Evaluation of Benzamide Derivatives as New Influenza A Nucleoprotein Inhibitors

Virus nucleoprotein (NP) is an emerging target for drug development for Influenza. We designed benzamide derivatives as new inhibitors of NP that demonstrate good potency in blocking influenza A. Screening revealed that compound 39 was the most potent molecule in the series, exhibiting IC50 values of 0.46 and 0.27 μM in blocking the replication of H3N2 (A/HK/8/68) and (A/WSN/33) influenza A viral strains. The observed inhibition of viral replication correlated well with cytopathic protection. Furthermore, based on computational analysis and fluorescence microscopy, it was determined that compound 39 inhibited nuclear accumulation by targeting influenza A viral nucleoproteins. Finally, the rodent pharmacokinetic profile of compound 32 displayed half-life of greater than 4 hours and bioavailability greater than 20%, suggesting this class of molecules had drug-like properties.

MDSNet:a multiscale decoupled supervision network for semantic segmentation of remote sensing images

Recent deep-learning successes have led to a new wave of semantic segmentation in remote sensing(RS)applications.However,most approaches rarely distinguish the role of the body and edge of RS ground objects;thus,our understanding of these semantic parts has been frustrated by the lack of detailed geometry and appearance.Here we present a multiscale decoupled supervision network for RS semantic segmentation.Our proposed framework extends a densely supervised encoder-decoder network with a feature decoupling module that can decouple semantic features with different scales into distinct body and edge components.We further conduct multiscale supervision of the original and decoupled body and edge features to enhance inner consistency and spatial boundaries in remote sensing image(RSl)ground objects,enabling new segmentation designs and semantic components that can learn to perform multiscale geometry,and appearance.Our results outperform the previous algorithm and are robust to different datasets.These results demonstrate that decoupled supervision is an effective solution to semantic segmentation tasks of RS images.

Use of donor-specific red blood cell transfusions for patients undergoing liver transplantation during the COVID-19 pandemic

Background:Increased risks have been found for patients undergoing liver transplantation due to the blood supply shortage following the ongoing coronavirus disease 2019(COVID-19)pandemic.Hence,exploring a method to alleviate this dilemma is urgent.This phase I,nonrandomized,prospective trial aimed to evaluate the safety and feasibility of using donor-specific red blood cell transfusion(DRBCT)as an urgent measurement to alleviate the blood supply shortage in deceased donor liver transplantation(DDLT).Methods:The outcomes of 26 patients who received DRBCT and 37 patients in the control group who only received 3rd party packed red blood cells(pRBCs)transfusion between May 2020 and January 2021 were compared.Results:Patients receiving DRBCT did not develop transfusion-related complications,and the incidence of postoperative infection was similar to that in the control group(23.1%vs.18.9%,P=0.688).Because the patients received the red blood cells from organ donors,the median volume of intraoperative allogeneic red blood cell transfusion from blood bank was 4.0 U(IQR 1.1-8.0 U)in the DRBCT group,which is significantly lower than that(7.5 U,IQR 4.0-10.0 U)in the control group(P=0.018).The peak aspartate aminotransferase(AST)level was significantly lower in the DRBCT group than in the control group(P=0.008)and so were the AST levels in the first two days after the operation(P=0.006 and P=0.033).Conclusions:DRBCT is a safe and effective procedure to lower the need for blood supply and is associated with a reduction in AST levels after transplantation.DRBCT is beneficial to patients receiving life-saving transplantation without sufficient blood supply during the COVID-19 pandemic.