The simulation of the dynamics of viral infections by mathematical equations has been applied successfully to the study of viral infections during antiviral therapy. Standard models applied to viral hepatitis describe...The simulation of the dynamics of viral infections by mathematical equations has been applied successfully to the study of viral infections during antiviral therapy. Standard models applied to viral hepatitis describe the viral load decline in the f irst 2-4 wk of antiviral therapy, but do not adequately simulate the dynamics of viral infection for the following period. The hypothesis of a constant clearance rate of the infected cells provides an unrealistic estimation of the time necessary to reach the control or the clearance of hepatitis B virus (HBV)/ hepatitis C virus (HCV) infection. To overcome the problem, we have developed a new multiphasic model in which the immune system activity is modulated by a negative feedback caused by the infected cells reduction, and alanine aminotransferase kinetics serve as a surrogate marker of infected-cell clearance. By this approach, we can compute the dynamics of infected cells during the whole treatment course, and find a good correlation between the number of infected cells at the end of therapy and the long-term virological response in patients with chronic hepatitis C. The new model successfully describes the HBV infection dynamics far beyond the third month of antiviral therapy under the assumption that the sum of infected and non-infected cells remains roughly constant during therapy, and both target and infected cells concur in the hepatocyte turnover. In clinical practice, these new models will allow the development of simulators of treatment response that will be used as an "automatic pilot" for tailoring antiviral therapy in chronic hepatitis B as well as chronic hepatitis C patients.展开更多
BACKGROUND Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infections is diagnosed via real time reverse transcriptase polymerase chain reaction(RT-PCR)and reported as a binary assessment of the test being ...BACKGROUND Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infections is diagnosed via real time reverse transcriptase polymerase chain reaction(RT-PCR)and reported as a binary assessment of the test being positive or negative.High SARS-CoV-2 viral load is an independent predictor of disease severity and mortality.Quantitative RT-PCR may be useful in predicting the clinical course and prognosis of patients diagnosed with coronavirus disease 2019(COVID-19).AIM To identify whether quantitative SARS-CoV-2 viral load assay correlates with clinical outcome in COVID-19 infections.METHODS A systematic literature search was undertaken for a period between December 30,2019 to December 31,2020 in PubMed/MEDLINE using combination of terms“COVID-19,SARS-CoV-2,Ct values,Log_(10) copies,quantitative viral load,viral dynamics,kinetics,association with severity,sepsis,mortality and infectiousness”.After screening 990 manuscripts,a total of 60 manuscripts which met the inclusion criteria were identified.Data on age,number of patients,sample sites,RT-PCR targets,disease severity,intensive care unit admission,mortality and conclusions of the studies was extracted,organized and is analyzed.RESULTS At present there is no Food and Drug Administration Emergency Use Authorization for quantitative viral load assay in the current pandemic.The intent of this research is to identify whether quantitative SARS-CoV-2 viral load assay correlates with severity of infection and mortality?High SARS-CoV-2 viral load was found to be an independent predictor of disease severity and mortality in majority of studies,and may be useful in COVID-19 infection in susceptible individuals such as elderly,patients with co-existing medical illness such as diabetes,heart diseases and immunosuppressed.High viral load is also associated with elevated levels of TNF-α,IFN-γ,IL-2,IL-4,IL-6,IL-10 and C reactive protein contributing to a hyper-inflammatory state and severe infection.However there is a wide heterogeneity in fluid samples and different p展开更多
文摘The simulation of the dynamics of viral infections by mathematical equations has been applied successfully to the study of viral infections during antiviral therapy. Standard models applied to viral hepatitis describe the viral load decline in the f irst 2-4 wk of antiviral therapy, but do not adequately simulate the dynamics of viral infection for the following period. The hypothesis of a constant clearance rate of the infected cells provides an unrealistic estimation of the time necessary to reach the control or the clearance of hepatitis B virus (HBV)/ hepatitis C virus (HCV) infection. To overcome the problem, we have developed a new multiphasic model in which the immune system activity is modulated by a negative feedback caused by the infected cells reduction, and alanine aminotransferase kinetics serve as a surrogate marker of infected-cell clearance. By this approach, we can compute the dynamics of infected cells during the whole treatment course, and find a good correlation between the number of infected cells at the end of therapy and the long-term virological response in patients with chronic hepatitis C. The new model successfully describes the HBV infection dynamics far beyond the third month of antiviral therapy under the assumption that the sum of infected and non-infected cells remains roughly constant during therapy, and both target and infected cells concur in the hepatocyte turnover. In clinical practice, these new models will allow the development of simulators of treatment response that will be used as an "automatic pilot" for tailoring antiviral therapy in chronic hepatitis B as well as chronic hepatitis C patients.
文摘BACKGROUND Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infections is diagnosed via real time reverse transcriptase polymerase chain reaction(RT-PCR)and reported as a binary assessment of the test being positive or negative.High SARS-CoV-2 viral load is an independent predictor of disease severity and mortality.Quantitative RT-PCR may be useful in predicting the clinical course and prognosis of patients diagnosed with coronavirus disease 2019(COVID-19).AIM To identify whether quantitative SARS-CoV-2 viral load assay correlates with clinical outcome in COVID-19 infections.METHODS A systematic literature search was undertaken for a period between December 30,2019 to December 31,2020 in PubMed/MEDLINE using combination of terms“COVID-19,SARS-CoV-2,Ct values,Log_(10) copies,quantitative viral load,viral dynamics,kinetics,association with severity,sepsis,mortality and infectiousness”.After screening 990 manuscripts,a total of 60 manuscripts which met the inclusion criteria were identified.Data on age,number of patients,sample sites,RT-PCR targets,disease severity,intensive care unit admission,mortality and conclusions of the studies was extracted,organized and is analyzed.RESULTS At present there is no Food and Drug Administration Emergency Use Authorization for quantitative viral load assay in the current pandemic.The intent of this research is to identify whether quantitative SARS-CoV-2 viral load assay correlates with severity of infection and mortality?High SARS-CoV-2 viral load was found to be an independent predictor of disease severity and mortality in majority of studies,and may be useful in COVID-19 infection in susceptible individuals such as elderly,patients with co-existing medical illness such as diabetes,heart diseases and immunosuppressed.High viral load is also associated with elevated levels of TNF-α,IFN-γ,IL-2,IL-4,IL-6,IL-10 and C reactive protein contributing to a hyper-inflammatory state and severe infection.However there is a wide heterogeneity in fluid samples and different p
