Background: Early interactions between respiratory viruses and microbiota mightmodulate host immune responses and subsequently contribute to later development of recurrent wheezing and asthma in childhood. We aimed to study the possible association between respiratory microbiome, host immune response, and the development of recurrent wheezing in infants with severe respiratory syncytial virus (RSV) bronchiolitis.Methods: Seventy-four infants who were hospitalized at Beijing Children's Hospital during an initial episode of severe RSV bronchiolitis at 6 months of age or less were included and followed up until the age of 3 years. Sputum samples were collected, and their microbiota profiles, LPS, and cytokines were analyzed by 16S rRNA-based sequencing, ELISA, and multiplex immunoassay, respectively.Results: Twenty-six (35.1%) infants developed recurrent wheezing by the age of 3 years, and 48 (64.9%) did not. The relative abundance of Haemophilus, Moraxella, and Klebsiella was higher in infants who later developed recurrent wheezing than in those who did not (LDA score >3.5). Airway levels of LPS (P = .003), CXCL8 (P = .004), CCL5 (P = .029), IL-6 (P = .004), and IL-13 (P < .001) were significantly higher in infants who later developed recurrent wheezing than in those who did not. Moreover, high airway abundance of Haemophilus was associated with CXCL8 (r = 0.246, P = .037) level, and that of Moraxella was associated with IL-6 level (r = 0.236, P = .046) and IL-10 level (r = 0.266, P = .024). Conclusion:Our study suggests that higher abundance of Haemophilus and Moraxella in airway microbiome might modulate airway inflammation during severe RSV bronchiolitis in infancy, potentially contributing to the development of subsequent recurrent wheezing in later childhood. K E Y W O R D S airway inflammation, children, lipopolysaccharide, recurrent wheezing, respiratory microbiome, respiratory syncytial virus S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Zhang X, Zhang X, Zhang N, et al. Airway microbiome, host immune response and recurrent wheezing in infants with severe respiratory syncytial virus bronchiolitis. Pediatr Allergy Immunol. 2020;31:281-289.

We aimed to use serum metabolomics to discriminate infants with severe respiratory syncytial virus (RSV) bronchiolitis who later developed subsequent recurrent wheezing from those who did not and to investigate the relationship between serum metabolome and host immune responses with regard to the subsequent development of recurrent wheezing. Fifty‐one infants who were hospitalized during an initial episode of severe RSV bronchiolitis at 6 months of age or less were included and followed for up to the age of 3 years. Of them, 24 developed subsequent recurrent wheezing and 27 did not. Untargeted serum metabolomics was performed by ultraperformance liquid chromatography coupled with high‐resolution mass spectrometry (UPLC‐MS/MS). Cytokines were measured by multiplex immunoassay. Difference in serum metabolomic profiles was observed between infants who developed recurrent wheezing and those who did not. L‐lactic acid level was significantly higher in infants with recurrent wheezing than those without. Pyrimidine metabolism, glycerophospholipid metabolism, and arginine biosynthesis were identified as the most significant changed pathways between the two groups. Moreover, L‐lactic acid level was positively associated with serum CXCL8 level. This exploratory study showed that differential serum metabolic signatures during severe RSV bronchiolitis in early infancy were associated with the development of subsequent recurrent wheezing in later childhood.

Objectives: To identify the value of radiomics method derived from CT images to predict prognosis in patients with COVID-19. Methods: A total of 40 patients with COVID-19 were enrolled in the study. Baseline clinical data, CT images, and laboratory testing results were collected from all patients. We defined that ROIs in the absorption group decreased in the density and scope in GGO, and ROIs in the progress group progressed to consolidation. A total of 180 ROIs from absorption group (n = 118) and consolidation group (n = 62) were randomly divided into a training set (n = 145) and a validation set (n = 35) (8:2). Radiomics features were extracted from CT images, and the radiomics-based models were built with three classifiers. A radiomics score (Rad-score) was calculated by a linear combination of selected features. The Rad-score and clinical factors were incorporated into the radiomics nomogram construction. The prediction performance of the clinical factors model and the radiomics nomogram for prognosis was estimated. Results: A total of 15 radiomics features with respective coefficients were calculated. The AUC values of radiomics models (kNN, SVM, and LR) were 0.88, 0.88, and 0.84, respectively, showing a good performance. The C-index of the clinical factors model was 0.82 [95% CI (0.75–0.88)] in the training set and 0.77 [95% CI (0.59–0.90)] in the validation set. The radiomics nomogram showed optimal prediction performance. In the training set, the C-index was 0.91 [95% CI (0.85–0.95)], and in the validation set, the C-index was 0.85 [95% CI (0.69–0.95)]. For the training set, the C-index of the radiomics nomogram was significantly higher than the clinical factors model (p = 0.0021). Decision curve analysis showed that radiomics nomogram outperformed the clinical model in terms of clinical usefulness. Conclusions: The radiomics nomogram based on CT images showed favorable prediction performance in the prognosis of COVID-19. The radiomics nomogram could be used as a potential biomarker for more accurate categorization of patients into different stages for clinical decision-making process. Advances in knowledge: Radiomics features based on chest CT images help clinicians to categorize the patients of COVID-19 into different stages. Radiomics nomogram based on CT images has favorable predictive performance in the prognosis of COVID-19. Radiomics act as a potential modality to supplement conventional medical examinations.

Importance Nasal nitric oxide ( nNO ) testing is a method used in the diagnosis of primary ciliary dyskinesia ( PCD ). It has not been evaluated in Chinese population. Objective To establish a reference nNO value to assist in the diagnosis of PCD in Chinese children. Methods nNO values were measured in children with PCD ( n = 36), cystic fibrosis ( CF ) ( n = 20), asthma ( n = 45), post‐infectious bronchiolitis obliterans ( BO ) ( n = 41) and non‐ PCD /non‐ CF bronchiectasis ( n = 32). The receiver operating characteristic nNO value for the diagnosis of PCD was plotted and the area under the curve was calculated. Results nNO values were significantly lower in children with PCD (median 25.66 nL /min) than in children with asthma (186.26 ± 58.95 nL / min), BO (143.47 ± 49.71 nL /min) and non‐ PCD /non‐ CF bronchiectasis (173.13 ± 63.80 nL /min), but not in children with CF (90.90 ± 43.20 nL /min). Notably however, no CF patient had an nNO value < 45 nL /min. A cut‐off of 76 nL /min yielded the best sensitivity of 86.1%, and specificity of 91.4%, with an area under the curve of 0.920 (95% confidence interval 0.859–0.981) for the diagnosis of PCD . If CF was ruled out the specificity increased to nearly 100%. Interpretation nNO testing is able to discriminate between patients with PCD and those with CF , asthma, post‐infectious BO and non‐ PCD /non‐ CF bronchiectasis. A cut‐off of 76 nL /min could be further examined in patients suspected of PCD , to establish an nNO reference value for PCD screening in Chinese children.