Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 20 appraisals
Nanotechnology
DFT and machine learning investigation of Au/Pt-decorated SnS2 monolayers for asthma and COPD diagnosis
Asthma and chronic obstructive pulmonary disease (COPD) are among the most prevalent chronic respiratory diseases worldwide, affecting hundreds of millions of people and contributing significantly to global morbidity and mortality. This work introduces a novel Au/Pt-decorated SnS2heterostructure for exhaled NO2detection, representing the new study to explore its role in lung disease diagnostics. It demonstrates ppb level NO2detection, a key biomarker for asthma and COPD, enabling early and differentiation of lung conditions by providing quantitative analysis of trace-level gases, which are often elevated in inflamed airways. While two-dimensional (2D) SnS2offers strong potential as a sensing platform, prior studies relied mainly on density functional theory (DFT) based gas sensing. Here, we present unprecedented integration of DFT and machine learning (ML) to investigate the gas sensing performance of pristine and Au/Pt-decorated SnS2monolayers. DFT analysis revealed enhanced adsorption and charge transfer upon noble-metal decoration, with Pt-SnS2showing optimal characteristics for asthma and COPD detection. Five ML models were trained on DFT and experimental-derived descriptors to rapidly predict the sensing behaviour of multiple gases, including NO2, among which XGBoost achievingR2= 0.9961. Both ML and DFT methods consistently identified NO2as the most sensitive analyte. This novel DFT-ML synergy not only validates fundamental adsorption mechanisms but also provides a scalable pathway for accelerated screening and design of high-performance gas sensors. Our findings establish a new prototype for integrating ML with first-principles simulations in the design of next-generation 2D material-based sensing devices.
24 July 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review
Cough is a common and physiologically informative component of respiratory morbidity, but its potential for diagnosing and monitoring disease is not thoroughly investigated. This systematic review synthesised the literature on algorithmic and statistical models analysing cough acoustics for diagnosing or monitoring respiratory conditions. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, five databases (PubMed, Embase, Scopus, Web of Science and CENTRAL) were systematically searched for studies published from January 2010 to June 2025. Eligible studies performed quantitative acoustic feature analysis of human coughs using statistical, machine learning or deep learning models and reported diagnostic or prognostic performance. 89 studies from 34 countries were assessed, covering cough detection (n=31), disease classification (n=55) and disease severity prediction (n=3), reflecting potential applications in disease monitoring. Deep learning approaches, especially convolutional and recurrent networks, were predominant (n=56) and tended to achieve the higher accuracies, although machine learning ensemble methods and logistic regression also demonstrated strong performance, particularly with well-engineered features. Across different diseases, sensitivities and specificities were often reported to be ≥90%, notably for tuberculosis, asthma and COVID-19. However, methodological weaknesses were common, with only 11.2% of studies introducing external validation, 71.1-87.6% demonstrating high risk of bias (according to PROBAST-AI) and most based on small, homogeneous or crowdsourced cohorts with limited generalisability. These limitations contribute to inflated internal performance and uncertainty about real-world applicability. Cough acoustic biomarkers hold promise as an adjunctive tool for screening and longitudinal monitoring in low-resource environments. Nevertheless, widespread implementation will require large, multicentre validation, standardised calibration, bias control and incorporation into privacy-preserving workflows.
