Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
J Allergy Clin Immunol
Proteomic profiling and machine learning for endotype prediction in chronic rhinosinusitis
BACKGROUND: Chronic rhinosinusitis (CRS) is a common, heterogeneous upper airway inflammatory disorder, affecting approximately 12% of the general population. The disease is clinically stratified into CRS without nasal polyps and CRS with nasal polyps, including the most severe subtype of nonsteroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (N-ERD). OBJECTIVE: To identify molecular signatures and biomarkers allowing for the distinction between different disease endotypes and controls, we used targeted proteomics combined with bioinformatics and machine learning analyses. METHODS: Nasal secretions and serum from 80 patients (20 each of CRS without nasal polyps, CRS with nasal polyps, N-ERD, and disease controls) were subjected to high-throughput targeted proteomics (Olink). The expression patterns of 161 and 2677 proteins, for nasal secretions and serum, respectively, were analyzed alongside clinical evaluations of nasal polyp and smell test scores. RESULTS: Two distinct expression patterns were identified in nasal secretions: proteins associated with macrophage recruitment and type 2 inflammation were increased in CRS with nasal polyps and N-ERD, whereas proteins associated with innate immunity, particularly Toll-like receptor 4 signaling, were gradually downregulated from disease control to N-ERD. Furthermore, using machine learning, we confirmed 2 potential biomarkers for nasal polyposis: the glial cell line-derived neurotrophic factor in nasal secretions and Charcot-Leyden crystal protein in serum. CONCLUSIONS: Our findings provide unique insights into CRS pathophysiology and highlight potential biomarkers for precision diagnosis and treatment, particularly in severe cases such as N-ERD.
1 Jan 2026
Read appraisal →J Allergy Clin Immunol
Explainable artificial intelligence predicts inflammatory and spatial heterogeneity from nasal polyp histology
BACKGROUND: Chronic rhinosinusitis with nasal polyps is a heterogeneous disorder characterized by diverse inflammatory signatures and endotypes. OBJECTIVE: We sought to develop a histology-based deep learning network for predicting inflammatory gene signatures and spatial patterns in chronic rhinosinusitis with nasal polyps. METHODS: We developed HE2Signature, a deep learning model, using 70 hematoxylin and eosin-stained whole-slide images of nasal polyps paired with corresponding endotypic signature gene expression profiles derived from transcriptomic data. The model was validated in an internal cohort (n = 30) and tested in an independent external cohort (n = 224) from 4 medical centers. The performance was evaluated using correlation and confusion matrix analyses and receiver-operating characteristic curves. Spatial predictions were validated by immunohistochemistry. RESULTS: The HE2Signature demonstrated strong positive correlations between the predicted and actual expression levels of 28 of the 33 signature genes. The internal validation cohort was stratified into distinct endotypes on the basis of predicted signature gene expression, achieving area under the curve values of 0.833, 0.903, and 0.935 for the T1, T2, and T3 endotypes, respectively. In the external cohort, the combination of HE2Signature-predicted Fc epsilon receptor II and CST1 effectively identified the T2 endotype, with a receiver-operating characteristic value of 0.716. Histopathologic examination of high-prediction patches revealed characteristic features associated with signature gene expression. Heatmaps and immunohistochemistry confirmed the accuracy of the model in mapping spatial gene expression patterns. HE2Signature-predicted spatial expression of signature genes correlated with blood and tissue eosinophil counts, Lund-Kennedy score, and Sino-Nasal Outcome Test-22 score, particularly within subepithelial regions. CONCLUSIONS: Our study introduces the first histology-based, explainable deep learning model capable of predicting inflammatory gene signatures and spatial molecular heterogeneity. This proof-of-concept highlights that artificial intelligence-powered histopathologic analysis can generate digital biomarkers by linking tissue patterns with molecular profiles, providing a clinically applicable framework for endotype-guided precision medicine in chronic rhinosinusitis with nasal polyps.
1 Dec 2025
Read appraisal →Ann Allergy Asthma Immunol
Current updates in the epidemiology and comorbidities of atopic dermatitis
Atopic dermatitis (AD) is a chronic, inflammatory skin disorder that affects individuals across the lifespan, with significant implications for both public health and individual well-being. This paper provides an overview of the epidemiology of AD, focusing on its prevalence, incidence, and risk factors across different populations and regions. We evaluate the growing global burden of AD, highlighting trends in both high-income and low- to middle-income countries, and the impact of socioeconomic, environmental, and genetic factors on disease manifestation. The review also explores the variations in disease severity, age of onset, and comorbidities such as asthma and allergic rhinitis. This paper emphasizes the need for more robust epidemiologic studies to further elucidate the complex interactions between genetic predisposition and environmental exposures in the development and progression of AD and explores the potential role of technology (eg, artificial intelligence) in helping to collect robust epidemiologic data. Understanding the epidemiology of AD is crucial for identifying high-risk groups, informing preventive strategies, and optimizing management approaches.
1 Nov 2025
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