A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death

The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10−8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10−8). A total of 113 variants were associated with survival at P-value < 1.0 × 10−5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways

Clinical features and therapeutic management of patients admitted to Italian acute hospital psychiatric units: the PERSEO (psychiatric emergency study and epidemiology) survey

<p>Abstract</p> <p>Background</p> <p>The PERSEO study (psychiatric emergency study and epidemiology) is a naturalistic, observational clinical survey in Italian acute hospital psychiatric units, called SPDCs (Servizio Psichiatrico Diagnosi e Cura; in English, the psychiatric service for diagnosis and management). The aims of this paper are: (i) to describe the epidemiological and clinical characteristics of patients, including sociodemographic features, risk factors, life habits and psychiatric diagnoses; and (ii) to assess the clinical management, subjective wellbeing and attitudes toward medications.</p> <p>Methods</p> <p>A total of 62 SPDCs distributed throughout Italy participated in the study and 2521 patients were enrolled over the 5-month study period.</p> <p>Results</p> <p>Almost half of patients (46%) showed an aggressive behaviour at admission to ward, but they engaged more commonly in verbal aggression (38%), than in aggression toward other people (20%). A total of 78% of patients had a psychiatric diagnosis at admission, most frequently schizophrenia (36%), followed by depression (16%) and personality disorders (14%), and no relevant changes in the diagnoses pattern were observed during hospital stay. Benzodiazepines were the most commonly prescribed drugs, regardless of diagnosis, at all time points. Overall, up to 83% of patients were treated with neuroleptic drugs and up to 27% received more than one neuroleptic either during hospital stay or at discharge. Atypical and conventional antipsychotics were equally prescribed for schizophrenia (59 vs 65% during stay and 59 vs 60% at discharge), while atypical drugs were preferred in schizoaffective psychoses (72 vs 49% during stay and 70 vs 46% at discharge) and depression (41 vs 32% during stay and 44 vs 25% at discharge). Atypical neuroleptics were slightly preferred to conventional ones at hospital discharge (52 vs 44%). Polypharmacy was in general widely used. Patient attitudes toward medications were on average positive and self-reported compliance increased during hospital stay.</p> <p>Conclusion</p> <p>Results confirm the widespread use of antipsychotics and the increasing trend in atypical drugs prescription, in both psychiatric in- and outpatients.</p

Light extinction and scattering from aggregates composed of submicron particles

The influence of the internal structure of inhomogeneous particles on their radiative properties is an open issue repeatedly questioned in many fields of science and technology. The importance of a refined description of the particle composition and structure, going beyond mean-field approximations, is generally recognized. Here, we focus on describing internal inhomogeneities from a statistical point of view. We introduce an analytical description based on the two-point density-density correlation function, or the corresponding static structure factor, to calculate the extinction cross sections. The model agrees with numerical predictions and is validated experimentally with colloidal aggregates in the 0.3\u20136&nbsp;\u3bcm size range, which serve as an inhomogeneous model system that can be characterized enough to work without any free parameters. The model can be tightly compared to measurements with single particle extinction and scattering and spectrophotometry and suggests a simple behavior for 90\ub0 scattering from fractal aggregates as a function of extinction, which is also confirmed experimentally and numerically. We also discuss the case of absorbing particles and report the experimental results for water suspensions of black carbon for both the forward and 90\ub0 scattering properties. In this case, the total scattering and the extinction cross sections determine the single scattering albedo, which agrees with numerical simulations. The three parameters necessary to feed radiative transfer models, namely, extinction, asymmetry parameter, and single scattering albedo, can all be set by the analytical model, with explicit dependence on a few parameters. Results are applicable to radiative transfer problems in climate, paleoclimate, star and planetary formation, and nanoparticle optical characterization for science and industry, including the intercomparison of different optical methods such as those adopted by ISO standards

Securing freshmen’s learning through a Physics refresher course: a breakthrough experience at Politecnico di Milano

Nowadays, an academic institution faces the challenge of a not negligible number of undergraduates who drop out. To organise welcome sessions for first-year students appears to be effective in increasing their persistence. For several years Politecnico di Milano has provided a Physics refresher course to its new freshmen, which is part of a much broader pedagogical project consisting in offering a blended course which integrates this face-to-face activity with Massive Open Online Courses (MOOCs), delivered through Politecnico di Milano MOOCs portal. Held in September and ending before the beginning of the students’ academic programme, this two-week course focuses on classical Physics. Freshmen are arranged in different sections depending on their own free choice. In the 2018 edition of this Physics refresher course an ad hoc multiple-choice test was administered to the participants through the online portal Socrative during the first lesson. Besides fostering a renewed interest in this discipline, this test was intended by us as a means that would enable the teacher to evaluate the students’ level of understanding rather than their knowledge in basic Physics and would allow for the freshmen’s self-assessment. Considering the positive results of BYOD (Bring Your Own Device) strategy, the students were highly encouraged to use their own electronic devices to take the multiple-choice test. In order to evaluate the effectiveness of the Physics refresher course, the students who got involved in the test and took the classes were monitored closely with relation to their findings in the undergraduate basic Physics course, which they attended during their first academic year. On the one hand, the average score achieved by these students is higher than the mark attained by the overall freshmen who attended a university Physics course at Politecnico di Milano. On the other hand, also their success rate is higher. On balance, not only does the Physics refresher course appear to be effective in increasing the new freshmen initial level of knowledge in Physics, but at the same time it is likely to increase the attentiveness to this discipline

Unexplained somatic symptoms during major depression: prevalence and clinical impact in a national sample of Italian psychiatric outpatients

Background: The aim of this study was to explore the prevalence and impact of unexplained somatic symptoms during major depression. Sampling and Methods: A total of 560 consecutive outpatients with a major depressive episode according to the DSM-IV (text revision) were evaluated in 30 psychiatric facilities throughout Italy. 'Unexplained' somatic symptoms were evaluated using the 30-item Somatic Symptoms Checklist (SSCL-30). Somatic symptoms were considered explained if they were best accounted for as coming from a concomitant physical illness or side effects. Patients evaluated their own mood symptomatology using the Zung questionnaires for depression and anxiety and the Hypomania Checklist-32. Results: According to the SSCL-30, only 90 subjects (16.1%) had no unexplained somatic symptoms, while 231 (41.3%) had 1-5 unexplained symptoms and 239 (42.7%) had more than 5. Asthenia was the most commonly observed unexplained somatic symptom (53% of patients). Unexplained somatic symptoms were more common in females and among those suffering from major depression and depression not otherwise specified rather than in patients with recurrent major depression and bipolar disorders. No relationship between unexplained somatic symptoms and hypomanic features was observed. Conclusions: The presence of a large number of unexplained somatic symptoms is associated with more severe depression and higher rates of misdiagnosis and inappropriate treatment

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management