Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection

It has been known that,the novel coronavirus,2019-nCoV,which is considered similar to SARS-CoV,invades human cells via the receptor angiotensin converting enzyme II(ACE2).Moreover,lung cells that have ACE2 expression may be the main target cells during 2019-nCoV infection.However,some patients also exhibit non-respiratory symptoms,such as kidney failure,implying that 2019-nCoV could also invade other organs.To construct a risk map of different human organs,we analyzed the single-cell RNA sequencing(scRNA-seq)datasets derived from major human physiological systems,including the respiratory,cardiovascular,digestive,and urinary systems.Through scRNA-seq data analyses,we identified the organs at risk,such as lung,heart,esophagus,kidney,bladder,and ileum,and located specific cell types(i.e.,type II alveolar cells(AT2),myocardial cells,proximal tubule cells of the kidney,ileum and esophagus epithelial cells,and bladder urothelial cells),which are vulnerable to 2019-nCoV infection.Based on the findings,we constructed a risk map indicating the vulnerability of different organs to 2019-nCoV infection.This study may provide potential clues for further investigation of the pathogenesis and route of 2019-nCoV infection.

Molecular dynamics simulations exploring drug resistance in HIV-1 proteases

Although HIV-1 subtype B still dominates the epidemic AIDS in developed countries,an increasing number of people in developing countries are suffering from an epidemic of non-subtype B viruses.What is worse,the efficacy of the combinational use of antiretroviral drugs is gradually compromised by the rapid development of drug resistance.To gain an insight into drug resistance, 10-ns MD simulations were simultaneously conducted on the complexes of the TL-3 inhibitor with 4 different proteases(Bwt,Bmut, Fwt and Fmut),among which the complex of the Bwt protease with the TL-3 inhibitor was treated as the control group.Detailed analyses of MD data indicated that the drug resistance of Bmut against TL-3 mainly derived from loss of an important hydrogen bond and that of Fwt was caused by the decrease of hydrophobic interactions in S1/S1'pocket,while both of the two reasons mentioned above were the cause of the Fmut protease's resistance.These results are in good agreement with the previous experiments, revealing a possible mechanism of drug resistance for the aforementioned protease subtypes against the TL-3 inhibitor.Additionally,another indication was obtained that the mutations of M36I,V82A and L90M may induce structural transforms so as to alter the inhibitor's binding mode.

Assessment of sleep disturbances with the athlete sleep screening questionnaire in Chinese athletes

This study investigated the factors that are associated with sleep disturbances among Chinese athletes.Sleep quality and associated factors were assessed by the Athlete Sleep Screening Questionnaire(ASSQ,n?394,aged 18–32 years,47.6%female).Sleep difficulty score(SDS)and level of sleep problem(none,mild,moderate,or severe)were used to classify participants'sleep quality.Categorical variables were analyzed by Chi-square or fisher's exact tests.An ordinal logistic regression analysis was used to explore factors with poor sleep(SDS8).Approximately 14.2%of participants had moderate to severe sleep problem(SDS8).Fifty-nine percent of the athletes reported sleep disturbance during travel,while 43.3%experienced daytime dysfunction when travelling for competition.No significant difference was found in the SDS category between gender,sports level and events.Athletes with evening chronotype were more likely to report worse sleep than athletes with morning and intermediate chronotype(OR,2.25;95%CI,1.44–3.52;p<0.001).For each additional year of age,there was an increase of odds ratio for poor sleep quality(OR,1.15;95%CI,1.04–1.26;p?0.004),while each additional year of training reduced the odds ratio(OR,0.95;95%CI,0.91–0.99;p?0.044).To improve sleep health in athletes,chronotype,travel-related issues,age and years of training should be taken into consideration.