Inferior outcomes of liver transplantation for hepatocellular carcinoma during early-COVID-19 pandemic in the United States

BACKGROUND Early in the coronavirus disease 2019(COVID-19)pandemic,there was a significant impact on routine medical care in the United States,including in fields of transplantation and oncology.AIM To analyze the impact and outcomes of early COVID-19 pandemic on liver transplantation(LT)for hepatocellular carcinoma(HCC)in the United States.METHODS WHO declared COVID-19 as a pandemic on March 11,2020.We retrospectively analyzed data from the United Network for Organ Sharing(UNOS)database regarding adult LT with confirmed HCC on explant in 2019 and 2020.We defined pre-COVID period from March 11 to September 11,2019,and early-COVID period as from March 11 to September 11,2020.RESULTS Overall,23.5%fewer LT for HCC were performed during the COVID period(518 vs 675,P<0.05).This decrease was most pronounced in the months of March-April 2020 with a rebound in numbers seen from May-July 2020.Among LT recipients for HCC,concurrent diagnosis of non-alcoholic steatohepatitis significantly increased(23 vs 16%)and alcoholic liver disease(ALD)significantly decreased(18 vs 22%)during the COVID period.Recipient age,gender,BMI,and MELD score were statistically similar between two groups,while waiting list time decreased during the COVID period(279 days vs 300 days,P=0.041).Among pathological characteristics of HCC,vascular invasion was more prominent during COVID period(P<0.01),while other features were the same.While the donor age and other characteristics remained same,the distance between donor and recipient hospitals was significantly increased(P<0.01)and donor risk index was significantly higher(1.68 vs 1.59,P<0.01)during COVID period.Among outcomes,90-day overall and graft survival were the same,but 180-day overall and graft were significantly inferior during COVID period(94.7 vs 97.0%,P=0.048).On multivariable Coxhazard regression analysis,COVID period emerged as a significant risk factor of post-transplant mortality(Hazard ratio 1.85;95%CI:1.28-2.68,P=0.001).CONCLUSION During COVID period,there was a significant decrease in LTs performed for HCC.While early postoperative outcomes of LT for HCC were same,the overall and graft survival of LTs for HCC after 180 days were significantly inferior.

TIFAflow: Enhancing Traffic Archiving System with Flow Granularity for Forensic Analysis in Network Security

The archiving of Internet traffic is an essential function for retrospective network event analysis and forensic computer communication. The state-of-the-art approach for network monitoring and analysis involves storage and analysis of network flow statistic. However, this approach loses much valuable information within the Internet traffic. With the advancement of commodity hardware, in particular the volume of storage devices and the speed of interconnect technologies used in network adapter cards and multi-core processors, it is now possible to capture 10 Gbps and beyond real-time network traffic using a commodity computer, such as n2disk. Also with the advancement of distributed file system （such as Hadoop, ZFS, etc.） and open cloud computing platform （such as OpenStack, CloudStack, and Eucalyptus, etc.）, it is practical to store such large volume of traffic data and fully in-depth analyse the inside communication within an acceptable latency. In this paper, based on well- known TimeMachine, we present TIFAflow, the design and implementation of a novel system for archiving and querying network flows. Firstly, we enhance the traffic archiving system named TImemachine＋FAstbit （TIFA） with flow granularity, i.e., supply the system with flow table and flow module. Secondly, based on real network traces, we conduct performance comparison experiments of TIFAflow with other implementations such as common database solution, TimeMachine and TIFA system. Finally, based on comparison results, we demonstrate that TIFAflow has a higher performance improvement in storing and querying performance than TimeMachine and TIFA, both in time and space metrics.

Rice Expression Database(RED)：An integrated RNA-Seq-derived gene expression database for rice

Rice is one of the most important stable food as well as a monocotyledonous model organism for the plant research community.Here,we present RED（Rice Expression Database;http：//expression.ic4r.org）,an integrated database of rice gene expression profiles derived entirely from RNA-Seq data.RED features a comprehensive collection of 284 high-quality RNA-Seq experiments,integrates a large number of gene expression profiles and covers a wide range of rice growth stages as well as various treatments.Based on massive expression profiles,RED provides a list of housekeeping and tissue-specific genes and dynamically constructs co-expression networks for gene（s） of interest.Besides,it provides user-friendly web interfaces for querying,browsing and visualizing expression profiles of concerned genes.Together,as a core resource in BIG Data Center,RED bears great utility for characterizing the function of rice genes and better understanding important biological processes and mechanisms underlying complex agronomic traits in rice.