Enhanced Gravitational Search Optimization with Hybrid Deep Learning Model for COVID-19 Diagnosis on Epidemiology Data.

Effective screening provides efficient and quick diagnoses of COVID-19 and could alleviate related problems in the health care system. A prediction model that combines multiple features to assess contamination risks was established in the hope of supporting healthcare workers worldwide in triaging patients, particularly in situations with limited health care resources. Furthermore, a lack of diagnosis kits and asymptomatic cases can lead to missed or delayed diagnoses, exposing visitors, medical staff, and patients to 2019-nCoV contamination. Non-clinical techniques including data mining, expert systems, machine learning, and other artificial intelligence technologies have a crucial role to play in containment and diagnosis in the COVID-19 outbreak. This study developed Enhanced Gravitational Search Optimization with a Hybrid Deep Learning Model (EGSO-HDLM) for COVID-19 diagnoses using epidemiology data. The major aim of designing the EGSO-HDLM model was the identification and classification of COVID-19 using epidemiology data. In order to examine the epidemiology data, the EGSO-HDLM model employed a hybrid convolutional neural network with a gated recurrent unit based fusion (HCNN-GRUF) model. In addition, the hyperparameter optimization of the HCNN-GRUF model was improved by the use of the EGSO algorithm, which was derived by including the concepts of cat map and the traditional GSO algorithm. The design of the EGSO algorithm helps in reducing the ergodic problem, avoiding premature convergence, and enhancing algorithm efficiency. To demonstrate the better performance of the EGSO-HDLM model, experimental validation on a benchmark dataset was performed. The simulation results ensured the enhanced performance of the EGSO-HDLM model over recent approaches.

Fabrication and characterization of field effect transistor based on single walled carbon nanotubes

Objectives Sensor Biology and sensor devices have been advancing since its inceptions. In this work, we report fabrication of carbon nanotubes filed-effect transistor (CNT-FET) sensor and its characterization. CNT intensively has been used in the construction of sensing layers due to their exceptional features, large surface area, stability, high mechanical strength, adaptability, and functional behavior. Methods Carbon nanotubes (CNTs) as semiconductor were fabricated as an active nanomaterial between the source-drain electrodes. The fabrication of CNT-FETs performed by following conventional photolithography method and lift-off techniques. Results The structural morphology of deposited CNT was confirmed by the scanning electron micrograph (SEM) imaging. The transfer curves between drain-source were considered as a function of the drain-source voltage (VDS) and gate-source voltage (VGS) from individual CNT-FET fabricated wafer. The characterized Ion/Ioff ratio was calculated for every CNT-FET device. The semiconductor properties of the fabricated CNT-FET device characterized by the source-drain current (IDS) versus gate voltage (VGS). Conclusions CNT-FET based device have advantages of low cost fabrication, quick response, increased sensitivity, small size, and high flexibility. CNT-FETs have been used comprehensively in the biosensing of chemicals, proteins, nucleic acids, bacteria, and virus etc. This device could be used for SARS-CoV-2 and related variant detection in current scenario.

Structure-Activity Studies Reveal Scope for Optimisation of Ebselen-Type Inhibition of SARS-CoV-2 Main Protease.

The reactive organoselenium compound ebselen is being investigated for treatment of coronavirus disease 2019 (COVID-19) and other diseases. We report structure-activity studies on sulfur analogues of ebselen with the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro ), employing turnover and protein-observed mass spectrometry-based assays. The results reveal scope for optimisation of ebselen/ebselen derivative- mediated inhibition of Mpro , particularly with respect to improved selectivity.

Carbon nanotube field-effect transistor (CNT-FET)-based biosensor for rapid detection of SARS-CoV-2 (COVID-19) surface spike protein S1.

