RESUMEN
Viruses can be spread from one person to another; therefore, they may cause disorders in many people, sometimes leading to epidemics and even pandemics. New, previously unstudied viruses and some specific mutant or recombinant variants of known viruses constantly appear. An example is a variant of coronaviruses (CoV) causing severe acute respiratory syndrome (SARS), named SARS-CoV-2. Some antiviral drugs, such as remdesivir as well as antiretroviral drugs including darunavir, lopinavir, and ritonavir are suggested to be effective in treating disorders caused by SARS-CoV-2. There are data on the utilization of antiretroviral drugs against SARS-CoV-2. Since there are many studies aimed at the identification of the molecular mechanisms of human immunodeficiency virus type 1 (HIV-1) infection and the development of novel therapeutic approaches against HIV-1, we used HIV-1 for our case study to identify possible molecular pathways shared by SARS-CoV-2 and HIV-1. We applied a text and data mining workflow and identified a list of 46 targets, which can be essential for the development of infections caused by SARS-CoV-2 and HIV-1. We show that SARS-CoV-2 and HIV-1 share some molecular pathways involved in inflammation, immune response, cell cycle regulation.
Asunto(s)
Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/metabolismo , Minería de Datos/métodos , Infecciones por VIH/epidemiología , Infecciones por VIH/metabolismo , Interacciones Huésped-Patógeno/inmunología , Pandemias , Neumonía Viral/epidemiología , Neumonía Viral/metabolismo , Antiinflamatorios/uso terapéutico , Antígenos de Diferenciación/genética , Antígenos de Diferenciación/inmunología , Antivirales/uso terapéutico , Betacoronavirus/efectos de los fármacos , Betacoronavirus/inmunología , Betacoronavirus/patogenicidad , COVID-19 , Proteínas del Sistema Complemento/genética , Proteínas del Sistema Complemento/inmunología , Infecciones por Coronavirus/tratamiento farmacológico , Infecciones por Coronavirus/inmunología , Bases de Datos Genéticas , Regulación de la Expresión Génica , Infecciones por VIH/tratamiento farmacológico , Infecciones por VIH/inmunología , VIH-1/efectos de los fármacos , VIH-1/inmunología , VIH-1/patogenicidad , Interacciones Huésped-Patógeno/efectos de los fármacos , Interacciones Huésped-Patógeno/genética , Humanos , Inmunidad Innata/efectos de los fármacos , Factores Inmunológicos/uso terapéutico , Inflamación , Interferones/genética , Interferones/inmunología , Interleucinas/genética , Interleucinas/inmunología , Redes y Vías Metabólicas/efectos de los fármacos , Redes y Vías Metabólicas/genética , Redes y Vías Metabólicas/inmunología , Neumonía Viral/tratamiento farmacológico , Neumonía Viral/inmunología , Proteínas Represoras/genética , Proteínas Represoras/inmunología , SARS-CoV-2 , Transducción de Señal , Receptores Toll-Like/genética , Receptores Toll-Like/inmunología , Ubiquitina-Proteína Ligasas/genética , Ubiquitina-Proteína Ligasas/inmunologíaRESUMEN
INTRODUCTION: Medical statistics is one of the "milestones" of current medical systems. It is the foundation for many protocols, including medical care systems, government recommendations, epidemic planning, etc. At this time of global COVID-19, credible data on epidemic spread can help governments make better decisions. This study's aim is to evaluate the cyclicity in the number of daily diagnosed coronavirus patients, thus allowing governments to plan how to allocate their resources more effectively. METHODS: To assess this cycle, we consider the time series of the first and second differences in the number of registered patients in different countries. The spectral densities of the time series are calculated, and the frequencies and amplitudes of the maximum spectral peaks are estimated. RESULTS: It is shown that two types of cycles can be distinguished in the time series of the case numbers. Cyclical fluctuations of the first type are characterized by periods from 100 to 300 days. Cyclical fluctuations of the second type are characterized by a period of about seven days. For different countries, the phases of the seven-day fluctuations coincide. It is assumed that cyclical fluctuations of the second type are associated with the weekly cycle of population activity. CONCLUSIONS: These characteristics of cyclical fluctuations in cases can be used to predict the incidence rate.