Spike Protein Mutations and the Effects on SARS-CoV-2 Pathogenesis

The severe acute respiratory syndrome coronavirus 2 spike (S) glycoprotein facilitates receptor binding to initiate cell entry that is the critical initial step in the infection cycle. Due to S glycoprotein's pivotal role, in this review, we pointed to show potential functional and structural consequences of S glycoprotein and its variants, which has been related with increased viral load in humans with SARS-CoV-2 infection

Clinical Performance of RT-PCR and Chest CT Scan for Covid-19 Diagnosis; A Systematic Review

Context: Due to their availability and rapid turnaround time, the supplemental role of chest computed tomography (CT) scan and real-time polymerase chain reaction (RT-PCR) is growing for early diagnosis of patients with COVID-19. However, due to the low efficiency of viral nucleic acid detection as well as low specificity of chest CT scan for detecting COVID-19 pneumonia, both methods show incomplete clinical performance for proper COVID-19 disease diagnosis. The purpose of this review was to compare the clinical performance of two methods and to evaluate the diagnostic values of chest CT scan and RT-PCR for suspected COVID-19 patients. Evidence acquisition: We systemically searched PubMed, Cochrane, from December 2019 to the end of April 2020. Clinical research papers in goal fields that reviewed COVID-19 patients, whom chest CT scan, and PCR testing were performed together were included. Results: In total, we found 536 studies; and finally168 studies were shortlisted. Following title and abstract screening, we reached 83 studies based on the inclusion and exclusion criteria. Conducted screen by the full text covered 28 studies, which led to data extraction. By the full-text assessment of 28 included studies, we found 4486 assessed patients. Totally, 3164 patients had positive chest CT scans, and 3014 patients had positive PCR results. The finding showed that recent studies on the diagnostic performance of RT-PCR and chest CT scan have commonly been reported from China. Conclusion: The results from this review indicate that the chest CT scan should be used for symptomatic and hospitalized patients. Moreover, chest CT scan should not be used as a primary screening tool for diagnosing COVID-19. Application of RT-PCR as the first line diagnosis is still recommended

A Snapshot of Different Types of Under Research Vaccines Against COVID-19: A Review

SARS-CoV-2 as an emerging coronavirus, which first emerged in late 2019 in China causes a respiratory disease called “Coronavirus Disease 2019 (COVID-19)’’. SARS-CoV-2 has since infected more than 26 million people worldwide and caused more than 864000 deaths as of September 04, 2020. The SARS-CoV-2 spike (S) protein consists of two subunits: S1 and S2, which plays a role in binding to cellular receptors and mediating the fusion process between the membranes of the virus and host cells. The S protein has an important role to induce neutralizing-antibody, as well as protective immunity, during SARS-CoV-2 infection. In this review, we focused on different types of the vaccine against COVID-19. *Corresponding Author: Maryam Fazeli; Email: [email protected] Please cite this article as: Zandi M, Rashid S, Nasimzade S, Pourhossein B, Fazeli M. A Snapshot of Different Types of Under Research Vaccines Against COVID-19: A Review. Arch Med Lab Sci. 2020;6:1-7 (e7). https://doi.org/10.22037/amls.v6.3237

Robot Motion Prediction by Channel State Information

Autonomous robotic systems have gained a lot of attention, in recent years. However, accurate prediction of robot motion in indoor environments with limited visibility is challenging. While vision-based and light detection and ranging (LiDAR) sensors are commonly used for motion detection and localization of robotic arms, they are privacy-invasive and depend on a clear line-of-sight (LOS) for precise measurements. In cases where additional sensors are not available or LOS is not possible, these technologies may not be the best option. This paper proposes a novel method that employs channel state information (CSI) from WiFi signals affected by robotic arm motion. We developed a convolutional neural network (CNN) model to classify four different activities of a Franka Emika robotic arm. The implemented method seeks to accurately predict robot motion even in scenarios in which the robot is obscured by obstacles, without relying on any attached or internal sensors.Comment: 6 pages, 10 figures, 2 tables, MLSP Conferenc

Robustness Evaluation of Machine Learning Models for Robot Arm Action Recognition in Noisy Environments

