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Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.
Eur Respir Rev
January 2025
Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.
Introduction: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the methodology.
Methods: 10 databases were searched.
Registries in allergy: Structure, target groups, and key findings of allergy-focused registries in Germany.
Allergol Select
December 2024
Division of Allergy and Immunology, Department of Dermatology, Venerology and Allergology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
In allergology, clinical registries fill knowledge gaps of epidemiology, mechanisms of allergic diseases, and real-world treatment outcomes. Considering the continuous rise of allergic diseases worldwide, registries become increasingly important for the optimization and harmonization of patient care. In the current review, we present four ongoing allergy-focused registries initiated in Germany.
View Article and Find Full Text PDFDetermining the role of basophil activation testing in reported type 1 allergy to beta-lactam antibiotics.
Front Allergy
December 2024
Department of Dermatology, Eberhard Karls University, Tuebingen, Germany.
Background: Allergy to beta-lactam antibiotics (BLA), especially to penicillin, is the most commonly reported drug allergy by patients. Alternative antibiotics can yield negative consequences, such as extended hospitalization days due to less efficacy and overall higher costs. The basophil activation test (BAT) is an assay, in which activation of an individual's own basophils is quantified by flow cytometry.
View Article and Find Full Text PDFMachine learning of endoscopy images to identify, classify, and segment sinonasal masses.
Int Forum Allergy Rhinol
January 2025
Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, California, USA.
Background: We developed and assessed the performance of a machine learning model (MLM) to identify, classify, and segment sinonasal masses based on endoscopic appearance.
Methods: A convolutional neural network-based model was constructed from nasal endoscopy images from patients evaluated at an otolaryngology center between 2013 and 2024. Images were classified into four groups: normal endoscopy, nasal polyps, benign, and malignant tumors.
Awareness of anaphylaxis among public in Al-Ahsa City of Saudi Arabia: A cross-sectional study.
PLoS One
January 2025
Respiratory Service, King Abdulaziz Medical City, Riyadh, Saudi Arabia.
Background: Anaphylaxis is a severe, life-threatening allergic reaction requiring prompt treatment with epinephrine. However, gaps in public understanding exist globally. To guide future education efforts, this study assessed anaphylaxis awareness among adults in Al-Ahsa, Saudi Arabia.
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