25 June 2026
Read appraisal →European journal of pediatrics
Effect of probiotics on allergic rhinitis in children; A systematic review and meta-analysis
BACKGROUND: Allergic rhinitis (AR) is a prevalent chronic inflammatory disorder in children, often undiagnosed and associated with reduced quality of life and risk of comorbidities such as asthma. Probiotics have been found to be potential modulators of immune responses in allergic conditions. This systematic review and meta-analysis evaluated the efficacy of probiotics compared with placebo in the management of pediatric AR. METHODS: Searches were conducted in accordance with a pre-registered protocol using MEDLINE (PubMed), the Web of Science, Scopus, and Embase through April 8, 2025. Randomized controlled trials (RCTs) of children aged < 18 years that compared probiotics, prebiotics, or synbiotics with a placebo were considered for inclusion. Outcomes of interest included clinical and laboratory parameters. Random-effects models with Hedges' g were used; heterogeneity was assessed via I2. RESULTS: Fourteen RCTs involving 1,739 children were included. Meta-analysis revealed a significant reduction in nasal symptom scores (SMD = -1.40; 95% CI: -2.26 to -0.54; I2 = 91.3%) and eye symptom scores (SMD = -3.59; 95% CI: -5.84 to -1.33; I2 = 94.3%). Quality of life (RQLQ) was also significantly improved (SMD = -2.98; 95% CI: -4.85 to -1.12; I2 = 95.7%). Among laboratory markers, a small but significant increase in serum eosinophils was observed (SMD = 0.29; 95% CI: 0.06 to 0.52; I2 = 0%). CONCLUSION: Probiotics were associated with improvements in nasal and ocular symptoms and quality of life in children with AR; however, these findings should be interpreted with caution due to substantial heterogeneity across studies. Probiotics may have a potential role as an adjunctive therapy, but further high-quality, standardized trials are needed. PROSPERO registration code: CRD420250654461 on 24 February 2025. What is Known • Probiotics have been investigated as adjunctive therapy for pediatric allergic rhinitis because of their potential immunomodulatory effects. • Previous studies and meta-analyses have reported inconsistent findings regarding the clinical and immunological effects of probiotics in allergic rhinitis. What is New •This meta-analysis demonstrated significant improvements in nasal symptoms, ocular symptoms, and quality of life in children with allergic rhinitis receiving probiotics versus placebo. • Probiotic supplementation showed no consistent beneficial effects on immunological markers, while substantial heterogeneity remained across studies.
30 May 2026
Read appraisal →Journal of breath research
Investigating short-term lung inflammation using exhaled breath VOCs from exposure to candles: a randomized controlled crossover study among young mild asthmatics
Lung inflammation is associated with a response to pollution or irritants and is reported to release volatile organic compounds (VOCs) in breath, that can be monitored non-invasively. An approach to studying short-term lung inflammation is exposure to environmental emissions. This study used brief exposure to PM₂.₅ emissions from two types of burning candles to evaluate potential differences in airway inflammation among mildly asthmatic individuals. The aim was to explore VOCs linked to lung inflammation by exposure to candle emissions; emissions from candle A, a newly developed candle with a smaller emission to the surroundings and candle B, a well-characterised commercially available candle. Seventeen non-smoking asthmatics (11 female) with a mean age of 21.9 years participated in a randomised controlled double-blind crossover study including two exposure sessions: i) air with candle A emissions (mean PM2.554.1µg m-3), and ii) air with candle B emissions (mean PM2.598.4µg m-3). Participants underwent two five-hour, double-blind exposure sessions in a controlled climate chamber, each involving emissions from one of two candle types. Exhaled breath was collected at baseline (0 h), immediately post-exposure (5 h), and the following morning (24 h). VOCs were analysed using both a targeted panel of 23 predefined compounds and an untargeted workflow, which yielded 21 breath-derived VOCs after ambient-blank filtering. Statistical comparisons used Wilcoxon signed-rank tests with Benjamini-Hochberg correction. Across both exposure sessions, ten exhaled VOCs showed significant changes between baseline and immediate post-exposure (5 h), including increases in several aldehydes and decreases in selected sulfur-containing compounds. These changes were not present at 24 h, where samples resembled baseline profiles. The ten VOCs were identified from a combined dataset of 23 targeted compounds and 21 untargeted breath-derived VOCs; only the untargeted analysis yielded significant findings after correction. PCA demonstrated clear separation between baseline and 5 h samples, particularly for candle B, for which 0 h, 5 h and 24 h samples formed distinct clusters. This candle-specific response produced a multi-VOC signature capable of distinguishing individuals immediately after exposure, suggesting that exhaled VOCs can reflect short-term airway inflammatory responses. The findings suggest that an emission-specific, exhaled breath, multi-VOC profile can detect individuals exposed to inflammation triggers, such as candles. This indicates that VOCs could serve as biomarkers for detecting short-term airway inflammation, as evidenced by the systematic difference in VOCs observed 5 h after exposure to candle B. This research contributes to the advancing field of VOC-based health monitoring and might affect public health implications.
