Determining the SARS-CoV-2 serological immunoassay test performance indices based on the test results frequency distribution

Coronavirus disease 2019 (COVID-19) is known to induce robust antibody response in most of the affected individuals. The objective of the study was to determine if we can harvest the test sensitivity and specificity of a commercial serologic immunoassay merely based on the frequency distribution of the SARS-CoV-2 immunoglobulin (Ig) G concentrations measured in a population-based seroprevalence study. The current study was conducted on a subset of a previously published dataset from the canton of Geneva. Data were taken from two non-consecutive weeks (774 samples from May 4-9, and 658 from June 1-6, 2020). Assuming that the frequency distribution of the measured SARS-CoV-2 IgG is binormal (an educated guess), using a non-linear regression, we decomposed the distribution into its two Gaussian components. Based on the obtained regression coefficients, we calculated the prevalence of SARS-CoV-2 infection, the sensitivity and specificity, and the most appropriate cut-off value for the test. The obtained results were compared with those obtained from a validity study and a seroprevalence population-based study. The model could predict more than 90% of the variance observed in the SARS-CoV-2 IgG distribution. The results derived from our model were in good agreement with the results obtained from the seroprevalence and validity studies. Altogether 138 of 1432 people had SARS-CoV-2 IgG ≥ 0.90, the cut-off value which maximized the Youden’s index. This translates into a true prevalence of 7.0% (95% confidence interval 5.4% to 8.6%), which is in keeping with the estimated prevalence of 7.7% derived from our model. Our model can provide the true prevalence. Having an educated guess about the distribution of test results, the test performance indices can be derived with acceptable accuracy merely based on the test results frequency distribution without the need for conducting a validity study and comparing the test results against a gold-standard test

The molecular basis of the neutralization breadth of the RBD-specific antibody CoV11

SARS-CoV-2, the virus behind the COVID-19 pandemic, has changed over time to the extent that the current virus is substantially different from what originally led to the pandemic in 2019–2020. Viral variants have modified the severity and transmissibility of the disease and continue do so. How much of this change is due to viral fitness versus a response to immune pressure is hard to define. One class of antibodies that continues to afford some level of protection from emerging variants are those that closely overlap the binding site for angiotensin-converting enzyme 2 (ACE2) on the receptor binding domain (RBD). Some members of this class that were identified early in the course of the pandemic arose from the VH 3-53 germline gene (IGHV3-53*01) and had short heavy chain complementarity-determining region 3s (CDR H3s). Here, we describe the molecular basis of the SARS-CoV-2 RBD recognition by the anti-RBD monoclonal antibody CoV11 isolated early in the COVID-19 pandemic and show how its unique mode of binding the RBD determines its neutralization breadth. CoV11 utilizes a heavy chain VH 3-53 and a light chain VK 3-20 germline sequence to bind to the RBD. Two of CoV11’s four heavy chain changes from the VH 3-53 germline sequence, ThrFWR H128 to Ile and SerCDR H131 to Arg, and some unique features in its CDR H3 increase its affinity to the RBD, while the four light chain changes from the VK 3-20 germline sequence sit outside of the RBD binding site. Antibodies of this type can retain significant affinity and neutralization potency against variants of concern (VOCs) that have diverged significantly from original virus lineage such as the prevalent omicron variant. We also discuss the mechanism by which VH 3-53 encoded antibodies recognize spike antigen and show how minimal changes to their sequence, their choice of light chain, and their mode of binding influence their affinity and impact their neutralization breadth

Simulation of nanofluid flow in a micro-heat sink with corrugated walls considering the Effect of Nanoparticle Diameter on Heat Sink Efficiency

