A model describing the microwave emission from a multi-layer snowpack at 37 GHz

A multilayer emission model is described and applied to emission measurements obtained at 37 GHz and H polarization using a microwave radiometer attached to a truck-mounted boom in Steamboat Springs, Colorado in 1977. Estimated absorption and scattering coefficients and their dependence on wetness were obtained using calculated values of the dielectric constant at 37 GHz along with the model. It was found that the scattering coefficient is comparable in value to the absorption coefficient for dry snow however, the absorption coefficient increases linearly with increasing snow wetness while the scattering coefficient decreases linearly with increasing wetness. The emission from each layer of the snowpack was also calculated using the estimated coefficients. It is shown that for dry snow, the ground underneath the snowpack contributes about 45% of all measured emission while the rest is due to emission from all the layers within the snowpack. When the wetness of the top 5 cm layer of snowpack increases to about 2% by volume, this top 5 cm snowlayer contributes more than 90% of all the measured emission

Intrahepatic expression of interferon alpha & interferon alpha receptor m-RNA can be used as predictors to interferon response in HCV and HCC patients

Chronic hepatitis C Virus (HCV) is the leading cause of liver cirrhosis worldwide and in Egypt. Patients with cirrhosis secondary to chronic HCV infection are at increased risk for developing Hepatocellular carcinoma (HCC) in which Interferon therapy is the only effective anti-viral therapy. The current study aimed to investigate the expression IFN-αand IFN-αReceptor genes in liver biopsies from patients with HCV and HCC. Correlation of their expression with the clinical, histopathological progress of the disease and the effectiveness of IFN therapy in HCV patients after a period of 6 months follow-up was done. Expression of IFN-α and IFNα-Rc m-RNA was investigated by RT-PCR using liver biopsy specimens from 30 HCV patients including 7 patients complicated with HCC. Liver biopsies were also subjected to formalin fixation for complete histopathological examination. Ninety seven percent of patients expressed Interferon Alpha m-RNA while 30% only expressed Interferon Alpha Receptor m-RNA. Responders and non-responders to Interferon therapy were divided according to their HCV RNA after six-months follow up period of interferon therapy. Responders showed significantly lower mean age, better histopathological states and higher incidence of expression of IFN Alpha Receptor mRNA. Regardless of the response to interferon, histological activity index scores and the degree of fibrosis showed a significant inverse correlation to the presence of IFNα-R m-RNA. IFNα-R mRNA expression decreases with the histological progress of the disease, suggesting that lower expression of the IFNα-Rc may be partially responsible for the unfavorable response to interferon in these patients. African Journal of Health Sciences Vol. 14 (1-2) 2007: pp. 86-9

Wideband High Gain Printed Quasi-Yagi Diffraction Gratings-Based Antenna for 5G Applications

A broadband high-gain printed Quasi-Yagi antenna with a perturbation-based planar dielectric lens is presented. The perturbation design parameters are based on the diffraction gratings theory for gain enhancement, radiation pattern improvement, and higher order modes suppression. The proposed antenna provides 94.5% aperture efficiency with a high gain of 15 dBi at 30 GHz, high radiation efficiency of ~90%, and (24–40) GHz ultra-wide matching (S11 < −10 dB) bandwidth. The measured cross-polarization is lower than −20 dB in both E - and H - planes. With these features in addition to being low-profile and lightweight, this antenna is suitable for various millimeter-wave applications

Optimum Wideband High Gain Analog Beamforming Network for 5G Applications

A broadband high-gain millimeter-wave (mmWave) array beamforming network (BFN) design, analysis, and implementation based on the Rotman lens antenna array feeding are presented. The BFN is intended for operation in the (26-40) GHz frequency band for a wide range of potential applications in the fifth generation (5G). The system is made on Rogers substrate, RO6010, to provide compatibility with standard planar low-cost processing techniques for millimeter-wave monolithic integrated circuit (MMIC). The measured results show the system capability of 80° beam scanning for different angles at -39.7°, -26.5°, -13.3°, 0°, +13.3°, +26.5°, and +39.5° at 28 GHz. With these features in addition to being compact size, low profile, and lightweight, this BFN is suitable for various millimeter-wave and 5G applications such as the advanced multi-in multi-out (MIMO) systems, remote sensing, and automotive radar

Prevalence of prediabetes, diabetes, and Its associated risk factors among males in Saudi Arabia: A population-based survey

Objectives: The study aims at determining the prevalence of prediabetes and diabetes and at ascertaining some concomitant risk factorsamong males in Saudi Arabia.Methods: A population-based cross-sectional study including 381 Saudi adult males from different institutions was recruited. Odds ratios for diabetes risk and risk factors were calculated using log-binomial and multinomial logistic regression, using STATA version 12.Results: The participants included 381 diabetic males with a median age of 45 years, average body mass index of 25 ± 40 kg/m2, whereas waist circumferences ranged from 66 to 180 cm in the male study population. In addition, 27.82% had normal BMI, 32.28% were overweight, and 36.22% were obese. Around 36% had higher waist circumference, that is, \u3e102 cm. Age, BMI, marital status, and educational attainment were statistically significant predictors for prediabetes and diabetes.Conclusion: This study found that the prevalence of diabetes and prediabetes was 9.2% and 27.6%, respectively, for male Al-Kharj study population. The factors that increase the risk of diabetes and prediabetes include older age, obesity and overweight, being married, smoker, and having a civilian job and less education. All these factors were found statistically significant except smoking status and job type. In order to evaluate the causal relationship of these factors, prospective studies are required in future

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

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 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

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)

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Numerical methods for the design and description of in vitro expansion processes of human mesenchymal stem cells

Human mesenchymal stem cells (hMSCs) are a valuable source of cells for clinical applications (e.g., treatment of acute myocardial infarction or inflammatory diseases), especially in the field of regenerative medicine. However, for autologous (patient-specific) and allogeneic (off-the-shelf) hMSC-based therapies, in vitro expansion is necessary prior to the clinical application in order to achieve the required cell numbers. Safe, reproducible, and economic in vitro expansion of hMSCs for autologous and allogeneic therapies can be problematic because the cell material is restricted and the cells are sensitive to environmental changes. It is beneficial to collect detailed information on the hydrodynamic conditions and cell growth behavior in a bioreactor system, in order to develop a so called “Digital Twin” of the cultivation system and expansion process. Numerical methods, such as Computational Fluid Dynamics (CFD) which has become widely used in the biotech industry for studying local characteristics within bioreactors or kinetic growth modelling, provide possible solutions for such tasks. In this review, we will present the current state-of-the-art for the in vitro expansion of hMSCs. Different numerical tools, including numerical fluid flow simulations and cell growth modelling approaches for hMSCs, will be presented. In addition, a case study demonstrating the applicability of CFD and kinetic growth modelling for the development of an microcarrier-based hMSC process will be shown