Advances in high-throughput sequencing (HTS) have fostered rapid developments in the field of microbiome research, and massive microbiome datasets are now being generated. However, the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field. Here, we systematically summarize the advantages and limitations of microbiome methods. Then, we recommend specific pipelines for amplicon and metagenomic analyses, and describe commonly-used software and databases, to help researchers select the appropriate tools. Furthermore, we introduce statistical and visualization methods suitable for microbiome analysis, including alpha- and beta-diversity, taxonomic composition, difference comparisons, correlation, networks, machine learning, evolution, source tracing, and common visualization styles to help researchers make informed choices. Finally, a step-by-step reproducible analysis guide is introduced. We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.

The purpose of this review is to provide medical researchers, especially those without a bioinformatics background, with an easy-to-understand summary of the concepts and technologies used in microbiome research. First, we define primary concepts such as microbiota, microbiome, and metagenome. Then, we discuss study design schemes, the methods of sample size calculation, and the methods for improving the reliability of research. We emphasize the importance of negative and positive controls in this section. Next, we discuss statistical analysis methods used in microbiome research, focusing on problems with multiple comparisons and ways to compare β-diversity between groups. Finally, we provide step-by-step pipelines for bioinformatics analysis. In summary, the meticulous study design is a key step to obtaining meaningful results, and appropriate statistical methods are important for accurate interpretation of microbiome data. The step-by-step pipelines provide researchers with insights into newly developed bioinformatics analysis methods.

Aim To evaluate the effectiveness of a mobile application‐assisted nurse‐led management model in childhood asthma. Background Studies have shown that a nurse‐led asthma management model can improve asthma outcomes. However, the role of a mobile application‐assisted nurse‐led model in paediatric asthma management has not been studied well. Design A multi‐centre randomized clinical trial. Methods The trial was conducted between March 2017–March 2018. A total of 152 children (6 to 11.9 years old) were enrolled, with 77 children in the experimental group and 75 in the control group. All children received nurse‐led asthma management and other routine treatment measures, including inhaled corticosteroids. Meanwhile, a mobile application was used to manage asthma only for children in the experimental group. Primary outcome was frequency of asthma exacerbations. All outcomes were evaluated twice a month for 12 months. Results Compared with the pre‐enrollment period, frequency of asthma exacerbations decreased in the post‐enrollment period in the two groups, with a greater decrease in the experimental group. Compared with children in the control group, children in the experimental group had better secondary outcomes, such as improved adherence, higher Childhood Asthma Control Test scores, decreased respiratory tract infections, days of antibiotic use, days of school absence, parental work loss, and medical expenses. Conclusion A mobile application‐assisted nurse‐led management model decreased asthma exacerbations and improved secondary outcomes in children with asthma. Further research is needed to verify its validity in larger population samples. Impact Children with asthma benefited from a nurse‐led asthma management model when combined with mobile application. This trial suggested that computer and Internet technologies should be incorporated into nurse‐led asthma strategy in paediatric asthma management. Trial registration: The current trial was registered online with the Chinese Clinical Trial Registry (registration number: ChiCTR1800016726).

Background: Recent studies have suggested that the gut microbiota is altered in children with juvenile idiopathic arthritis (JIA). However, age, sex, and body mass index (BMI) were not matched in the previous studies, and the results are inconsistent. We conducted an age-, sex-, and BMI-matched cross-sectional study to characterize the gut microbiota in children with JIA, and evaluate its potential in clinical prediction. Methods: A total of 40 patients with JIA and 42 healthy controls, ranging from 1 to 16 years, were enrolled in this study. Fecal samples were collected for 16S rDNA sequencing. The data were analyzed using QIIME software and R packages. Specifically, the random forest model was used to identify biomarkers, and the receiver operating characteristic curve and the decision curve analysis were used to evaluate model performance. Results: A total of 39 fecal samples from patients with JIA, and 42 fecal samples from healthy controls were sequenced successfully. The Chao 1 and Shannon-Wiener index in the JIA group were significantly lower than those in the control group, and the Bray-Curtis dissimilarity also differed significantly between the two groups. The relative abundance of 4 genera, Anaerostipes, Dialister, Lachnospira, and Roseburia, decreased significantly in the JIA group compared to those in the control group. The 4 genera included microbes that produce short-chain fatty acids (SCFAs) and were negatively correlated with some rheumatic indices. Moreover, 12 genera were identified as potential biomarkers by using the nested cross-validation function of the random forest. A random forest model constructed using these genera was able to differentiate the patients with JIA from the healthy controls, and the area under the receiver operating characteristic curve was 0.7975. The decision curve analysis indicated that the model had usefulness in clinical practice.