E-cigarette use and respiratory symptoms in residents of the United States : A BRFSS report

Publisher Copyright: Copyright: © 2022 Varella et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Purpose E-cigarettes are the most common type of electronic nicotine delivery system in the United States. E-cigarettes contain numerous toxic compounds that has been shown to induce severe structural damage to the airways. The objective of this study is to assess if there is an association between e-cigarette use and respiratory symptoms in adults in the US as reported in the BRFSS. Methods We analyzed data from 18,079 adults, 18–44 years, who participated at the Behavioral Risk Factor Surveillance System (BRFSS) in the year 2017. E-cigarette smoking status was categorized as current everyday user, current some days user, former smoker, and never smoker. The frequency of any respiratory symptoms (cough, phlegm, or shortness of breath) was compared. Unadjusted and adjusted logistic regression analysis were used to calculate odds ratios (OR) and 95% confidence intervals (CI). Results The BRFSS reported prevalence of smoking e-cigarettes was 6%. About 28% of the participants reported any of the respiratory symptoms assessed. The frequency of reported respiratory symptoms was highest among current some days e-cigarette users (45%). After adjusting for selected participant’s demographic, socio-economic, and behavioral characteristics, and asthma and COPD status, the odds of reporting respiratory symptoms increased by 49% among those who use e-cigarettes some days (OR 1.49; 95% CI: 1.06–2.11), and by 29% among those who were former users (OR 1.29; 95% CI: 1.07–1.55) compared with those who never used e-cigarettes. No statistically significant association was found for those who used e-cigarettes every day (OR 1.41; 95% CI 0.96–2.08). Conclusion E-cigarettes cannot be considered as a safe alternative to aid quitting use of combustible traditional cigarettes. Cohort studies may shed more evidence on the association between e-cigarette use and respiratory diseases.Peer reviewe

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets. BárbaraAvelar-Pereira 9 , EthanRoy2 , Valerie J.Sydnor3,4,5, JasonD.Yeatman1,2, The Fibr Community Science Consortium*, TheodoreD.Satterthwaite3,4,5,88 & Ariel Roke

Turbo FISH: a method for rapid single molecule RNA FISH.

Advances in RNA fluorescence in situ hybridization (RNA FISH) have allowed practitioners to detect individual RNA molecules in single cells via fluorescence microscopy, enabling highly accurate and sensitive quantification of gene expression. However, current methods typically employ hybridization times on the order of 2-16 hours, limiting its potential in applications like rapid diagnostics. We present here a set of conditions for RNA FISH (dubbed Turbo RNA FISH) that allow us to make accurate measurements with no more than 5 minutes of hybridization time and 3 minutes of washing, and show that hybridization times can go as low as 30 seconds while still producing quantifiable images. We further show that rapid hybridization is compatible with our recently developed iceFISH and SNP FISH variants of RNA FISH that enable chromosome and single base discrimination, respectively. Our method is simple and cost effective, and has the potential to dramatically increase the throughput and realm of applicability of RNA FISH

Breaking reflection symmetry: evolving long dynamical cycles in Boolean systems

In interacting dynamical systems, specific local interaction rules for system components give rise to diverse and complex global dynamics. Long dynamical cycles are a key feature of many natural interacting systems, especially in biology. Examples of dynamical cycles range from circadian rhythms regulating sleep to cell cycles regulating reproductive behavior. Despite the crucial role of cycles in nature, the properties of network structure that give rise to cycles still need to be better understood. Here, we use a Boolean interaction network model to study the relationships between network structure and cyclic dynamics. We identify particular structural motifs that support cycles, and other motifs that suppress them. More generally, we show that the presence of dynamical reflection symmetry in the interaction network enhances cyclic behavior. In simulating an artificial evolutionary process, we find that motifs that break reflection symmetry are discarded. We further show that dynamical reflection symmetries are over-represented in Boolean models of natural biological systems. Altogether, our results demonstrate a link between symmetry and functionality for interacting dynamical systems, and they provide evidence for symmetry’s causal role in evolving dynamical functionality

Demonstration of Turbo iceFISH.

<p>We performed Turbo FISH using iceFISH probes that targeted a total of 20 introns in genes on chromosome 19 (right panels), while simultaneously performing RNA FISH for TOP2A mRNA (left panels). We compared both Turbo FISH to conventional RNA FISH performed overnight (top vs. bottom panels). All images are maximum projections of a stack of optical sections encompassing the three-dimensional volume of the cell. DAPI (nuclear stain) is in blue.</p

Comparison of fixation conditions for both traditional overnight hybridizations and rapid hybridization.

<p>A. Comparison of number of spots detected and cumulative distribution functions for the <i>TBCB</i> gene with probes labeled with the Alexa 594 fluorophore. Error bars represent the standard error of the mean. No statistically significant differences exist between the overnight RNA FISH samples. Turbo RNA FISH for <i>TBCB</i> gene on formaldehyde-fixed cells is statistically different from Turbo RNA FISH on methanol- and ethanol-fixed cells (p = 3.82×10⁻⁶⁵ and p = 4.89×10⁻⁹⁶, respectively; two-tailed t-test). For all conditions, we analyzed spot counts on 100–150 cells. B. Comparison of number of spots detected and cumulative distribution functions for the <i>TOP2A</i> gene with probes labeled with the Cy3 fluorophore. Error bars represent the standard error of the mean. Overnight RNA FISH for <i>TOP2A</i> gene on formaldehyde-fixed cells is statistically different from overnight RNA FISH on ethanol-fixed cells (p = 0.0067; two tailed t-test). No other statistically significant differences exist between overnight RNA FISH samples. Turbo RNA FISH for <i>TOP2A</i> gene on formaldehyde-fixed cells is statistically different from Turbo RNA FISH on methanol- and ethanol-fixed cells (p = 9.57×10⁻²⁸ and p = 4.22×10⁻³⁰, respectively; two-tailed t-test). For all conditions, we analyzed spot counts on 100–150 cells. Data shown represents one of two replicate experiments.</p

Quantification of signal quality and comparison of different hybridization times and probe concentrations.

<p>A. Schematic depicting the manner in which we quantify signal quality via threshold sensitivity. B. Sensitivity of threshold measured in varying probe concentrations and hybridization times. The dotted line represents the sensitivity of a traditional overnight RNA FISH. Error bars reflect standard error of the mean. C. Spot counts for the same conditions as in B. Error bars reflect standard deviation. At 10 minutes and for all probe concentrations, the spot counts for Turbo FISH are statistically different from overnight FISH (4X: p = 9.87×10⁻⁶, 1X: p = 0.0136, 1/4X: p = 4.86×10⁻⁶, 1/16X: p = 1.75×10⁻¹¹; two-tailed t-test). For all conditions, we analyzed spot counts and calculated the sensitivity on 80–120 cells. Data shown represents one of two replicate experiments.</p

Demonstration of Turbo SNP FISH.

<p>A. Demonstration of SNP FISH efficacy under Turbo FISH and conventional RNA FISH conditions in WM983b cells. We targeted BRAF mRNA with guide probes, and then used detection probes that targeted either the V600E mutation for which BRAF is heterozygous in this cell line (top panels) or a common region for which BRAF is homozygous in this cell line (bottom panels). Left panels show the signals from the guide probe (that labels the mRNA), the middle panel shows the detection probe that detects the wild-type sequence, and the right panel shows the detection probe that detects the mutant sequence. B. Quantification of RNA as being either mutant or wild type in this cell line. Each bar corresponds to data from a single cell.</p