In the realm of robot action recognition, identifying distinct but spatially proximate arm movements using vision systems in noisy environments poses a significant challenge. This paper studies robot arm action recognition in noisy environments using machine learning techniques. Specifically, a vision system is used to track the robot's movements followed by a deep learning model to extract the arm's key points. Through a comparative analysis of machine learning methods, the effectiveness and robustness of this model are assessed in noisy environments. A case study was conducted using the Tic-Tac-Toe game in a 3-by-3 grid environment, where the focus is to accurately identify the actions of the arms in selecting specific locations within this constrained environment. Experimental results show that our approach can achieve precise key point detection and action classification despite the addition of noise and uncertainties to the dataset.Comment: Accepted at ICASS

RoboFiSense: Attention-Based Robotic Arm Activity Recognition with WiFi Sensing

Despite the current surge of interest in autonomous robotic systems, robot activity recognition within restricted indoor environments remains a formidable challenge. Conventional methods for detecting and recognizing robotic arms' activities often rely on vision-based or light detection and ranging (LiDAR) sensors, which require line-of-sight (LoS) access and may raise privacy concerns, for example, in nursing facilities. This research pioneers an innovative approach harnessing channel state information (CSI) measured from WiFi signals, subtly influenced by the activity of robotic arms. We developed an attention-based network to classify eight distinct activities performed by a Franka Emika robotic arm in different situations. Our proposed bidirectional vision transformer-concatenated (BiVTC) methodology aspires to predict robotic arm activities accurately, even when trained on activities with different velocities, all without dependency on external or internal sensors or visual aids. Considering the high dependency of CSI data on the environment motivated us to study the problem of sniffer location selection, by systematically changing the sniffer's location and collecting different sets of data. Finally, this paper also marks the first publication of the CSI data of eight distinct robotic arm activities, collectively referred to as RoboFiSense. This initiative aims to provide a benchmark dataset and baselines to the research community, fostering advancements in the field of robotics sensing.Comment: 11 pages, 11 figure

A hybrid of Delphi, AHP and TOPSIS Methods for project portfolio management

Due to the importance and complexity of the portfolio management issue, over 100 different techniques have already been presented. In general, the final result of these tools is to create a prioritized list of the projects that must be selected for allocating resources. The use of financial strategies may be misleading in some cases, and it is necessary to combine these methods with other methods such as strategic approaches in order to guarantee a balanced portfolio toward the organizational strategies. On the other, categorizing projects into different baskets allows the organizations to select, evaluate and prioritize the projects in a subset using a set of similar criteria and techniques. In this article, by choosing agriculture sector as a case study, an attempt has been made to study the evaluation, ranking and management of projects with investment classifying strategy of the projects using Delphi, TOPSIS and AHP methods. The results reveal that in similar cases we can use the presented model by determining the type of activity and investment and localization of the indexes

Preparation of Cerium Oxide Nanoparticles and Their Cytotoxicity Evaluation In vitro and In vivo

Background: Nanotechnology plays a significant role in medicine, especially in diagnosis and treatment as a carrier to drugs and vaccinology. Several studies were conducted using nanoparticles as an adjuvant. The main aim of this study was in vivo and in vitro toxicity evaluation of synthesized Cerium Nanoparticles (CeNPs).Methods: In the present study, cerium nanoparticles were prepared using the wet chemical method. The formation of cerium nanoparticles was confirmed by scanning electron microscopy, transmission electron microscopes, x-ray diffraction analysis, dynamic light scattering. In vivo and in vitro toxicity of synthesized nanoparticles was evaluated in three different amounts of cerium nanoparticles (30 µg, 50 µg, & 100 µg) in mice and human fibroblast cell lines, respectively.Results: Cerium nanoparticles were successfully synthesized, and the identity was confirmed by x-ray diffraction analysis. The shape and size of nanoparticles were spherical and <100 nm, respectively. The prepared nanoparticles had a charge of -26.6 mV and a hydrodynamic radius of 446 nm. MTT assay indicated that none of the concentration of cerium was toxic, and in vivo toxicity also clarified the safety of cerium nanoparticles in mice; no significant un-normal behavioral and physical symptoms were observed in mice after CeNP administrationConclusion: Cerium nanoparticles have special properties, especially low toxicity, unique capabilities in stimulating the immune system. Cerium nanoparticles can be considered an effective and safe candidate in vaccines