The coronavirus disease 2019 is raging across the world. The radiomics, which explores huge amounts of features from medical image for disease diagnosis, may help the screen of the COVID-19. In this study, we aim to develop a radiomic signature to screen COVID-19 from CT images. We retrospectively collect 75 pneumonia patients from Beijing Youan Hospital, including 46 patients with COVID-19 and 29 other types of pneumonias. These patients are divided into training set (n = 50) and test set (n = 25) at random. We segment the lung lesions from the CT images, and extract 77 radiomic features from the lesions. Then unsupervised consensus clustering and multiple cross-validation are utilized to select the key features that are associated with the COVID-19. In the experiments, while twenty-three radiomic features are found to be highly associated with COVID-19, four key features are screened and used as the inputs of support vector machine to build the radiomic signature. We use area under the receiver operating characteristic curve (AUC) and calibration curve to assess the performance of our model. It yields AUCs of 0.862 and 0.826 in the training set and the test set respectively. We also perform the stratified analysis and find that its predictive ability is not affected by gender, age, chronic disease and degree of severity. In conclusion, we investigate the value of radiomics in screening COVID-19, and the experimental results suggest the radiomic signature could be a potential tool for diagnosis of COVID-19.

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Synergistic effects of anticancer drug and siRNA have displayed superior advantages for cancer therapy. Herein, we deeply analyzed the feasibility that whether doxorubicin (DOX) and siRNA could be co-delivered by mPEG-PCL-graft-PDMAEMA (PECD) micelles, which mediated excellent DNA/siRNA delivery in vitro and in vivo reported in our previous work. DOX-loaded NPs (PECD-D) were developed by nanoprecipitation technology and exhibited high drug loading content (DLC, 9.5%). In vitro cytotoxicity study in MDA-MB-231 cells, PECD-D treated groups had lower IC50 compared to free DOX groups (F-DOX) at different transfection time (24, 48, and 72h), which maybe attribute to its high cellular uptake and endosomal escape properties. The speculation was confirmed with the results of drug release profile in acidic media, flow cytometry analysis and confocal images. Futhermore, Cy5 labeled siRNA was introduced in PECD-D micelles (PECD-D/siRNA) to track the behavior of dual-loaded nanodrug in vitro and in vivo. Flow cytometry analysis presented that DOX and siRNA were successfully co-delivered into cells, the positive cells ratio were 94.6 and 99.5%, respectively. Confocal images showed that not only DOX and siRNA existed in cytoplasm, but DOX traversed endosome/lysosome and entered into cell nucleus. For in vivo tumor-targeting evaluation in BALB/c nude mice, both DOX and Cy5-siRNA could be detected in tumor sites after intravenous injection with PECD-D/siRNA formulation. Therefore, we believed that PECD micelles have a potential ability as DOX and siRNA co-delivery carrier for cancer therapy.

Since its outbreak in December 2019, the persistent coronavirus disease (COVID-19) became a global health emergency. It is imperative to develop a prognostic tool to identify high-risk patients and assist in the formulation of treatment plans. We retrospectively collected 366 severe or critical COVID-19 patients from four centers, including 70 patients who died within 14 days (labeled as high-risk patients) since their initial CT scan and 296 who survived more than 14 days or were cured (labeled as low-risk patients). We developed a 3D densely connected convolutional neural network (termed De-COVID19-Net) to predict the probability of COVID-19 patients belonging to

Tri-block copolymers have exhibited great potentials in small interfering RNA (siRNA) therapeutics. To reveal structure-activity relationships, we here synthesized a series of tri-block copolymers with different hydrophobic segments, PEG-PAMA-P(C6A-C7A-DPA-DBA) (EAAS) and PEG-PDAMAEMA-P(C6A-C7A-DPA-DBA) (EDAS), termed from EAASa to EAASh and EDASa to EDASh, with pKa ranging from 5.2 to 7.0. Our data showed that the better gene silencing efficiency was located in pKa of 5.8-6.2, which was contributed from higher endosomal escape observed with confocal images and hemolysis assay. EAASc, the leader polymer, showed excellent gene knockdown at w/w ratio of 14.5 on HepG2 (89.94%), MDA-MB-231 (92.45%), 293A (83.06%), and Hela cells (80.27%), all better than lipofectamine 2000. Besides, EAASc mediated effective gene silencing in tumor when performed peritumoral injection. This work found out that polymers with pKa ranging from 5.8 to 6.2 were efficient in siRNA delivery, which provided an optimization strategy for siRNA delivery systems, especially for tri-block copolymers.

A novel terpolymer of acrylamide (AM), 4-vinylpyridine (VP), and 2-acrylamide-2-methylpropanesulfonic acid (AMPS) was synthesized through free radical polymerization and characterized by proton nuclear magnetic resonance, Fourier transform infrared spectroscopy, elemental analysis, and static light scattering measurement. The monomer ratio was shown to be the predominant factor to the fluid-loss control performance of this polymer in drilling fluids. The terpolymer under optimal polymerization conditions (PAAV) was prepared, and the dipolymer of AM and AMPS (PAA) was synthesized as a contrast sample. In an American Petroleum Institute (API) filtration test of bentonite-based mud with 10% CaCl 2 contamination after a 16 h aging at 150 °C, mud with 1% PAAV maintained an API filtrate volume (FL API ) of 4.8 mL, whereas mud with 1% PAA reached a FL API of 96.0 mL. The fluid-loss control mechanism of PAAV was investigated through adsorption experiments, ζ potential measurements, and particle size distribution analysis. The results illustrate that the introduction of VP units into a polymer molecule greatly improves the temperature resistance performance of the polymer and enhances the interaction between the polymer and bentonite, which improves colloidal properties of bentonite particles, and these make PAAV a pronounced fluidloss control agent in deep gypsum drilling operations.

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