were measured at the end of inspiratory.ResultsThe differences of following parameters were statistically significant between the healthy control group, mild PH group, moderate PH group and severe PH group: velocity time integral of ventricular systolic wave (SVTI), velocity time integral of ventricular diastolic wave (DVTI), AR, AR/S, ARVTI/SVTI, AR/(S+D), ARVTI/(SVTI+DVTI,F=2.940, 2.838, 3.373, 7.681, 3.478, 12.052, 4.806;P<0.05,<0.05,<0.05,<0.01,<0.05,<0.01,<0.01). Correlation analysis showed that ratio of AR/(S+D) was positively correlated with pulmonary artery mean pressure (r= 0.575,P<0.01). ConclusionsThe ratio of AR/(S+D) of MHV Doppler flow spectra was correlated with pulmonary artery mean pressure. The ratio increased along with the increasing of pulmonary pressure in CHD patient. The Doppler echocardiographic detection of MHV flow had a certain reference value for the diagnosis of PH. The ratio of AR/(S+D) could be a new non-invasive parameter for evaluating PH.

Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity-or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled com-pound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unla-beled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and Bind-ingDB, as well as of the known drug-target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 recep-tor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.

Objective:To investigate the characteristics of HBV-specific T cell reactivity among pregnant, postpartum and non-pregnant women at childbearing age with chronic HBV infection.Methods:A total of 100 patients with chronic HBV infection were enrolled in this study, including 43 pregnant women (pregnant group), 26 patients giving birth within six months (postpartum group), and 31 non-pregnant patients at childbearing age (non-pregnant group). The functional HBV-specific T cells in peripheral blood were detected by ELISPOT. Clinical data as well as the results of virological and serological tests of HBV were collected for stratified analysis.Results:There was no significant difference in the number of functional HBV-specific T cells between the pregnant group and the postpartum group, but the number was significantly lesser in the pregnant group than in the non-pregnant group ( P<0.05). Furthermore, the number of functional HBV-specific T cells was significantly higher in the nucleoside analogues (NUCs)-treated pregnant women than in the NUCs-untreated pregnant women ( P<0.05). Among the patients without NUCs treatment, there were no significant differences in the numbers of hepatitis B envelope antigen/hepatitis B core antigen (HBeAg/HBcAg)-specific T cells between the pregnant group and the postpartum group, but the numbers were lower in the pregnant group than in the non-pregnant group ( P<0.05). Among the NUCs-treated patients, there was no significant difference in the number of functional HBV-specific T cells between the pregnant group and the non-pregnant group, and the numbers in the two groups were significantly higher than that in the postpartum group (both P<0.05). Additionally, receiver operating characteristic (ROC) curve indicated that the number of functional HBV-specific T cells in combination with HBV DNA load [area under the curve (AUC)=0.807] or hepatitis B surface antigen (HBsAg) levels (AUC=0.916) had a good predictive performance for hepatitis progression during pregnancy. Conclusions:Pregnancy can reduce HBV-specific T cell reactivity in women with chronic HBV infection, but NUCs treatment may improve the function of specific T cells. Routine monitoring of HBV-specific T cells during pregnancy and postpartum period can provide valuable guidance for the evaluation of immune function and treatments.

This study was performed to investigate the features of HBV-specific CD8+T cell reactivity in patients with chronic hepatitis B(CHB),HBV-induced liver cirrhosis(LC)or hepatocellular carcinoma(HCC).A total of 124 CHB patients,36 LC patients,and 114 HCC patients were enrolled in this study.The reactive HBV-specific CD8+T cells in peripheral blood were enumerated using an innovative ELISPOT system.In addition,19 CHB patients and 20 HCC patients were longitudinally monitored with an interval of 3-5 months.Data showed that the numbers of reactive HBV-specific CD8+T cells in CHB group were not significantly different from that in LC group,but obviously lower than that in HCC group(P=0.009 9),especially HBsAg-,HBpol-and HBe/cAg-specific CD8+T cells.In CHB group,the patients with normal ALT level,AST level,or low HBV-DNA load showed significantly more reactive HBV-specific CD8+T cells than the patients with abnormal ALT level,abnormal AST level,or high HBV-DNA load.Furthermore,the duration of NUCs treatment had an impact on the HBV-specific CD8+T cell reactivity in CHB patients,while different NUCs at the same treatment duration did not bring different reactivity of HBV-specific T cells.In LC group,the HBeAg-positive patients presented much more reactive HBV-specific CD8+T cells than the HBeAg-negative patients did.In HCC group,the numbers of reactive HBV-specific CD8+T cells in the patients with normal AFP level or normal DCP level were significantly higher than that in the patients with abnormal AFP level or abnormal DCP level.Longitudinal monitoring results showed that HBV-specific CD8+T cell reactivity displayed a slow upward trend in the CHB patients undergoing NUCs treatment,and an obvious increasing in the HCC patients undergoing combined treatment of targeted drugs and immunotherapy.Taken together,the features of HBV-specific CD8+T cell reactivity are distinct among the CHB,LC and HCC patients,and are influenced by virological indicators,tumor markers and treatment regimens.Therefore,more attention should be paid to the changes of HBV-specific CD8+T cell reactivity during clinical treatment.