30 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
The evolution of scientific knowledge in childhood asthma over time
Paediatric asthma management has undergone a significant transformation from rudimentary assessments in the early 20th century to sophisticated diagnostic and therapeutic approaches today. Early clinical observations lacked paediatric specificity, but mid-20th-century studies introduced functional assessments, spirometry and recognition of asthma as a chronic inflammatory condition. The introduction of inhaled corticosteroids transformed long-term management, offering targeted control with reduced systemic risks. Advances in noninvasive diagnostics, such as fractional exhaled nitric oxide, induced sputum analysis, exhaled breath condensate and electronic nose technology, have improved inflammation monitoring, phenotype classification and therapeutic responsiveness. The integration of omics technologies, i.e. genomics, proteomics and metabolomics, has enabled deeper insights into disease mechanisms and facilitated early, individualised interventions. Concurrently, artificial intelligence (AI) and machine learning are emerging as tools for predicting exacerbations, identifying clinical subtypes and enhancing decision-making through large-scale data integration. Despite these advancements, challenges remain around standardisation, data quality and ensuring equitable access. This narrative review synthesises decades of progress in paediatric asthma care, emphasising the transition from empirical treatment to personalised, biomarker-driven strategies. It highlights current gaps, particularly in algorithm transparency, paediatric-specific validation and holistic care integration. As asthma management enters an era of digital health and AI-assisted precision medicine, future success will depend on interdisciplinary collaboration, real-world validation and policies that close care disparities.
29 May 2026
Read appraisal →Pharmaceutical biology
Ginger-based formulations for allergic rhinitis disease: a systematic review and meta-analysis of experimental studies in animals and humans
BACKGROUND: Allergic rhinitis is a chronic non-communicable disease that adversely affects respiratory health and quality of life. Ginger-based formulations, owing to their anti-inflammatory and immunomodulatory properties, may offer a promising alternative therapeutic option. OBJECTIVE: To systematically review and meta-analyze evidence from animal studies and human randomized controlled trials assessing the efficacy of ginger-based formulations in allergic rhinitis. METHODS: A systematic search of the Cochrane Library, Embase, PubMed, and Scopus (April 2025) identified studies comparing ginger-based formulations with control interventions. Outcomes included clinical symptoms and relevant biomarkers. Risk of bias was assessed using the SYRCLE tool for animal studies and RoB2 for human studies. Meta-analyses were conducted using random-effects models, with results expressed as standardized mean difference (SMD) and 95% confidence intervals (CIs). RESULTS: Twenty-one studies (6 animal and 15 human studies) were included. In rodents, ginger-based formulations significantly reduced nasal rubbing (SMD = -1.99; 95%CI, -3.15 to -0.83), sneezing (SMD = -1.99; 95%CI, -2.78 to -1.20), and biomarkers (IL-4, IL-5, and OVA-specific IgE). In humans, ginger-based formulations significantly improved itching (SMD = -0.51; 95%CI, -0.79 to -0.24), runny nose (SMD = -0.59; 95%CI, -0.87 to -0.30), sneezing (SMD = -0.56; 95%CI, -0.75 to -0.37), total nasal symptom scores (SMD = -0.63; 95%CI, -1.00 to -0.26), and quality of life (SMD = -0.56; 95%CI, -0.87 to -0.24), and eosinophil levels. CONCLUSION: Ginger-based formulations improve symptoms and biomarkers of allergic rhinitis in both animal and human studies. As many interventions are multi-component, the observed effects are more appropriately attributed to the overall formulations rather than ginger alone. Further research is needed to explore mechanisms and clarify clinical applications.
