OBJECTIVEAbnormalities in dopamine production and receptor function have been described in human essential hypertension and rodent models of genetic hypertension. We investigated the role of G protein kinase (GRK) 4gamma in essential hypertension in GRK4gamma mutant A142V transgenic mice.

METHODSBlood pressure, renal sodium excretion, D(1) receptor protein expression and phosphorylation were measured in GRK4gammaA142V transgenic mice and control mice. Moreover, the effects of GRK4 inhibition by antisense oligonucleotides on D(1) receptor expressions were determined in HK-2 cells.

RESULTSAs compared with their control mice, GRK4gammaA142V transgenic mice had higher blood pressure, lower D(1) receptor expression (0.6 +/- 0.2 vs. 1.5 +/- 0.2, P < 0.05), higher D(1) receptor phosphorylation [(65 +/- 7) DU vs. (35 +/- 7) DU, P < 0.05] in renal cortical membranes and the diuretic and natriuretic effects after stimulation of renal D(1) receptor were impaired in GRK4gammaA142V transgenic mice. Inhibition of GRK4 expression (0.60 +/- 0.10 vs. 1.30 +/- 0.09, P < 0.05) by GRK4 antisense oligonucleotides upregulated D(1) receptor expression (1.5 +/- 0.2 vs. 0.8 +/- 0.1, P < 0.05) in HK-2 cells.

CONCLUSIONSOur results show that GRK4gammaA142V overexpression induced hypertension is mediated by dowregulated renal D(1) receptor expressions in GRK4gammaA142V transgenic mice.

Animals ; Blood Pressure ; Down-Regulation ; Female ; G-Protein-Coupled Receptor Kinase 4 ; genetics ; metabolism ; Gene Expression Regulation ; Hypertension ; genetics ; metabolism ; physiopathology ; Male ; Mice ; Mice, Inbred C57BL ; Mice, Transgenic ; Oligonucleotides, Antisense ; Phosphorylation ; Receptors, Dopamine D1 ; metabolism

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

To obtain protective human monoclonal antibody from HIV-1 infected person, we adapted a technology for isolating antigen specific monoclonal antibody from human memory B cells through in vitro B cell activation coupled with RT-PCT and expression cloning. Human B cells were purified by negative sorting from PBMCs of HIV-1 infected individuals and memory B cells were further enriched using anti-CD27 microbeads. Two hundred memory B cells per well were cultured in 96-well round-bottom plates Env-specific antibodies in supernatants were with feeder cells in medium containing EBV and CpG. screened by ELISA after 1-2 weeks' culture. Cells from positive wells of Env-specific antibody were harvested and total RNA was isolated. Human VH and Vkappa or Vlambda genes were amplified by RT-PCR and cloned into IgG1 and kappa or lambda expressing vectors. Functional VH and Vkappa or Vlambda were identified by cotransfecting 293T cells with individual heavy chain and light chain clones followed by analysis of culture supernatants by ELISA for Env-specific antibodies. Finally, corresponding mAb was produced by transient transfection of 293T cells with the identified VH and Vkappa/lambda pair and purified by protein A affinity chromatography. Purified monocolonal antibodies were used for HIV-1 specific antibody-dependent cell-mediated cytotoxicity (ADCC) and neutralizing activity assay. Four monocolonal Env-specific antibodies were isolated from one HIV-1 subtype B' infected individual. Two of them showed strong ADCC activity and one showed weak neutralizing activity against HIV-1. Its further studies on their application in therapeutic or prophylactic vaccines against HIV-1 should be grounded.
Antibodies, Monoclonal ; genetics ; immunology ; Antibody Specificity ; Asian Continental Ancestry Group ; B-Lymphocytes ; immunology ; Cloning, Molecular ; HEK293 Cells ; HIV Infections ; blood ; immunology ; HIV-1 ; immunology ; pathogenicity ; Humans ; Immunity, Humoral ; Neutralization Tests ; Polymerase Chain Reaction ; env Gene Products, Human Immunodeficiency Virus ; immunology