The roles of gut microbiota on the natural course of atopic dermatitis (AD) are not yet fully understood. We investigated whether the composition and function of gut microbiota and short-chain fatty acids (SCFAs) at 6 months of age could affect the natural course of AD up to 24 months in early childhood. Fecal samples from 132 infants were analyzed using pyrosequencing, including 84 healthy controls, 22 transient AD and 26 persistent AD subjects from the Cohort for Childhood Origin of Asthma and Allergic Diseases (COCOA) birth cohort. The functional profile of the gut microbiome was analyzed by whole-metagenome sequencing. SCFAs were measured using gas chromatography-mass spectrometry. Low levels of Streptococcus and high amounts of Akkermansia were evident in transient AD cases, and low Clostridium, Akkermansia and high Streptococcus were found in children with persistent AD. The relative abundance of Streptococcus positively correlated with scoring of AD (SCORAD) score, whereas that of Clostridium negatively correlated with SCORAD score. The persistent AD group showed decreased gut microbial functional genes related to oxidative phosphorylation compared with healthy controls. Butyrate and valerate levels were lower in transient AD infants compared with healthy and persistent AD infants. Compositions, functions and metabolites of the early gut microbiome are related to natural courses of AD in infants.

Mycoplasma pneumoniae is a major causative pathogen of community-acquired pneumonia in children, and the treatment of choice is macrolides. There is an increasing trend in reports of refractory clinical responses despite macrolide treatment due to the emergence of macrolide-resistant M. pneumoniae. Early discrimination of macrolide-refractory M. pneumoniae pneumonia (MrMP) from macrolide-sensitive M. pneumoniae pneumonia (MSMP) is vital; however, testing for macrolide susceptibility at the time of admission is not feasible. This study aimed to identify the characteristics of MrMP in Korean children, in comparison with those of MSMP. In this multicenter study, board-certified pediatric pulmonologists at 22 tertiary hospitals reviewed the medical records from 2010 to 2015 of 5294 children who were hospitalized with M. pneumoniae pneumonia and administered macrolides as the initial treatment. One-way analysis of variance and the Kruskal-Wallis test were used to compare differences between groups. Of 5294 patients (mean age, 5.6 years) included in this analysis, 240 (4.5%), 925 (17.5%), and 4129 (78.0%) had MrMP, macrolide-less effective M. pneumoniae pneumonia, and MSMP, respectively. Compared with the MSMP group, the MrMP group had a longer fever duration, overall (13.0 days) and after macrolide use (8.0 days). A higher proportion of MrMP patients had respiratory distress, pleural effusion, and lobar pneumonia. The mean aspartate aminotransferase, alanine aminotransferase, lactate dehydrogenase, and C-reactive protein levels were the highest in the MrMP group, along with higher incidences of extrapulmonary manifestations and atelectasis (during and post infection). Pre-existing conditions were present in 17.4% (n = 725/4159) of patients, with asthma being the most common (n = 334/4811, 6.9%). This study verified that MrMP patients show more severe initial radiographic findings and clinical courses than MSMP patients. MrMP should be promptly managed by agents other than macrolides.

Auscultation, a cost-effective and non-invasive part of physical examination, is essential to diagnose pediatric respiratory disorders. Electronic stethoscopes allow transmission, storage, and analysis of lung sounds. We aimed to develop a machine learning model to classify pediatric respiratory sounds. Lung sounds were digitally recorded during routine physical examinations at a pediatric pulmonology outpatient clinic from July to November 2019 and labeled as normal, crackles, or wheezing. Ensemble support vector machine models were trained and evaluated for four classification tasks (normal vs. abnormal, crackles vs. wheezing, normal vs. crackles, and normal vs. wheezing) using K-fold cross-validation (K = 10). Model performance on a prospective validation set (June to July 2021) was compared with those of pediatricians and non-pediatricians. Total 680 clips were used for training and internal validation. The model accuracies during internal validation for normal vs. abnormal, crackles vs. wheezing, normal vs. crackles, and normal vs. wheezing were 83.68%, 83.67%, 80.94%, and 90.42%, respectively. The prospective validation (n = 90) accuracies were 82.22%, 67.74%, 67.80%, and 81.36%, respectively, which were comparable to pediatrician and non-pediatrician performance. An automated classification model of pediatric lung sounds is feasible and maybe utilized as a screening tool for respiratory disorders in this pandemic era.

Park HJ, et al. Korean J Pediatr. 2015 Nov;58(11):454-458. http://dx.doi.org/10.3345/kjp.2015.58.11.454