26 May 2026
Read appraisal →The British journal of dermatology
Burden and risk of asthma and rhinitis in people with atopic dermatitis: global estimates from a hierarchical Bayesian model
BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease often associated with asthma and rhinitis. However, epidemiological evidence on these comorbidities remains fragmented and is largely limited to high-income countries. OBJECTIVES: To provide comprehensive global, regional and country-level estimates of the prevalence and odds ratios (ORs) of asthma, rhinitis and their subtypes among individuals with AD. METHODS: We systematically reviewed 278 studies retrieved from PubMed, Embase, Web of Science and CNKI (China National Knowledge Infrastructure). Standardized prevalence estimates and ORs were derived using a Bayesian hierarchical linear mixed-effects model. RESULTS: Among individuals with AD, the global prevalence estimate of asthma, rhinitis, allergic rhinitis, rhinoconjunctivitis, and coexisting asthma and rhinitis was 20.1% (ranging from 2.5% in Israel to 59.4% in India), 45.1% (ranging from 7.8% in Israel to 82.7% in India), 41.4% (ranging from 6.8% in Israel to 80.3% in India), 30.7% (ranging from 4.3% in Israel to 71.9% in India) and 10.4% (ranging from 1.1% in Israel to 40.8% in India), respectively. Estimates were higher when restricted to patients with AD diagnosed by dermatologists or physicians. The highest comorbid prevalence rates were found for India, Cuba, Finland and Puerto Rico. Compared with healthy control individuals, AD was associated with higher odds of asthma (OR 2.73; ranging from 1.59 in China to 6.64 in Ethiopia), rhinitis (OR 2.98; ranging from 1.73 in China to 7.23 in Ethiopia), allergic rhinitis (OR 2.42; ranging from 1.41 in China to 5.89 in Ethiopia), rhinoconjunctivitis (OR 4.21; ranging from 2.45 in China to 10.19 in Ethiopia), and coexisting asthma and rhinitis (OR 2.42; ranging from 1.41 in China to 5.89 in Ethiopia). CONCLUSIONS: This study provides the first globally standardized estimates of respiratory comorbidities in AD, and can inform clinical practice and public health policy. Atopic dermatitis is a common long-term inflammatory skin disease. It is also called eczema. It affects more than 200 million children and adults worldwide. People with the disease often have other conditions, particularly asthma and rhinitis. Asthma affects breathing and rhinitis causes sneezing or congestion. Most studies on these conditions have been in wealthy countries. They have also used a range of different research methods. This makes it difficult to compare and understand the global situation. In this study, researchers in China and the USA analysed data from 278 studies from around the world. We estimated how common asthma and rhinitis are in people with eczema in different regions and countries. We found that about 1 in 5 people with eczema have asthma and almost half have rhinitis. We also found that people with eczema are 2 to 4 times more likely to have these conditions than people without eczema. Some countries showed particularly high rates of these conditions. Our findings suggest that asthma and rhinitis are common problems for people with eczema. This is true no matter where they live. The information from this study may help doctors recognize and manage these conditions in people with eczema. These findings could also support future research to improve disease prevention and patient care.
20 May 2026
Read appraisal →Acta dermato-venereologica
Pregnancy in Women with Atopic Dermatitis: A Systematic Review of Concerns and Challenges
Atopic dermatitis (AD) has negative individual and societal implications. However, the potential impact of ADatopic dermatitis on reproduction has only been sporadically investigated. We aimed to provide an overview of pregnancy in ADatopic dermatitis, with a specific focus on concerns, challenges, the number of children, and adverse pregnancy and birth outcomes. A systematic literature search was performed using EMBASE and PubMed up until 19th January 2026 for original articles in relation to reproduction in female ADatopic dermatitis. Twenty-four highly heterogeneous studies were included. Concerns about ADatopic dermatitis in relation to pregnancy and family planning were reported in >50% of the patients in three3 out of four4 studies. Conflicting results were seen regarding fertility and disease severity during pregnancy. Pregnancy rates were similar or increased based on two2 studies. Eleven studies addressed adverse pregnancy and birth outcomes, of which five5 studies unanimously reported a protective or indifferent effect of ADatopic dermatitis on pre-term birth, gestational diabetes and stillbirth. Outcomes such as gestational hypertension, preeclampsia and miscarriage showed divergent results. Overall, ADatopic dermatitis causes considerable worries in relation to family planning. However, this review implies, that pregnancy rates as well as complications in pregnancy and during childbirth in women with ADatopic dermatitis overall are consistent with women without ADatopic dermatitis.
20 May 2026
Read appraisal →Scientific reports
Cytokines and immunologic checkpoint molecules in predicting success of allergen immunotherapy.
