Background: COVID-19 is an infectious disease caused by a novel coronavirus (SARS-CoV-2). The immunopathogenesis of the infection is currently unknown. Healthcare workers (HCWs) are at highest risk of infection and disease. Aim of the study was to assess the sero-prevalence of SARS-CoV-2 in an Italian cohort of HCWs exposed to COVID-19 patients. Methods: A point-of-care lateral flow immunoassay (BioMedomics IgM-IgG Combined Antibody Rapid Test) was adopted to assess the prevalence of IgG and IgM against SARS-CoV-2. It was ethically approved (“Milano Area 1” Ethical Committee prot. n. 2020/ST/057). Results: A total of 202 individuals (median age 45 years; 34.7% males) were retrospectively recruited in an Italian hospital (Milan, Italy). The percentage (95% CI) of recruited individuals with IgM and IgG were 14.4% (9.6–19.2%) and 7.4% (3.8–11.0%), respectively. IgM were more frequently found in males (24.3%), and in individuals aged 20–29 (25.9%) and 60–69 (30.4%) years. No relationship was found between exposure to COVID-19 patients and IgM and IgG positivity. Conclusions: The present study did show a low prevalence of SARS-CoV-2 IgM in Italian HCWs. New studies are needed to assess the prevalence of SARS-CoV-2 antibodies in HCWs exposed to COVID-19 patients, as well the role of neutralizing antibodies.

COVID-19 dramatically reshaped mortality across Europe. This study aimed to assess its impact on total mortality in European countries taking into consideration the relationship with selected country-level socioeconomic indicators, non-pharmaceutical interventions, and vaccine uptake. We obtained weekly mortality data from 2010 to 2023 from the Short-term Mortality Fluctuations data series, the annual population data from the United Nations archives, selected sociodemographic and economic indicators from the World Bank’s database, the stringency index and the percentage of the population fully vaccinated from Our World in Data. A quasi-Poisson regression model trained on pre-pandemic years was used to estimate expected number of deaths in 2020–2023 in 29 European countries. Excess mortality was estimated using three different metrics: excess deaths (number), relative excess mortality (% different from expected deaths) and age-standardized excess death rate per 10,000 population. The relationship between socioeconomic indicators and excess mortality was evaluated using linear regression models, which included both linear and quadratic terms for the predictors to account for possible non-linear relationships. We estimated 1,642,586 excess deaths (95% confidence interval, CI: 1,607,161–1,678,010) across all countries over the four years (+8.0% compared to the expected number of deaths). Excess mortality was mainly concentrated in 2020–2022 (0.52 million excess deaths in 2020, 0.57 million in 2021 and 0.44 million in 2022), with no substantial excess (0.11 million) estimated for 2023. Over the period 2020–23, the highest number of excess deaths was estimated for Italy (227,736 deaths, +8.7%), Poland (223,735 deaths, +13.7%), and Germany (218,111 deaths, +5.6%), while the highest excesses in relative terms were in Bulgaria (72,328 deaths, +17.2%), Lithuania (23,813 deaths, +16.1%), and Slovakia (31,984 deaths, +14.9%). The age-standardised death rates ranged from 1.8 per 10,000 population in Sweden to 24.7 in Bulgaria. The percentage of the population living below the poverty line and the Gini index were significantly associated with an increased excess death rate, with p-values for the linear and quadratic terms being 0.003 and 0.003 for the Gini index, and 0.024 and 0.017 for the population living below the poverty line. Conversely, gross domestic product per capita (p-values for the linear and quadratic terms: <0.001, 0.003), health expenditure (0.001, 0.273) and the percentage of people fully vaccinated by the end of 2021 (<0.001, 0.989) or 2022 (0.001, 0.890) were inversely associated with excess death rate. No significant association was observed with population density and stringency index. The observed geographic disparities in total mortality excess across Europe can be related to differences in socioeconomic contexts, as well as to suboptimal vaccine uptakes in some countries. This research was supported by European Union (EU) funding within the NextGeneration EU-MUR PNRR Extended Partnership initiative on Emerging Infectious Diseases (Project no. PE00000007, INF-ACT). The funding source had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

To date, the European experience with COVID-19 mortality has been different to that observed in China and Asia. We aimed to forecast mortality trends in the 27 countries of the European Union (EU), plus Switzerland and the UK, where lockdown dates and confinement interventions have been heterogeneous, and to explore its determinants. We have adapted our predictive model of COVID-19-related mortality, which rested on the observed mortality within the first weeks of the outbreak and the date of the respective lockdown in each country. It was applied in a training set of three countries (Italy, Germany and Spain), and then applied to the EU plus the UK and Switzerland. In addition, we explored the effects of timeliness and rigidity of the lockdown (on a five-step scale) and population density in our forecasts. We report r², and percent variation of expected versus observed deaths, all following TRIPOD guidance. We identified a homogeneous distribution of deaths, and found a median of 24 days after lockdown adoption to reach the maximum daily deaths. Strikingly, cumulative deaths up to April 25^th, 2020 observed in Europe separated countries in three waves, according to the time lockdown measures were adopted following the onset of the outbreak: after a week, within a week, or even prior to the outbreak (r²=0.876). In contrast, no correlation neither with lockdown rigidity nor population density were observed. The European experience confirms that early, effective interventions of lockdown are fundamental to minimizing the COVID-19 death toll.

Beckwith-Wiedemann syndrome (BWS) is a clinically and genetically heterogeneous overgrowth disease. BWS is caused by (epi)genetic defects at the 11p15 chromosomal region, which harbors two clusters of imprinted genes, IGF2/H19 and CDKN1C/KCNQ1OT1, regulated by differential methylation of imprinting control regions, H19/IGF2:IG DMR and KCNQ1OT1:TSS DMR, respectively. A subset of BWS patients show multi-locus imprinting disturbances (MLID), with methylation defects extended to other imprinted genes in addition to the disease-specific locus. Specific (epi)genotype-phenotype correlations have been defined in order to help clinicians in the classification of patients and referring them to a timely diagnosis and a tailored follow-up. However, specific phenotypic correlations have not been identified among MLID patients, thus causing a debate on the usefulness of multi-locus testing in clinical diagnosis. Finally, the high incidence of BWS monozygotic twins with discordant phenotypes, the high frequency of BWS among babies conceived by assisted reproductive technologies, and the female prevalence among BWS-MLID cases provide new insights into the timing of imprint establishment during embryo development. In this review, we provide an overview on the clinical and molecular diagnosis of single- and multi-locus BWS in pre- and post-natal settings, and a comprehensive analysis of the literature in order to define possible (epi)genotype-phenotype correlations in MLID patients.

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