In this numerical work, the cooling performance of water–Al2O3 nanofluid (NF) in a novel microchannel heat sink with wavy walls (WMH-S) is investigated. The focus of this article is on the effect of NP diameter on the cooling efficiency of the heat sink. The heat sink has four inlets and four outlets, and it receives a constant heat flux from the bottom. CATIA and CAMSOL software were used to design the model and simulate the NF flow and heat transfer, respectively. The effects of the Reynolds number (Re) and volume percentage of nanoparticles (Fi) on the outcomes are investigated. One of the most significant results of this work was the reduction in the maximum and average temperatures of the H-S by increasing both the Re and Fi. In addition, the lowest Tmax and pumping power belong to the state of low NP diameter and higher Fi. The addition of nanoparticles reduces the heat sink maximum temperature by 3.8 and 2.5% at the Reynolds numbers of 300 and 1800, respectively. Furthermore, the highest figure of merit (FOM) was approximately 1.25, which occurred at Re 1800 and Fi 5%. Eventually, it was revealed that the best performance of the WMH-S was observed in the case of Re 807.87, volume percentage of 0.0437%, and NP diameter of 20 nm.Taif University, Taif, Saudi Arabiahttp://www.frontiersin.org/Energy_Researcham2022Mechanical and Aeronautical Engineerin

Correlations for total entropy generation and Bejan number for free convective heat transfer of an eco-friendly nanofluid in a rectangular enclosure under uniform magnetic field

In this paper, focusing on the study of entropy generation (EGN), the convection flow of an eco-friendly nanofluid (N-F) in a rectangular enclosure is studied numerically. The nanoparticles (N-Ps) used are silver N-P, which are obtained in an eco-friendly manner from natural materials. By suspending these N-Ps in an equal mixture of water and ethylene glycol (E-G), the N-F has been prepared. There are two constant-temperature triangular obstacles with height w and base H that are placed on the hot wall. There is a magnetic field (M-F) in the x-direction. To simulate the N-F flow, eco-friendly N-P relations are used, and the equations are solved using the volume control method and the SIMPLE algorithm. The variables include Rayleigh number (Ra), Hartmann number (Ha), H, W, and the volume fraction of silver N-Ps. The effect of these parameters is evaluated on the EGN and Bejan number (Be). Finally, a correlation is expressed for the EGN for a range of variables. The most important results of this paper demonstrate that the addition of silver eco-friendly N-Ps intensifies the EGN so that the addition of 3% of N-Ps enhances the EGN by 3.8%. An increment in the obstacle length reduces the Be barrier while increasing the Ha, which enhances the Be when the convection is strong. Increasing the height of the obstacle intensifies entropy generation.Taif University, Taif, Saudi Arabia.https://www.mdpi.com/journal/processesam2022Mechanical and Aeronautical Engineerin

Simulation of alumina/water manofluid flow in a micro-heatsink with wavy microchannels : impact of two-phase and single-phase nanofluid models

In this article, alumina/water nanofluid (NF) flow in a heatsink (H-S) with wavy microchannels (W-MCs) is simulated. The H-S is made of aluminum containing four similar parts. Each part has an inlet and outlet. Constant heat flux is applied on the bottom of the H-S. The study is based on two-phase (T-P) mixture and single-phase (S-P) models to determine the difference between these two types of simulations. FLUENT software and the control volume method were used for simulations. The volume control method is employed to solve equations. The effective variables include the volume fraction 0 < φ < 5% of alumina and Reynolds number (Re) 300 < Re < 1800. The maximum H-S bottom temperature, the required amount of pumping power (PP), the temperature uniformity, and the heat resistance of the H-S are the outputs studied to simulate the S-P and T-P models. The results show that the use of the T-P model has less error in comparison with the experimental data than the S-P model. An increment in the Re and φ reduces the maximum temperature (M-T) of the H-S. The S-P model, especially at a higher value of φ, leads to a lower M-T value than the T-P model. The S-P model shows a 0.5% greater decrease than the T-P model at the Reynolds number of 300 by enhancing the volume percentage of nanoparticles (NPs) from 1 to 5%. Temperature uniformity is improved with Re and φ. The reduction of H-S thermal resistance with Re and φ is the result of this study. Adding NPs to water, especially at higher amounts of φ, enhances the required PP. The T-P model predicts higher PP than the S-P one, especially at a high value of φ. The T-P model shows 4% more PP than the S-P model at Re 30 and a volume fraction of 4%.The German Research Foundation (DFG) and Taif University, Taif, Saudi Arabia.http://www.frontiersin.org/Energy_Researcham2022Mechanical and Aeronautical Engineerin