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10.1007/s10875-020-00821-7 http://scihub22266oqcxt.onion/10.1007/s10875-020-00821-7 32661797!7357264!32661797     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Clin+Immunol 2020 ; 40 (7): 960-969 Nephropedia Template TP {{tp|p=32661797|t=2020. Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection |pdf=|usr=}}{{32661797}} gab.com Text Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection JO: J Clin Immunol http://www.kidney.de/pget.php?&tw=1&pmid=32661797 #FOAvip BACKGROUND: There are currently rare satisfactory markers for predicting the death of patients with coronavirus disease 2019 (COVID-19). The aim of this study is to establish a model based on the combination of serum cytokines and lymphocyte subsets for predicting the prognosis of the disease. METHODS: A total of 739 participants with COVID-19 were enrolled at Tongji Hospital from February to April 2020 and classified into fatal (n = 51) and survived (n = 688) groups according to the patient's outcome. Cytokine profile and lymphocyte subset analysis was performed simultaneously. RESULTS: The fatal patients exhibited a significant lower number of lymphocytes including B cells, CD4(+) T cells, CD8(+) T cells, and NK cells and remarkably higher concentrations of cytokines including interleukin-2 receptor, interleukin-6, interleukin-8, and tumor necrosis factor-alpha on admission compared with the survived subjects. A model based on the combination of interleukin-8 and the numbers of CD4(+) T cells and NK cells showed a good performance in predicting the death of patients with COVID-19. When the threshold of 0.075 was used, the sensitivity and specificity of the prediction model were 90.20% and 90.26%, respectively. Meanwhile, interleukin-8 was found to have a potential value in predicting the length of hospital stay until death. CONCLUSIONS: Significant increase of cytokines and decrease of lymphocyte subsets are found positively correlated with in-hospital death. A model based on the combination of three markers provides an attractive approach to predict the prognosis of COVID-19. Twit Text FOAVip Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection ????J Clin Immunol http://www.kidney.de/pget.php?&tw=1&pmid=32661797 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32661797 #FOAvip English Wikipedia <ref>{{Cite pmid|32661797}}</ref> Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection #MMPMID32661797 Luo Y; Mao L; Yuan X; Xue Y; Lin Q; Tang G; Song H; Wang F; Sun Z J Clin Immunol 2020[Oct]; 40 (7): 960-969 PMID32661797show ga BACKGROUND: There are currently rare satisfactory markers for predicting the death of patients with coronavirus disease 2019 (COVID-19). The aim of this study is to establish a model based on the combination of serum cytokines and lymphocyte subsets for predicting the prognosis of the disease. METHODS: A total of 739 participants with COVID-19 were enrolled at Tongji Hospital from February to April 2020 and classified into fatal (n = 51) and survived (n = 688) groups according to the patient's outcome. Cytokine profile and lymphocyte subset analysis was performed simultaneously. RESULTS: The fatal patients exhibited a significant lower number of lymphocytes including B cells, CD4(+) T cells, CD8(+) T cells, and NK cells and remarkably higher concentrations of cytokines including interleukin-2 receptor, interleukin-6, interleukin-8, and tumor necrosis factor-alpha on admission compared with the survived subjects. A model based on the combination of interleukin-8 and the numbers of CD4(+) T cells and NK cells showed a good performance in predicting the death of patients with COVID-19. When the threshold of 0.075 was used, the sensitivity and specificity of the prediction model were 90.20% and 90.26%, respectively. Meanwhile, interleukin-8 was found to have a potential value in predicting the length of hospital stay until death. CONCLUSIONS: Significant increase of cytokines and decrease of lymphocyte subsets are found positively correlated with in-hospital death. A model based on the combination of three markers provides an attractive approach to predict the prognosis of COVID-19. |*Models, Biological[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Betacoronavirus/genetics/*immunology/isolation & purification[MESH] |Biomarkers/blood[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |China/epidemiology[MESH] |Clinical Laboratory Techniques/methods[MESH] |Coronavirus Infections/blood/diagnosis/epidemiology/immunology/*mortality[MESH] |Cytokines/*blood/immunology[MESH] |Female[MESH] |Humans[MESH] |Length of Stay[MESH] |Lymphocyte Count[MESH] |Lymphocyte Subsets/*immunology[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/blood/epidemiology/immunology/*mortality[MESH] |Prognosis[MESH] |RNA, Viral/isolation & purification[MESH] |Reverse Transcriptase Polymerase Chain Reaction[MESH] |Risk Assessment/methods[MESH]Prediction Model Based on the Combination of Cytokines and Lymphocyte (epmc).pdf DeepDyve Pubget Overpricing