D2D Communication Underlaying UAV-Enabled Network: A Content-Sharing Perspective

The last era has witnessed an unprecedented demand for digital content. To meet these rigorous demands, researchers have been busy developing content-sharing applications and services. The advancement in technology has aided this process. Unmanned aerial vehicles (UAVs) have gained a lot of attention in assisting cellular networks since they play a paramount role in disaster management, capacity enhancement, on-demand communication, and content dissemination. In this study, we consider content-centric UAV communication underlaid device-to-device (D2D) users. Different from the current research trends, this study considers clustering the D2D users (i.e., ground users) and UAV only deliver the requested content to the cluster heads. We considered the clustering approach since the UAV is an energy constraint device and the aim is to reduce the energy consumed by the UAV during the communication phase. Clustering the ground nodes will allow the UAV to communicate to only cluster heads as compared with a bigger group of users. Cluster heads are then responsible to forward the cached contents to their respective cluster members. A comprehensive performance evaluation of the proposed scheme was conducted by benchmarking it against state-of-the-art research works and considering various performance parameters such as throughput, energy consumption, and content delivery delay. The proposed scheme produced promising results for all parameters and against other research works as well

Clinical characteristics, mortality and associated risk factors in COVID-19 patients reported in ten major hospitals of Khyber Pakhtunkhwa, Pakistan

COVID-19 is an ongoing public health issue across the world. Several risk factors associated with mortality in COVID-19 have been reported. The present study aims to describe clinical and epidemiological characteristics and predictors of mortality in hospitalized patients from Khyber Pakhtunkhwa, a province in Pakistan with highest COVID-19 associated case fatality rate. This multicentre, retrospective study was conducted in hospitalized COVID-19 patients who died or discharged alive until 1st May 2020. Data about sociodemographic characteristics, clinical and laboratory findings, treatment and outcome were obtained from hospital records and compared between survivors and non-survivors. Statistical tests were applied to determine the risk factors associated with mortality in hospitalized patients. Of the total 179 patients from the 10 designated hospitals, 127 (70.9%) were discharged alive while 52 (29.1%) died in the hospital. Overall, 109 (60.9%) patients had an underlying comorbidity with hypertension being the commonest. Multivariate logistics regression analysis showed significantly higher odds of in-hospital death from COVID-19 in patients with multiple morbidities (OR 3.2, 95% CI 1.1, 9.1, p-value=0.03), length of hospital stay (OR 0.8, 95% CI 0.7, 0.9, p-value <0.001), those presenting with dyspnoea (OR 4.0, 95% CI 1.1, 14.0, p-value=0.03) and oxygen saturation below 90 (OR 9.6, 95% CI: 3.1, 29.2, p-value <0.001). Comorbidity, oxygen saturation and dyspnoea on arrival and length of stay in hospital (late admission) are associated with COVID-19 mortality. The demographic, clinical and lab characteristics could potentially help clinician and policy makers before potential second wave in the country

Whole-genome sequencing reveals host factors underlying critical COVID-19

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease