OBJECTIVE: To analyze the characteristics of genetic variants among children with refractory epilepsy (RE). METHODS: One hundred and seventeen children with RE who had presented at the Affiliated Jinhua Hospital of Zhejiang University School of Medicine from January 1, 2018 to November 21, 2019 were selected as the study subjects. The children were divided into four groups according to their ages of onset: < 1 year old, 1 ~ 3 years old, 3 ~ 12 years old, and >= 12 years old. Clinical data and results of trio-whole exome sequencing were retrospectively analyzed. RESULTS: In total 67 males and 50 females were included. The age of onset had ranged from 4 days to 14 years old. Among the 117 patients, 33 (28.21%) had carried pathogenic or likely pathogenic variants. The detection rates for the < 1 year old, 1 ~ 3 years old and >= 3 years old groups were 53.85% (21/39), 12.00% (3/25) and 16.98% (9/53), respectively, with a significant difference among the groups (χ² = 19.202, P < 0.001). The detection rates for patients with and without comorbidities were 33.33% (12/36) and 25.93% (21/81), respectively (χ² = 0.359, P = 0.549). Among the 33 patients carrying genetic variants, 27 were single nucleotide polymorphisms (SNPs) or insertion/deletions (InDels), and 6 were copy number variations (CNVs). The most common mutant genes were PRRT2 (15.15%, 5/33) and SCN1A (12.12%, 4/33). Among children carrying genetic variants, 72.73% (8/11) had attained clinical remission after adjusting the medication according to the references. CONCLUSION 28.21% of RE patients have harbored pathogenic or likely pathogenic variants or CNVs. The detection rate is higher in those with younger age of onset. PRRT2 and SCN1A genes are more commonly involved. Adjusting medication based on the types of affected genes may facilitate improvement of the remission rate.
Infant ; Female ; Male ; Humans ; Child ; Infant, Newborn ; Child, Preschool ; DNA Copy Number Variations ; Drug Resistant Epilepsy/genetics* ; Retrospective Studies ; Polymorphism, Single Nucleotide

Background Noise is the most common occupational hazard in the automobile manufacturing industry with the most workers exposed. Automobile manufacturing industry is a high-risk industry for noise-induced hearing loss. Objective To understand the epidemiological characteristics of noise-induced hearing loss among workers in automobile manufacturing industry and explore related influencing factors. Methods A questionnaire survey, individual noise recording, and pure tone audiometry were conducted among workers (n=656) exposed to noise from five automobile manufacturing enterprises. The data on age, sex, exposure duration, noise intensity, kurtosis, and hearing loss were obtained. The positive rates of high-frequency noise-induced hearing loss (HFNIHL) and speech-frequency noise-induced hearing loss (SFNIHL) were calculated, and each factor was compared between workers with and without HFNIHL. Chi-square test and analysis of trend were conducted among different groups of age, sex, exposure duration, A-weighted equivalent continuous sound pressure level normalized to a nominal 8-hour working day (L_Aeq,8h), and kurtosis. Logistic regression analysis was conducted to analyze the factors influencing the positive rates of HFNIHL and SFNIHL. Results The exposure rates of non-Gaussian noise was 73.6%. The positive rates of HFNIHL and SFNIHL were 32.6% (214 workers) and 6.7% (44 workers), respectively. The HFNIHL workers showed older age, higher proportion of male, longer exposure duration, higher noise intensity (L_{Aeq,8 h}), and increased kurtosis than those without HFNIHL (P<0.05). The positive rates of HFNIHL increased with the increase of age, exposure duration, L_{Aeq,8 h}, and kurtosis (\begin{document}$ {\chi ^2} $\end{document}_trend-age=49.25, P<0.001; \begin{document}$ {\chi ^2} $\end{document}_{trend-duration}=22.19, P<0.001; \begin{document}$ {\chi ^2} $\end{document}_trend-LAeq=6.91, P=0.009; \begin{document}$ {\chi ^2} $\end{document}_{trend-kurtosis}=8.56, P=0.003). The results of logistic regression showed that age (OR=2.13, 95%CI: 1.67-2.71, P<0.001), sex (OR=2.29, 95%CI: 1.44-3.62, P<0.001), exposure duration (OR=1.43, 95%CI: 1.11-1.85, P=0.006), L_Aeq,8h (OR=1.37, 95%CI: 1.08~1.76, P=0.011), and kurtosis (OR=1.37, 95%CI: 1.14-1.63, P=0.001) were factors associated with the risk of HFNIHL, while only age was associated with the risk of SFNIHL (OR=2.15, 95%CI: 1.33-3.33, P=0.001). Conclusion Workers exposed to noise in automobile manufacturing industry are at a high risk of hearing loss. Age, sex, exposure duration, L_{Aeq,8 h}, and kurtosis are key influencing factors of hearing loss.

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.