There are large variations in how individual patients respond to allergen immunotherapy (AIT) against grass and/or birch allergy. There are currently no reliable biomarkers to predict which patients are likely to benefit from the treatment. The purpose of this study was to examine the potential of cytokine and soluble immunologic checkpoint molecule (ICM) levels as biomarkers for AIT success. Blood samples collected before starting AIT were analyzed for concentration of 92 different cytokines and 14 different ICMs. Traditional univariable statistical analysis was performed to evaluate differences between responders and non-responders. Furthermore, both unsupervised and supervised machine learning algorithms were used for multivariable analysis of differences between the responders and non-responders and to try to identify clusters within the subjects which could potentially be linked to endotypes of allergic rhinitis. Neither univariable nor multivariable analysis showed any significant correlations between AIT outcome and pre-treatment levels of cytokines or soluble ICM. In the cluster analysis, 4 clustering algorithms consistently grouped 48 of the 60 subjects into 3 distinct clusters. However, these clusters did not correlate with clinical characteristics, indicating that the clusters are unlikely to represent actual biological endotypes. The findings of this study did not provide evidence supporting the use of pre-treatment levels of cytokines and ICM as biomarkers for AIT outcome.
20 May 2026
Read appraisal →Current allergy and asthma reports
Impact of IL-4/IL-13 Blockade with Dupilumab on the Microbiome in Type 2 Inflammatory Diseases: A Systematic Review
PURPOSE OF REVIEW: To systematically review current evidence on microbiota changes associated with dupilumab treatment across different anatomical sites in type 2 inflammatory diseases. RECENT FINDINGS: Fifteen studies were included, comprising two randomized trials and thirteen observational studies, mostly in atopic dermatitis, with fewer data in chronic rhinosinusitis with nasal polyps and NSAID-exacerbated respiratory disease. The skin was the most frequently investigated site, followed by the sinonasal tract and gut. Across skin studies, dupilumab was consistently associated with reduced Staphylococcus aureus, increased microbial diversity, and enrichment of commensal taxa. Sinonasal studies suggested shifts toward more eubiotic microbial communities. Gut evidence was limited, although one study suggested modulation of tryptophan metabolism-related pathways. Dupilumab appears to exert compartment-specific and disease-dependent effects on the microbiome. The strongest evidence concerns the skin and sinonasal compartments, whereas gut microbiota changes remain poorly defined. Further prospective studies are needed to assess microbiota signatures as potential biomarkers of response.
18 May 2026
Read appraisal →Current allergy and asthma reports
Pharmacotherapeutic Medication Adherence in Patients With Allergic Rhinitis: A Systematic Review and Meta-Analysis
PURPOSE OF REVIEW: Medication adherence is essential for effective management of allergic rhinitis (AR), yet real-world adherence to guideline-recommended pharmacotherapies remains poorly characterized. This systematic review and meta-analysis aims to estimate pooled adherence proportions to prescribed intranasal corticosteroids and oral antihistamines in adults with AR, further stratified by adherence measurement method and geographic region. RECENT FINDINGS: Twelve studies encompassing 191,103 AR patients were included. Overall pooled adherence to both intranasal corticosteroids and oral antihistamines was 43%, with substantial heterogeneity across studies. Adherence estimates differed significantly by measurement method with self-reported adherence consistently exceeding pharmacy refill-based estimates for both medication classes. Significant geographic variation was also observed, with intranasal corticosteroid adherence ranging from 17% in North America to 61% in Asia, and oral antihistamine adherence ranging from 26% in North America to 48% in Northern and Western Europe. Peters' test did not indicate any significant publication bias and a Leave-One-Out (LOO) sensitivity analysis showed that no single study had a disproportionate effect on the overall effect size. Adherence to pharmacotherapy in AR is suboptimal, highly method-dependent, and varies significantly across geographic regions. These findings suggest that reported adherence rates are not only influenced by patients' behaviour, but also by the methods used to measure adherence and the broader healthcare environment. Integrating methodological rigour with geographic stratification provides more informative benchmarks for clinical practice and highlights the need for context-sensitive strategies to improve real-world AR management. Future research should prioritize standardized adherence measurement and prospective studies in underrepresented regions.
