Stability of multiple testing procedures, defined as the standard deviation of total number of discoveries, can be used as an indicator of variability of multiple testing procedures. Improving stability of multiple testing procedures can help to increase the consistency of findings from replicated experiments. Benjamini-Hochberg’s and Storey’s q-value procedures are two commonly used multiple testing procedures for controlling false discoveries in genomic studies. Storey’s q-value procedure has higher power and lower stability than Benjamini-Hochberg’s procedure. To improve upon the stability of Storey’s q-value procedure and maintain its high power in genomic data analysis, we propose a new multiple testing procedure, named Bon-EV, to control false discovery rate (FDR) based on Bonferroni’s approach. Simulation studies show that our proposed Bon-EV procedure can maintain the high power of the Storey’s q-value procedure and also result in better FDR control and higher stability than Storey’s q-value procedure for samples of large size(30 in each group) and medium size (15 in each group) for either independent, somewhat correlated, or highly correlated test statistics. When sample size is small (5 in each group), our proposed Bon-EV procedure has performance between the Benjamini-Hochberg procedure and the Storey’s q-value procedure. Examples using RNA-Seq data show that the Bon-EV procedure has higher stability than the Storey’s q-value procedure while maintaining equivalent power, and higher power than the Benjamini-Hochberg’s procedure. For medium or large sample sizes, the Bon-EV procedure has improved FDR control and stability compared with the Storey’s q-value procedure and improved power compared with the Benjamini-Hochberg procedure. The Bon-EV multiple testing procedure is available as the BonEV package in R for download at https://CRAN.R-project.org/package=BonEV .

Background Exposure to visual posts featuring e-cigarette products on social media is associated with increased e-cigarette use among US adolescents. Instagram is the largest source of e-cigarette social media marketing, where influencers—for example, bloggers, brand ambassadors—post promotional materials. This study analysed the network of e-cigarette brands and influencers on Instagram, characterising the most central players in e-cigarette social media marketing. Methods We tracked influencers with public profiles on Instagram who posted promotional e-cigarette content in 2020, had over 1000 followers and high user engagement rate (ratio of likes and comments to followers) of 1%–25% per post. By conducting a social network analysis, we identified the most central (highly involved in promotional activities) influencers and e-cigarette brands. The number of the influencers’ followers aged 13–17 years old and the age verification practices restricting youth access were also assessed. Results There is a highly interconnected network of engaging e-cigarette influencers (n=55) worldwide who collaborated with over 600 e-cigarette brands in 2020. The Asian and US influencers had five to six times more teenage followers compared with the European influencers. 75% of the influencers did not restrict youth access to their promotional content on Instagram. The brands Voopotech, Innokin, Geekvape, Lost Vape, Smok and Vaporesso collaborated with the largest number of influencers (mean n=20). Conclusions It is important to understand associations among influencers and e-cigarette use behaviours, especially youth, to inform effective public health communication and potential policies that could regulate social media marketing sponsored by e-cigarette companies.

Extracellular vesicles (EVs) play a vital role in normal lung physiology to maintain homeostasis in the airways via intercellular communication. EVs include exosomes and microvesicles, and are characterized by their phospholipid bilayers. EVs have been recognized as novel circulating biomarkers of disease, which are released by different cell types. In this study, we used different EV isolation and purification methods to characterize the plasma‐derived EV miRNAs from non‐smokers, smokers and patients with COPD. A small RNA sequencing (RNA‐seq) approach was adapted to identify novel circulating EV miRNAs. We found that plasma‐derived EVs from non‐smokers, smokers and patients with COPD vary in their size, concentration, distribution and phenotypic characteristics as confirmed by nanoparticle tracking analysis, transmission electron microscopy, and immunoblot analysis of EV surface markers. RNA‐seq analysis confirmed the most abundant types of small RNAs, such as miRNAs, tRNAs, piRNAs snRNAs, snoRNAs and other biotypes in plasma‐derived EVs. We mainly focused on miRNAs as novel biomarkers in smokers and patients with COPD for further analysis. Differential expression by DESeq2 identified distinct miRNA profiles (up‐regulated: miR‐22‐3p, miR‐99a‐5p, miR‐151a‐5p, miR‐320b, miR‐320d; and down‐regulated: miR‐335‐5p, miR‐628‐3p, miR‐887‐5p and miR‐937‐3p) in COPD versus smokers or non‐smokers in a pairwise comparison. Gene set enrichment analysis (GSEA) of differentially expressed miRNAs revealed the top pathways, gene ontology and diseases associated with smokers and patients with COPD. We selectively validated miRNAs in EVs isolated from BEAS‐2B cells treated with cigarette smoke extract by quantitative PCR analysis. For the first time, we report that plasma‐derived EV miRNAs are novel circulating pulmonary disease biomarkers. Thus, molecular profiling of EV miRNAs has great translational potential for the development of biomarkers that may be used in the diagnosis, prognosis, and therapeutics of COPD.

