Prevalence and Dynamics of SARS-CoV-2 Antibodies in the Population of St. Petersburg, Russia.

BACKGROUND: The aim of the study was to assess the prevalence of seropositive status for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-IgA, -IgM, and -IgG; its dynamics in connection with restrictive measures during the coronavirus disease (COVID-19) pandemic; and the quantitative dynamics of antibody levels in the population of St. Petersburg, Russia. METHODS: From May to November 2020, a retrospective analysis of Saint Petersburg State University Hospital laboratory database was performed. The database included 158,283 test results of 87,067 patients for SARS-CoV-2 detection by polymerase chain reaction (PCR) and antibody detection of SARS-CoV-2-IgA, -IgM, and -IgG. The dynamics of antibody level was assessed using R v.3.6.3. RESULTS: The introduction of a universal lockdown was effective in containing the spread of COVID-19. The proportion of seropositive patients gradually decreased; approximately 50% of these patients remained seropositive for IgM after 3-4 weeks; for IgG, by follow-up week 22; and for IgA, by week 12. The maximum decrease in IgG and IgA was observed 3-4 months and 2 months after the detection of the seropositive status, respectively. CONCLUSIONS: The epidemiological study of post-infection immunity to COVID-19 demonstrates significant differences in the dynamics of IgA, IgM, and IgG seropositivity and in PCR test results over time, which is linked to the introduction of restrictive measures. Both the proportion of seropositive patients and the level of all antibodies decreased in terms of the dynamics, and only approximately half of these patients remained IgG-positive 6 months post-infection.

Convolutional neural network in proteomics and metabolomics for determination of comorbidity between cancer and schizophrenia.

The association between cancer risk and schizophrenia is widely debated. Despite many epidemiological studies, there is still no strong evidence regarding the molecular basis for the comorbidity between these two pathological conditions. The vast majority of assays have been performed using clinical records of schizophrenic patients or those undergoing cancer treatment and monitored for sufficient time to find shared features between the considered conditions. We performed mass spectrometry-based proteomic and metabolomic investigations of patients with different cancer phenotypes (breast, ovarian, renal, and prostate) and patients with schizophrenia. The resulting vast quantity of proteomic and metabolomic data were then processed using systems biology and one-dimensional (1D) convolutional neural network (1DCNN) machine learning approaches. Traditional systematic approaches permit the segregation of schizophrenia and cancer phenotypes on the level of biological processes, while 1DCNN recognized "signatures" that could segregate distinct cancer phenotypes and schizophrenia at the comorbidity level. The designed network efficiently discriminated unrelated pathologies with a model accuracy of 0.90 and different subtypes of oncophenotypes with an accuracy of 0.94. The proposed strategy integrates systematic analysis of identified compounds and application of 1DCNN model for unidentified ones to reveal the similarity between distinct phenotypes.