tuning to culture protocol optimization and patient-specific dose adjustment. We highlight key algorithmic advances, including constrained, multi-objective, parallel and high-dimensional BO, as well as recent developments such as grey-box optimization and transfer learning. Practical considerations, such as software tools and reproducibility-enhancing checklists, are also discussed. By integrating black-box optimization with automated experimentation platforms and high-throughput biological systems, researchers can accelerate discovery, personalize interventions, and systematically optimize complex immunological processes. We argue that black-box optimization will become a foundational component of experimental design and decision-making in the life sciences, bridging computational strategies with biological insight in increasingly adaptive and interpretable ways.

The conventional in vitro lymphocyte transformation test (LTT) for diagnosing drug hypersensitivity reactions (DHRs) is limited by low sensitivity. To improve detection and assess T cell activation strength, we adapted this test to a cytokine-based assay (Cyto-LTT) by measuring IL-5, IL-13, IFN-γ, granzyme B and granulysin secretion.

Dupilumab is approved in Japan for the treatment of atopic dermatitis in patients aged 6 months to 18 years. However, long-term data are lacking in this patient population. Here we report the final analysis of a long-term open-label extension (OLE) of a phase 3 study that assessed dupilumab in Japanese patients aged ≥6 months to <18 years with moderate-to-severe atopic dermatitis inadequately controlled with existing therapies.

Drug Reaction with Eosinophilia and Systemic Symptoms (DReSS) is a severe T cell-mediated hypersensitivity reaction. T cells in DReSS are stimulated via the p-i mechanism (pharmacological interaction with immune receptors), where the drug shows an off-target binding to immune receptors (TCR and/or HLA) leading to an unorthodox activation of T cells. P-i stimulations are particularly strong in DReSS, as the causative drugs are typically administered at high doses for prolonged durations (>7 days) and bind with relatively high affinity to a specific HLA allele and/or TCR. This mechanism results in delayed yet profound immune activation, progressing through four distinct phases. The p-i concept provides a unifying explanation for many puzzling aspects of DReSS and has significant implications for diagnosis, management, and prevention. Recognizing drug concentration, therapy duration, and HLA affinity as key determinants of strong p-i-mediated immune activation can improve risk assessment, early diagnosis, and intervention strategies for DReSS.

Allergen sensitization patterns are heterogeneous, and their clinical relevance is often obscured by extensive cross-reactivity. We applied non-negative matrix factorization (NMF) to disentangle overlapping immunoglobulin E (IgE) signals and define clinically meaningful allergen signatures in a large Korean cohort.

Despite advances in pharmacologic therapy, a subset of patients with bronchial asthma (BA) experience persistent symptoms. Multiple chemical sensitivity (MCS), a non-allergic condition triggered by low-level chemical exposures, may be responsible for asthma-like symptoms. Although epidemiological studies have reported a high co-prevalence of MCS and BA, clinical comparisons among patients with BA between those with and without MCS are limited. We aimed to characterize the clinical and symptomatic profiles of patients with BA and comorbid MCS.