SARS-CoV-2 has emerged as a human pathogen, causing clinical signs, from fever to pneumonia-COVID-19-but may remain mild or asymptomatic. To understand the continuing spread of the virus, to detect those who are and were infected, and to follow the immune response longitudinally, reliable and robust assays for SARS-CoV-2 detection and immunological monitoring are needed. We quantified IgM, IgG, and IgA antibodies recognizing the SARS-CoV-2 receptor-binding domain (RBD) or the Spike (S) protein over a period of 6 months following COVID-19 onset. We report the detailed setup to monitor the humoral immune response from over 300 COVID-19 hospital patients and healthcare workers, 2500 University staff, and 198 post-COVID-19 volunteers. Anti-SARS-CoV-2 antibody responses follow a classic pattern with a rapid increase within the first three weeks after symptoms. Although titres reduce subsequently, the ability to detect anti-SARS-CoV-2 IgG antibodies remained robust with confirmed neutralization activity for up to 6 months in a large proportion of previously virus-positive screened subjects. Our work provides detailed information for the assays used, facilitating further and longitudinal analysis of protective immunity to SARS-CoV-2. Importantly, it highlights a continued level of circulating neutralising antibodies in most people with confirmed SARS-CoV-2.

SARS-CoV-2 has emerged as a novel human pathogen, causing clinical signs, from fever to pneumonia - COVID-19 - but may remain mild or even asymptomatic. To understand the continuing spread of the virus, to detect those who are and were infected, and to follow the immune response longitudinally, reliable and robust assays for SARS-CoV-2 detection and immunological monitoring are needed and have been setup around the world. We quantified immunoglobulin M (IgM), IgG and IgA antibodies recognizing the SARS-CoV-2 receptor-binding domain (RBD) or the Spike (S) protein over a period of five months following COVID-19 disease onset or in previously SARS-CoV-2 PCR-positive volunteers. We report the detailed setup to monitor the humoral immune response from over 300 COVID-19 hospital patients and healthcare workers, 2500 University staff and 187 post-COVID19 volunteers, and assessing titres for IgM, IgG and IgA. Anti-SARS-CoV-2 antibody responses followed a classic pattern with a rapid increase within the first three weeks after symptoms. Although titres reduce from approximately four weeks, the ability to detect SARS-CoV-2 antibodies remained robust for five months in a large proportion of previously virus-positive screened subjects. Our work provides detailed information for the assays used, facilitating further and longitudinal analysis of protective immunity to SARS-CoV-2. Moreover, it highlights a continued level of circulating neutralising antibodies in most people with confirmed SARS-CoV-2, at least up to five months after infection.

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This work provides an overview and appraisal of the general evolution of IS/IT in haemovigilance, from which lessons can be learned for its future strategic management. An electronic survey was conducted among the members of the International Haemovigilance Network to compile information on the mechanisms implemented to gather, process, validate, and store these data, to monitor haemovigilance activity, and to produce analytical reports. Survey responses were analysed by means of descriptive statistics, and comments/observations were considered in the final discussion. The answers received from 23 haemovigilance organizations show a direct relationship between the number of collected notifications (i.e., communication of adverse effects and events) and the technical specifications of the haemovigilance system in use. Notably, IT is used in the notification reception of 17 of these systems, out of which 8 systems are exclusively based on Web solutions. Most assessments of the evolution of IS/IT tend to focus on the scalability and flexibility of data gathering and reporting, considering the ever-changing requirements of haemovigilance. Data validation is poorly implemented, and data reporting has not reached its full potential. Web-based solutions are seen as the most intuitive and flexible for a system-user interaction.

IntroductionSchizophrenia (SCZ) patients are reported to present significant abnormalities in lipid and glucose metabolism, that increase the risk for cardiovascular disease and diabetes, possibly induced by antipsychotic therapy (APT) and lifestyle.Objectives/AimsCharacterize a sample of SCZ patients relating APT and biochemical laboratory profile.MethodsWe conducted a retrospective longitudinal study of SCZ patients’ records from Psychiatry Service of Hospital S.João (Porto, Portugal) from 2009 to 2011.ResultsThe study included 51 SCZ patients of which 82.4% (n = 42) were male, presenting a mean age of 39.3 ± 9.2 years. The most frequent subtype of SCZ was paranoid (90.0%). The average age of diagnosis was 24.5 ± 8.4 years old. Oral APT was prescribed to 48 patients. Depot APT were prescribed to 40 patients. Haloperidol and risperidona were in both cases the most frequent drugs. 50% of patients presented high total cholesterol values (≥200 mg/dL), 25% high LDL cholesterol (≥160 mg/dL), 81% low HDL cholesterol (≤40/50 mg/dL) and 44% presented high triglycerides results (≥150 mg/dL). 27% of patients presented impaired fasting glucose values (110 to 125 mg/dl) and 27% presented at least one plasma glucose result that meet the criteria for diabetes diagnosis (≥126 mg/dl). There was an overall increase in hepatic enzymes (54% ALT, 36% AST and GGT).ConclusionsHepatic metabolism of APT may explain elevated liver enzimes. Lipid and glucose profiles in our sample clearly indicate the need of monitoring SCZ patients, managing APT side effects and lowering risk. We encourage clinicians to record metabolic monitoring and to establish corrective measures (food, lifestyle and medication).

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This work introduces HaemoKBS, a novel Haemovigilance decision support system for adverse reactions in blood recipients. Machine learning inference and rule-based reasoning were applied to build the underlying decision support models, namely to automatically extract evidence from different types of data included in hospital notifications and incorporate a priori expert knowledge. The ultimate aim is to dynamically learn and improve the reasoning abilities of the system and thus, be able to provide educated recommendations to hospital notifiers along with understandable explanations on the acquired knowledge. Experiments over the records of the Portuguese National Haemovigilance System from the last 10 years demonstrate the practical usefulness of HaemoKBS, which will contribute to a better depiction of the adverse reactions and to flag any incomplete notification enforcing data quality.