Abstract
Dendritic cells (DCs) and macrophages (MFs) are important multifunctional immune cells. Like other cell types, they express hundreds of different microRNAs (miRNAs) that are recently discovered post-transcriptional regulators of gene expression. Here we present updated miRNA expression profiles of monocytes, DCs and MFs. Compared with monocytes, ∼50 miRNAs were found to be differentially expressed in immature and mature DCs or MFs, with major expression changes occurring during the differentiation. Knockdown of DICER1, a protein needed for miRNA biosynthesis, led to lower DC-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN) and enhanced CD14 protein levels, confirming the importance of miRNAs in DC differentiation in general. Inhibition of the two most highly up-regulated miRNAs, miR-511 and miR-99b, also resulted in reduced DC-SIGN level. Prediction of miRNA-511 targets revealed a number of genes with known immune functions, of which TLR4 and CD80 were validated using inhibition of miR-511 in DCs and luciferase assays in HEK293 cells. Interestingly, under the cell cycle arrest conditions, miR-511 seems to function as a positive regulator of TLR4. In conclusion, we have identified miR-511 as a novel potent modulator of human immune response. In addition, our data highlight that miRNA influence on gene expression is dependent on the cellular environment.
Highlights
The outbreak of atypical and person-to-person transmissible pneumonia caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2, known as 2019-nCov) has caused a global alarm
Delineation of ROIs We sketched the region of interest (ROI) on the CT images based on the features of pneumonia, such as ground-glass opacity, mosaic sign and interlobular septal thickening
Due to the limitation of nucleic acid -based laboratory testing, there is an urgent need to look for fast alternative methods that can be used by front-line health care personals for quickly and accurately diagnosing the disease
Summary
The outbreak of atypical and person-to-person transmissible pneumonia caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2, known as 2019-nCov) has caused a global alarm. We believed that CNN might help us identify unique features that might be difficult for visual recognition To test this notion, we retrospectively enrolled 453 CT images of pathogen-confirmed COVID-19 cases along with previously diagnosed typical viral pneumonia. The external testing showed a total accuracy of 73% with specificity of 67% and sensitivity of 74% These observations demonstrate the proof-of-principle using the deep learning method to extract radiological graphical features for COVID-19 diagnosis. Based on COVID-19 radiographical changes in CT images, we hypothesized that deep learning methods might be able to extract COVID-19’s graphical features and provide a clinical diagnosis ahead of the pathogenic test, saving critical time for disease control.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
