DeepFlu: a deep learning approach for forecasting symptomatic influenza A infection based on pre-exposure gene expression.

BACKGROUND AND OBJECTIVE: Not everyone gets sick after an exposure to influenza A viruses (IAV). Although KLRD1 has been identified as a potential biomarker for influenza susceptibility, it remains unclear whether forecasting symptomatic flu infection based on pre-exposure host gene expression might be possible. METHOD: To examine this hypothesis, we developed DeepFlu using the state-of-the-art deep learning approach on the human gene expression data infected with IAV subtype H1N1 or H3N2 viruses to forecast who would catch the flu prior to an exposure to IAV. RESULTS: The results indicated that such forecast is possible and, in other words, gene expression could reflect the strength of host immunity. In the leave-one-person-out cross-validation, DeepFlu based on deep neural network outperformed the models using convolutional neural network, random forest, or support vector machine, achieving 70.0% accuracy, 0.787 AUROC, and 0.758 AUPR for H1N1 and 73.8% accuracy, 0.847 AUROC, and 0.901 AUPR for H3N2. In the external validation, DeepFlu also reached 71.4% accuracy, 0.700 AUROC, and 0.723 AUPR for H1N1 and 73.5% accuracy, 0.732 AUROC, and 0.749 AUPR for H3N2, surpassing the KLRD1 biomarker. In addition, DeepFlu which was trained only by pre-exposure data worked the best than by other time spans and mixed training data of H1N1 and H3N2 did not necessarily enhance prediction. DeepFlu is available at https://github.com/ntou-compbio/DeepFlu. CONCLUSIONS: DeepFlu is a prognostic tool that can moderately recognize individuals susceptible to the flu and may help prevent the spread of IAV.

Lipid diffusion in giant unilamellar vesicles is more than 2 times faster than in supported phospholipid bilayers under identical conditions.

The lateral diffusion coefficients of a BODIPY tail-labeled lipid in two model systems, namely, free-standing giant unilamellar vesicles (GUVs) and supported phospholipid bilayers (SPBs), were determined by fluorescence correlation spectroscopy (FCS) using the Z-scan approach. For the first time, the performed measurements on 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) bilayers maintain exactly the same experimental conditions for both systems, which allows for a quantitative comparison of lipid diffusion in these two commonly used model membranes. The results obtained revealed that the lipid mobility in free-standing bilayers (D=7.8+/-0.8 microm2 s-1) is significantly higher than in the bilayer created on the solid support (mica) (D=3.1+/-0.3 microm2 s-1).