15 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Exploring the role of cold air in airway hyperresponsiveness and asthma diagnostic testing: a systematic review and meta-analysis
BACKGROUND: Asthma is a heterogeneous disease, making uniform diagnosis challenging, resulting in both under- and overdiagnosis. We conducted a systematic review and meta-analysis to summarise the data on diagnostic accuracy of a cold air bronchial challenge test (CACh) in exercise-induced bronchoconstriction and asthma. METHODS: Databases (PubMed, Embase, Web of Science core collection, Cochrane Library and the Cumulative Index to Nursing and Allied Health Literature via EBSCO) were systematically screened for articles evaluating the use of CACh as a diagnostic tool in exercise-induced bronchoconstriction and asthma. A random-effects model was used to calculate the effect on the reduction in forced expiratory volume in 1 s (FEV1) by CACh in healthy controls compared to patients with asthma. Risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-Comparative. This study is registered with PROSPERO (www.crd.york.ac.uk/PROSPERO/ identifier number CRD42021290350). RESULTS: After screening and removal of duplicates, 39 studies were included. Aggregate sensitivity and specificity of a FEV1 decrease ≥10% after CACh were 0.808 and 0.959, respectively. The weighted mean difference in percentage FEV1 decrease after CACh for patients with asthma compared to healthy controls was -17.58. High heterogeneity (I2=99) was mainly explained by differences in reference tests to diagnose asthma. Application of the trim-and-fill method to account for publication bias imputed eight studies, resulting in a notable reduction in heterogeneity. The overall effect (a significant decrease in FEV1 in patients with asthma) remained statistically significant (-8.18, p<0.001). CONCLUSION: Our study shows the high sensitivity and specificity and possible clinical implications of a CACh in the diagnosis of asthma. Further research is needed to confirm this.
15 May 2026
Read appraisal →International health
Atopic dermatitis in Ethiopia: a systematic review and meta-analysis
Despite being a leading contributor to the global burden of skin disease there is no information on the national prevalence of atopic dermatitis in Ethiopia. Therefore, the purpose of this systematic review and meta-analysis was to estimate the pooled national prevalence of atopic dermatitis in Ethiopia. A computerized systematic search using multiple databases was performed in search of relevant English articles from the inception of the databases to 30 July 2023. It was done in accordance with the Preferred Reporting Items for Systematic review and Meta-Analysis (i.e. PRISMA) standard. R and R Studio software were used for statistical analysis. A random-effects model was used for calculating the pooled estimate of the prevalence of atopic dermatitis. Forest plots and tables were used to represent the data. We found 10 full-text observational studies with 25 920 participants suitable for the review after checking for inclusion and exclusion criteria. The overall pooled prevalence of atopic dermatitis in Ethiopia was 12.75% (95% CI 8.32 to 17.17%). The subgroup analysis found that the atopic dermatitis pooled prevalence was 7% (95% CI 3 to 10%), 17% (95% CI 4 to 30%) and 20% (95% CI 15 to 24%) in the Oromia region, Amhara region and Addis Ababa city administration, respectively. This study demonstrates that atopic dermatitis is highly prevalent in Ethiopia, affecting approximately one in eight individuals, with significant geographical variations. To effectively address this condition, it is crucial to implement awareness campaigns, prioritize specialized training for healthcare professionals, allocate resources to high-prevalence areas, establish a national registry for monitoring, develop community support programs and encourage further research, as there are currently few studies on the condition.
11 May 2026
Read appraisal →Chinese medical journal
Artificial intelligence in atopic dermatitis: Current applications and future perspectives.
Atopic dermatitis (AD) is a prevalent and burdensome chronic skin disease, with profound effects on patient quality of life. Advancing its diagnosis and management remains a pressing clinical need. Artificial intelligence holds growing promise in addressing this challenge. This review outlines the current landscape of AD and explores the expanding role of AI in its care-from improving diagnostic accuracy and objective severity scoring to predicting therapeutic outcomes and personalizing treatment strategies. While these technologies are poised to transform dermatology practice, their successful integration will depend on overcoming technical and practical barriers through collaborative, interdisciplinary efforts. Further research and thoughtful implementation are essential to translate AI's potential into better, more precise patient care.