Background Wheezing is a symptom of potential respiratory disease and known to be associated with smoking. Electronic cigarette use (‘vaping’) has increased exponentially in recent years. This study examined the cross-sectional association of vaping with wheezing and related respiratory symptoms and compare this association with smokers and dual users. Methods The Population Assessment of Tobacco and Health study wave 2 data collected from October 2014 to October 2015 with 28 171 adults were used. The cross-sectional association of vaping with self-reported wheezing and related respiratory symptoms relative to smokers and dual users of tobacco and electronic cigarettes were studied using multivariable logistic and cumulative logistic regression models with consideration of complex sampling design. Results Among the 28 171 adult participants, 641 (1.2%) were current vapers who used e-cigarettes exclusively, 8525 (16.6%) were current exclusive smokers, 1106 (2.0%) were dual users and 17 899 (80.2%) were non-users. Compared with non-users, risks of wheezing and related respiratory symptoms were significantly increased in current vapers (adjusted OR (aOR)=1.67, 95% CI: 1.23 to 2.15). Current vapers had significantly lower risk in wheezing and related respiratory symptoms compared with current smokers (aOR=0.68, 95% CI: 0.53 to 0.87). No significant differences were found between dual users and current smokers in risk of wheezing and related respiratory symptoms (aOR=1.06, 95% CI: 0.91 to 1.24). Conclusions Vaping was associated with increased risk of wheezing and related respiratory symptoms. Current vapers had lower risk in wheezing and related respiratory symptoms than current smokers or dual users but higher than non-users. Both dual use and smoking significantly increased the risk of wheezing and related respiratory symptoms.

RNA-seq is a high-throughput sequencing technology widely used for gene transcript discovery and quantification under different biological or biomedical conditions. A fundamental research question in most RNA-seq experiments is the identification of differentially expressed genes among experimental conditions or sample groups. Numerous statistical methods for RNA-seq differential analysis have been proposed since the emergence of the RNA-seq assay. To evaluate popular differential analysis methods used in the open source R and Bioconductor packages, we conducted multiple simulation studies to compare the performance of eight RNA-seq differential analysis methods used in RNA-seq data analysis (edgeR, DESeq, DESeq2, baySeq, EBSeq, NOISeq, SAMSeq, Voom). The comparisons were across different scenarios with either equal or unequal library sizes, different distribution assumptions and sample sizes. We measured performance using false discovery rate (FDR) control, power, and stability. No significant differences were observed for FDR control, power, or stability across methods, whether with equal or unequal library sizes. For RNA-seq count data with negative binomial distribution, when sample size is 3 in each group, EBSeq performed better than the other methods as indicated by FDR control, power, and stability. When sample sizes increase to 6 or 12 in each group, DESeq2 performed slightly better than other methods. All methods have improved performance when sample size increases to 12 in each group except DESeq. For RNA-seq count data with log-normal distribution, both DESeq and DESeq2 methods performed better than other methods in terms of FDR control, power, and stability across all sample sizes. Real RNA-seq experimental data were also used to compare the total number of discoveries and stability of discoveries for each method. For RNA-seq data analysis, the EBSeq method is recommended for studies with sample size as small as 3 in each group, and the DESeq2 method is recommended for sample size of 6 or higher in each group when the data follow the negative binomial distribution. Both DESeq and DESeq2 methods are recommended when the data follow the log-normal distribution.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet