A Randomized Clinical Trial to Evaluate the Efficacy and Safety of the ACTLIFE Exercise Program for Women with Post-menopausal Osteoporosis: Study Protocol

Osteoporosis (OP) is a systemic disease of the skeleton characterized by increased risk of fracture. There is a general consensus on the efficacy of physical activity in the prevention of bone loss, falls and fractures, but there is no agreement on the best setting to exercise. The aim of the study is to evaluate the efficacy of a 12-months exercise protocol for women with post-menopausal OP when administered as individual home training (IHT) versus gym group training (GGT). The study is a randomized trial with two parallel groups. Sedentary patients with primary post-menopausal osteoporosis are recruited at the Istituto Ortopedico Rizzoli of Bologna. In the first group, the 12-month ACTLIFE program is performed as IHT, while in the second as GGT. The program is aimed at improving joint mobility, muscle force, balance, motor coordination and endurance. The study is single blinded. Patients are assessed at baseline and after 6 and 12 months. The primary outcome is the modification of quality of life measured with the Short Osteoporosis Quality of Life Questionnaire (ECOS-16). The findings of this study will highlight advantages and disadvantages of exercising in the two different settings and provide evidence on how to increase physical activity in osteoporotic women

Revamping Cloud Gaming with Distributed Engines

While cloud gaming has brought considerable advantages for its customers, from the point of view of cloud providers, multiple aspects related to infrastructure management still fall short of such kind of service. Indeed, differently from traditional cloud-ready applications, modern game engines are still based on monolithic software architectures. This aspect precludes the applicability of fine-grained resource management and service orchestration schemes, ultimately leading to poor cost-effectiveness. To mitigate these shortcomings, we propose a Cloud-Oriented Distributed Engine for Gaming (CODEG). Thanks to its distributed nature, CODEG is capable of fully exploiting the resource heterogeneity present in cloud data centers, while providing the possibility of spanning its service on multiple network layers up to the edge clouds

Scale invariant disordered nanotopography promotes hippocampal neuron development and maturation with involvement of mechanotransductive pathways

The identification of biomaterials which promote neuronal maturation up to the generation of integrated neural circuits is fundamental for modern neuroscience. The development of neural circuits arises from complex maturative processes regulated by poorly understood signaling events,often guided by the extracellular matrix (ECM). Here we report that nanostructured zirconia surfaces,produced by supersonic cluster beam deposition of zirconia nanoparticles and characterized by ECM-like nanotopographical features,can direct the maturation of neural networks. Hippocampal neurons cultured on such cluster-assembled surfaces displayed enhanced differentiation paralleled by functional changes. The latter was demonstrated by single-cell electrophysiology showing earlier action potential generation and increased spontaneous postsynaptic currents compared to the neurons grown on the featureless unnaturally flat standard control surfaces. Label-free shotgun proteomics broadly confirmed the functional changes and suggests furthermore a vast impact of the neuron/nanotopography interaction on mechanotransductive machinery components,known to control physiological in vivo ECM-regulated axon guidance and synaptic plasticity. Our results indicate a potential of cluster-assembled zirconia nanotopography exploitable for the creation of efficient neural tissue interfaces and cell culture devices promoting neurogenic events,but also for unveiling mechanotransductive aspects of neuronal development and maturation

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

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. © 2021, The Author(s)

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

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