[Patterns of experienced and anticipated discrimination in patients with schizophremia. Italian results from the INDIGO international multisite project]

To describe patterns of experienced and anticipated discrimination in a sample of schizophrenic patients recruited in Italy in the context of the International Study of Discrimination and Stigma Outcomes (INDIGO)

Impact of antiretroviral dosing and daily pill burden on viral rebound rates in naive patients receiving a tenofovir-based regimen

Methods A total of 480 ART-naive patients were selected from the GNOMO cohort. Incidence rate of viral rebound (VR = first of two consecutive VL>50 cp/ml) was calculated as number of events over PYFU and expressed at univariate and multivariate analysis as incidence rate ratio (IRR). Number of both pills and doses per day were used to define three different types of regimens: twice-a-day regimens (BID regimens); once-a-day regimens with 3 pills (high-pill QD [hp-QD]). Adjusted rates of viral rebound were estimated by Poisson regression using date of first HIV-RNA <50 c/ml as baseline. Follow-up was censored at the date of VR, death, or loss to follow-up

Modelling the Dynamics of an Aedes albopictus Population

We present a methodology for modelling population dynamics with formal means of computer science. This allows unambiguous description of systems and application of analysis tools such as simulators and model checkers. In particular, the dynamics of a population of Aedes albopictus (a species of mosquito) and its modelling with the Stochastic Calculus of Looping Sequences (Stochastic CLS) are considered. The use of Stochastic CLS to model population dynamics requires an extension which allows environmental events (such as changes in the temperature and rainfalls) to be taken into account. A simulator for the constructed model is developed via translation into the specification language Maude, and used to compare the dynamics obtained from the model with real data.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314

Evaluation of virological response and resistance profile in HIV-1 infected patients starting a first-line integrase inhibitor-based regimen in clinical settings

Background: Virological response and resistance profile were evaluated in drug-naïve patients starting their first-line integrase inhibitors (INIs)-based regimen in a clinical setting. Study design: Virological success (VS) and virological rebound (VR) after therapy start were assessed by survival analyses. Drug-resistance was evaluated at baseline and at virological failure. Results: Among 798 patients analysed, 38.6 %, 27.1 % and 34.3 % received raltegravir, elvitegravir and dolutegravir, respectively. Baseline resistance to NRTIs, NNRTIs, PIs and INIs was: 3.9 %, 13.9 %, 1.6 % and 0.5 %, respectively. Overall, by 12 months of treatment, the probability of VS was 95 %, while the probability of VR by 36 months after VS was 13.1 %. No significant differences in the virological response were found according to the INI used. The higher pre-therapy viremia strata was (&lt;100,000 vs. 100,000-500,000 vs. &gt; 500,000 copies/mL), lower was the probability of VS (96.0 % vs. 95.2 % vs. 91.1 %, respectively, P &lt; 0.001), and higher the probability of VR (10.2 % vs. 15.8 % vs. 16.6 %, respectively, P = 0.010). CD4 cell count &lt;200 cell/mm3 was associated with the lowest probability of VS (91.5 %, P &lt; 0.001) and the highest probability of VR (20.7 %, P = 0.008) compared to higher CD4 levels. Multivariable Cox-regression confirmed the negative role of high pre-therapy viremia and low CD4 cell count on VS, but not on VR. Forty-three (5.3 %) patients experienced VF (raltegravir: 30; elvitegravir: 9; dolutegravir: 4). Patients failing dolutegravir did not harbor any resistance mutation either in integrase or reverse transcriptase. Conclusions: Our findings confirm that patients receiving an INI-based first-line regimen achieve and maintain very high rates of VS in clinical practice

An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome.A multifaceted computational strategy identifies 16 genetic variants contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing dataset of a cohort of Italian patients

The polymorphism L412F in TLR3 inhibits autophagy and is a marker of severe COVID-19 in males

The polymorphism L412F in TLR3 has been associated with several infectious diseases. However, the mechanism underlying this association is still unexplored. Here, we show that the L412F polymorphism in TLR3 is a marker of severity in COVID-19. This association increases in the sub-cohort of males. Impaired macroautophagy/autophagy and reduced TNF/TNFα production was demonstrated in HEK293 cells transfected with TLR3L412F-encoding plasmid and stimulated with specific agonist poly(I:C). A statistically significant reduced survival at 28 days was shown in L412F COVID-19 patients treated with the autophagy-inhibitor hydroxychloroquine (p = 0.038). An increased frequency of autoimmune disorders such as co-morbidity was found in L412F COVID-19 males with specific class II HLA haplotypes prone to autoantigen presentation. Our analyses indicate that L412F polymorphism makes males at risk of severe COVID-19 and provides a rationale for reinterpreting clinical trials considering autophagy pathways. Abbreviations: AP: autophagosome; AUC: area under the curve; BafA1: bafilomycin A1; COVID-19: coronavirus disease-2019; HCQ: hydroxychloroquine; RAP: rapamycin; ROC: receiver operating characteristic; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; TLR: toll like receptor; TNF/TNF-α: tumor necrosis factor

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

A first update on mapping the human genetic architecture of COVID-19

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