23 results
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D2.151 - HZ-Y030, an Oral STAT6 Degrader, Attenuates Type 2 Inflammation in Preclinical Asthma Models

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D2.152 - Clinical and functional characteristics of patients with suspected epoxy resin-related occupational asthma

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D2.155 - Mapping the Architecture and Spectrum of the Cough Hypersensitivity Questionnaire Across Respiratory Diseases

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D2.158 - Circulating 1-methylnicotinamide can predict the treatment response of dupilumab in adult asthma

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D2.157 - Clinical remission with tezepelumab in severe asthma: prevalence and predictors in a real-world multicentre study

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D2.159 - Idiopathic Chronic Eosinophilic Pneumonia: A Case Series

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D2.160 - Household Overcrowding and Number of Siblings as Risk Factors for Uncontrolled Asthma in Inner-City Children

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D2.161 - Exploring the Early-Life Interplay Between Respiratory Health and Neurodevelopmental disorders

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D3.126 - Evaluation of Severe Pediatric Asthma Response to Biologics Using the Pediatric CONFiRM Score: A Real-Life Cohort Analysis

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D3.129 - Evaluation of Cardiovascular Disease Risk and Associated Factors in Childhood Asthma

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D3.132 - The impact of biological therapy on the quality of life of patients with severe asthma

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D3.135 - Real World Efficacy of Benralizumab Treatment in Severe Eosinophilic Asthma: A Single-Center Experience

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D3.137 - The “Street Cat” Paradox: Low Prevalence but High Disease Burden of Mouse Sensitization in Pediatric Asthma

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D3.138 - Can FeNO predict lung function decline in asthma? A 5-year multicenter cohort study

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D3.139 - Asthma control and medication adherence in patients with asthma using preferred versus alternative GINA treatment tracks - a real-world observational study

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D3.485 - Prevalence of Ambrosia artemisiifolia in Italy: Bioinformatics and Botanical Aerobiology Analysis

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D3.478 - Microsplenia: Spleen Size Reduction as a Comprehensive Biomarker for Immune-Related Diseases, Disease Monitoring, and Treatment Response

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D3.479 - Association between Mitochondrial DNA Copy Number and Lung Function in Schoolchildren

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D3.481 - Machine Learning–Based Plasma Proteomic Signatures for Prediction of Chronic Rhinosinusitis

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D3.482 - Harnessing the Baseline Nasal Microbiome to Predict Pollen Related Symptom Expression