11 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Oscillometry for the diagnosis of asthma in children: a systematic review
BACKGROUND: Diagnosing asthma in children and young people (CYP) remains challenging. Oscillometry is a promising tool and is feasible from 2 years of age. European Respiratory Society (ERS) technical standards and bronchodilator response (BDR) oscillometry thresholds have been published, but diagnostic accuracy is not established. METHODS: We systematically reviewed studies comparing oscillometry and spirometry in CYP under investigation for asthma. Reference standards were positive BDR or positive methacholine challenge test (MCT). Primary aims were to investigate the sensitivity and specificity of current ERS oscillometry thresholds (>40% decrease in resistance at 5 Hz (R 5), >50% increase in reactance at 5 Hz (X 5) or >80% decrease in the area under the reactance curve); secondary aims were to identify oscillometry threshold values optimising both sensitivity and specificity. RESULTS: 11 studies were included; six (n=992 CYP) utilised BDR and five (n=531 CYP) MCT as reference standard. Meta-analysis was not possible due to heterogeneity of results reported. In two studies using current ERS BDR thresholds, zero sensitivity and high specificity (>85%) were observed. In weighted regression analyses of BDR studies, a 17.0% decrease in resistance at 5-6 Hz had sensitivity and specificity of 71.6% (95% CI 69.7-73.7%); a 20.2% increase in X 5 had sensitivity and specificity of 68.6% (95% CI 66.6-70.8%). Similarly, 27.7% increase in R 5 had sensitivity and specificity of 73.6% (95% CI 71.9-75.3%) for MCT. CONCLUSION: Currently recommended ERS thresholds for oscillometry BDR have low sensitivity. Proposed thresholds for defining positive BDR and MCT by oscillometry require prospective validation and adoption of standards for measuring and reporting oscillometry parameters in future diagnostic comparative studies.
3 May 2026
Read appraisal →Journal of investigational allergology & clinical immunology
Future Perspectives for Artificial Intelligence in Allergy: Advances in Diagnosis, Monitoring, and Personalized Care
Artificial intelligence (AI) is significantly transforming the specialty of allergy by offering novel tools for diagnosis, risk prediction, disease monitoring, and personalized care. This narrative review comprehensively explores key advances in the application of AI techniques-expert systems, machine learning, deep learning, and natural language processing-across various allergic diseases such as asthma, anaphylaxis, atopic dermatitis, food allergy, allergic rhinitis, eosinophilic esophagitis, and drug hypersensitivity reactions. Despite these advancements, several challenges persist, including model interpretability, external validation, and ethical and regulatory barriers. Nonetheless, AI holds strong potential to enhance diagnostic efficiency, therapeutic precision, and patient outcomes. This review presents an updated perspective on the state of the art of AI in allergy, highlighting both its achievements and ongoing challenges.
16 Apr 2026
Read appraisal →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 Med
Advancements in artificial intelligence for atopic dermatitis: diagnosis, treatment, and patient management
Atopic dermatitis (AD) is a common and complex skin disease that significantly affects the quality of life of patients. The latest advances in artificial intelligence (AI) technology have introduced new methods for diagnosing, treating, and managing AD. AI has various innovative applications in the diagnosis and treatment of atopic dermatitis, with particular emphasis on its significant benefits in medical diagnosis, treatment monitoring, and patient care. AI algorithms, especially those that use deep learning techniques, demonstrate strong performance in recognizing skin images and effectively distinguishing different types of skin lesions, including common AD manifestations. In addition, artificial intelligence has also shown promise in creating personalized treatment plans, simplifying drug development processes, and managing clinical trials. Despite challenges in data privacy and model transparency, the potential of artificial intelligence in advancing AD care is enormous, bringing the future to precision medicine and improving patient outcomes. This manuscript provides a comprehensive review of the application of AI in the process of AD disease for the first time, aiming to play a key role in the advancement of AI in skin health care and further enhance the clinical diagnosis and treatment of AD.
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
Read appraisal →