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Background Nocturnal gastro-oesophageal reflux (nGER) is common in people with respiratory diseases, but its association with pulmonary abnormalities is not known.Aim Investigate the association between nGER and pulmonary abnormalities on chest CT in an adult general population.Methods In total, 28 846 individuals from the general population aged 50-64 years completed questionnaires and underwent chest CT, in the Swedish CArdioPulmonary BioImage Study (www.scapis.org). Participants with nGER symptoms on >= 1 night per week were defined as having nGER. Chest CT was evaluated for bronchial wall thickening, bronchiectasis, reticular abnormalities, honeycombing, cysts and ground glass opacities. Ever-smoking, current asthma, inflammatory bowel disease and autoimmune disease were defined as risk factors for pulmonary abnormalities. Analyses were adjusted for sex, age, body mass index, education level and study centre.Results The prevalence of nGER was 9.4%. Among participants with risk factors for pulmonary abnormalities (n=4004), having nGER was positively associated with bronchial wall thickening (adjusted OR (aOR) (95% CI): 1.25 (1.07 to 1.48)) and reticular abnormalities (aOR (95% CI): 1.51 (1.04 to 2.17)), but negatively associated with cysts (aOR (95% CI): 0.68 (0.48 to 0.97)). Among participants without risk factors for CT abnormalities (n=2555), nGER did not relate with pulmonary abnormalities.Conclusions In a middle-aged general population, nGER was not associated with pulmonary abnormalities on chest CT. However, in the presence of other risk factors for pulmonary abnormalities, nGER was associated with bronchial wall thickening and reticular abnormalities. Persons with nGER and risk factors for pulmonary abnormalities should, therefore, be evaluated for respiratory disease and treated appropriately.

Brain tumors are the most common solid tumors in children and young adults, but the scarcity of large histopathology datasets has limited the application of computational pathology in this group. This study implements two weakly supervised multiple-instance learning (MIL) approaches on patch features obtained from state-of-the-art histology-specific foundation models to classify pediatric brain tumors in hematoxylin and eosin whole slide images (WSIs) from a multi-center Swedish cohort. WSIs from 540 subjects (age 8.5 ± 4.9 years) diagnosed with brain tumors were gathered from the six Swedish university hospitals. Instance (patch)-level features were obtained from WSIs using three pre-trained feature extractors: ResNet50, UNI, and CONCH. Instances were aggregated using attention-based MIL (ABMIL) or clustering-constrained attention MIL (CLAM) for patient-level classification. Models were evaluated on three classification tasks based on the hierarchical classification of pediatric brain tumors: tumor category, family, and type. Model generalization was assessed by training on data from two of the centers and testing on data from four other centers. Model interpretability was evaluated through attention mapping. The highest classification performance was achieved using UNI features and ABMIL aggregation, with Matthew's correlation coefficient of 0.76 ± 0.04, 0.63 ± 0.04, and 0.60 ± 0.05 for tumor category, family, and type classification, respectively. When evaluating generalization, models utilizing UNI and CONCH features outperformed those using ResNet50. However, the drop in performance from the in-site to out-of-site testing was similar across feature extractors. These results show the potential of state-of-the-art computational pathology methods in diagnosing pediatric brain tumors at different hierarchical levels with fair generalizability on a multi-center national dataset.