The large-scale diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is important for traceability and treatment during pandemic outbreaks. We developed a fast (2-3 min), easy-to-use, low-cost, and quantitative electrochemical biosensor based on carbon nanotube field-effect transistor (CNT-FET) that allows digital detection of the SARS-CoV-2 S1 in fortifited saliva samples for quick and accurate detection of SARS-CoV-2 S1 antigens. The biosensor was developed on a Si/SiO2 surface by CNT printing with the immobilization of a anti-SARS-CoV-2 S1. SARS-CoV-2 S1 antibody was immobilized on the CNT surface between the S-D channel area using a linker 1-pyrenebutanoic acid succinimidyl ester (PBASE) through non-covalent interaction. A commercial SARS-CoV-2 S1 antigen was used to characterize the electrical output of the CNT-FET biosensor. The SARS-CoV-2 S1 antigen in the 10 mM AA buffer pH 6.0 was effectively detected by the CNT-FET biosensor at concentrations from 0.1 fg/mL to 5.0 pg/mL. The limit of detection (LOD) of the developed CNT-FET biosensor was 4.12 fg/mL. The selectivity test was performed by using target SARS-CoV-2 S1 and non-target SARS-CoV-1 S1 and MERS-CoV S1 antigens in the 10 mM AA buffer pH 6.0. The biosensor showed high selectivity (no response to SARS-CoV-1 S1 or MERS-CoV S1 antigen) with SARS-CoV-2 S1 antigen detection in the 10 mM AA buffer pH 6.0. The biosensor is highly sensitive, saves time, and could be a helpful platform for rapid detection of SARS-CoV-2 S1 antigen from the patients saliva.

Origin, Potential Therapeutic Targets and Treatment for Coronavirus Disease (COVID-19).

The ongoing episode of coronavirus disease 19 (COVID-19) has imposed a serious threat to global health and the world economy. The disease has rapidly acquired a pandemic status affecting almost all populated areas of the planet. The causative agent of COVID-19 is a novel coronavirus known as SARS-CoV-2. The virus has an approximate 30 kb single-stranded positive-sense RNA genome, which is 74.5% to 99% identical to that of SARS-CoV, CoV-pangolin, and the coronavirus the from horseshoe bat. According to available information, SARS-CoV-2 is inferred to be a recombinant virus that originated from bats and was transmitted to humans, possibly using the pangolin as the intermediate host. The interaction of the SARS-CoV-2 spike protein with the human ACE2 (angiotensin-converting enzyme 2) receptor, and its subsequent cleavage by serine protease and fusion, are the main events in the pathophysiology. The serine protease inhibitors, spike protein-based vaccines, or ACE2 blockers may have therapeutic potential in the near future. At present, no vaccine is available against COVID-19. The disease is being treated with antiviral, antimalarial, anti-inflammatory, herbal medicines, and active plasma antibodies. In this context, the present review article provides a cumulative account of the recent information regarding the viral characteristics, potential therapeutic targets, treatment options, and prospective research questions.

Implications of COVID-19 on the Labor Market of Saudi Arabia: The Role of Universities for a Sustainable Workforce

Governments all over the world are taking preventive measures to contain the spread of COVID-19. However, these measures have caused both long- and short-term effects on the socioeconomic situation of many countries. Due to lockdowns and business shutdowns, people are becoming unemployed or are working on reduced wages, creating a unique type of career shock in the global job market. Moreover, this phenomenon also produces a negative reflux among workers, encouraging a new skill set for this unprecedented time. The present study aimed to investigate the implications of COVID-19 on the labor market of Saudi Arabia. Data were collected with the help of a questionnaire from both public and private sector employers (n = 234) to inquire about their perceptions of the new skill set required in the changing business environment during and after pandemics. The data were analyzed with the help of descriptive statistics as well as simple and companion regression. The results indicate that the healthcare, service and education sectors have quickly transformed themselves from conventional to remote forms of working and consider virtual skills, autonomous working and effective communication the most important skills for their workforce during the current and the postpandemic scenarios. Interviews were then conducted with educational leaders to develop a conceptual framework by integrating both qualitative and quantitative analysis of the surveys. The results of the study are beneficial for the educational leadership of higher education institutions (HEIs) to better align their educational programs with changing market needs. By doing so, they not only increase the sustainability of the workforce but also minimize the impact of COVID-19 on the Saudi labor market.