Albeit the pathogenesis of COVID-19 remains unclear, host’s genetic polymorphisms in genes involved in infection and reinfection, inflammation, or immune stimulation could play a role in determining the course and outcome. We studied in the early phase of pandemic consecutive patients (N = 383) with SARS-CoV-2 infection, whose subsequent clinical course was classified as mild or severe, the latter being characterized by admission to intensive therapy unit or death. Five host gene polymorphisms (MERTK rs4374383, PNPLA3 rs738409, TLL-1 rs17047200, IFNL3 rs1297860, and INFL4 rs368234815) were assessed by using whole nucleic acids extracted from nasopharyngeal swabs. Specific protease cleavage sites of TLL-1 on the SARS-CoV-2 Spike protein were predicted in silico. Male subjects and older patients were significantly at higher risk for a severe outcome (p = 0.02 and p < 0.001, respectively). By considering patients ≤65 years, after adjusting for potential confounding due to sex, an increased risk of severe outcome was found in subjects with the GG genotype of PNPLA3 (adj-OR: 4.69; 95% CI = 1.01–22.04) or TT genotype of TLL-1 (adj-OR=9.1; 95% CI = 1.45–57.3). In silico evaluation showed that TLL-1 is potentially involved in the Spike protein cleavage which is essential for viral binding and entry into the host cells using the host receptor angiotensin-converting enzyme 2 (ACE2). Subjects carrying a GG genotype in PNPLA3 gene might have a constitutive upregulation of the NLRP3 inflammasome and be more prone to tissue damage when infected by SARS-CoV-2. The TT genotype in TLL-1 gene might affect its protease activity on the SARS-CoV-2 Spike protein, enhancing the ability to infect or re-infect host’s cells. The untoward effect of these variants on disease course is evident in younger patients due to the relative absence of comorbidities as determinants of prognosis. In the unresolved pathogenetic scenery of COVID-19, the identification of genetic variants associates with more prolonged course or with a severe outcome of infection would support the development of predictive tools useful to stratify subjects by risk class at presentation. Moreover, the individuation of key genes could contribute to a better understanding of the pathways involved in the pathogenesis, giving the basis for rational therapeutic approaches.

COVID-19 is a current global threat, and the characterization of antibody response is vitally important to update vaccine development and strategies. In this study we assessed SARS-CoV-2 antibody concentrations in SARS-CoV-2 positive patients (N = 272) and subjects vaccinated with the BNT162b2 m-RNA COVID-19 vaccine (N = 1256). For each participant, socio-demographic data, COVID-19 vaccination records, serological analyses, and SARS-CoV-2 infection status were collected. IgG antibodies against S1/S2 antigens of SARS-CoV-2 were detected. Almost all vaccinated subjects (99.8%) showed a seropositivity to anti-SARS-COV-2 IgG and more than 80% of vaccinated subjects had IgG concentrations > 200 AU/mL. In a Tobit multivariable regression analysis, SARS-CoV-2 vaccination was statistically significantly associated with increased IgG concentrations (β coef = 266.4; p < 0.001). A statistically significant reduction in SARS-CoV-2 IgG concentrations was found with older age (β coef = −1.96 per year increase; p < 0.001), male sex (β coef = −22.3; p < 0.001), and days after immunization (β coef = −1.67 per day increase; p < 0.001). Our findings could support the vaccination campaigns confirming the high immunogenicity of the SARS-CoV-2 vaccine under investigation with respect to the natural infection. Further studies will be required for evaluating the role of age and days after immunization in the persistence of vaccine antibodies and protection from the disease.

This study reinforces and extends previous conclusions concerning the success of federally funded MSTPs in producing physician scientists who compete favorably for NIH funding.

On December 31, 2019, an outbreak of lower respiratory infections was documented in Wuhan caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the beginning, SARS-CoV-2 has caused many infections among healthcare workers (HCWs) worldwide. Aims of this study were: a. to compare the distribution among the HCWs and the general population of SARS-CoV-2 infections in Western Sicily and Italy; b. to describe the characteristics of HCWs infected with SARS-CoV-2 in the western Sicilian healthcare context during the first wave of the epidemic diffusion in Italy. Incidence and mean age of HCWs infected with SARS-CoV-2 were comparable in Western Sicily and in the whole Italian country. The 97.6% of infections occurred in HCWs operating in non-coronavirus disease 2019 (COVID-19) working environments, while an equal distribution of cases between hospital and primary care services context was documented. Nurses and healthcare assistants, followed by physicians, were the categories more frequently infected by SARS-CoV-2. The present study suggests that healthcare workers are easily infected compared to the general population but that often infection could equally occur in hospital and non-hospital settings. Safety of HCWs in counteracting the COVID-19 pandemic must be strengthened in hospital [adequate provision of personal protective equipment (PPE), optimization of human resources, implementation of closed and independent groups of HCWs, creation of traffic control building and dedicated areas in every healthcare context] and non-hospital settings (influenza vaccination, adequate psychophysical support, including refreshments during working shifts, adequate rest, and family support).

The aim of the study is to show whether it is possible to predict infectious disease outbreaks early, by using machine learning. This study was carried out following the guidelines of the Cochrane Collaboration and the meta-analysis of observational studies in epidemiology and the preferred reporting items for systematic reviews and meta-analyses. The suitable bibliography on PubMed/Medline and Scopus was searched by combining text, words, and titles on medical topics. At the end of the search, this systematic review contained 75 records. The studies analyzed in this systematic review demonstrate that it is possible to predict the incidence and trends of some infectious diseases; by combining several techniques and types of machine learning, it is possible to obtain accurate and plausible results.