Increased Levels of Autoantibodies against ROS-Modified Proteins in Depressed Individuals with Decrease in Antibodies against SARS-CoV-2 Antigen (S1-RBD).

Coronavirus 2019 (COVID-19) disease management is highly dependent on the immune status of the infected individual. An increase in the incidence of depression has been observed during the ongoing COVID-19 pandemic. Autoantibodies against in vitro reactive oxygen species (ROS) modified BSA and Lys as well as antibodies against receptor binding domain subunit S1 (S1-RBD) (S1-RBD-Abs) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were estimated using direct binding and competition ELISA. Serum samples were also tested for fasting blood glucose (FBG), malondialdehyde (MDA), carbonyl content (CC), interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). Significant structural changes were observed in ROS modified BSA and Lys. Female depressed subjects who were also smokers (F-D-S) showed the highest levels of oxidative stress (MDA and CC levels). Similarly, increased levels of autoantibodies against ROS modified proteins were detected in F-D-S subjects, in males who were depressed and in smokers (M-D-S) compared to the other subjects from the rest of the groups. However, contrary to this observation, levels of S1-RBD-Abs were found to be lowest in the F-D-S and M-D-S groups. During the pandemic, large numbers of individuals have experienced depression, which may induce excessive oxidative stress, causing modifications in circulatory proteins. Thus, the formation of neo-antigens is induced, which lead to the generation of autoantibodies. The concomitant effect of increased autoantibodies with elevated levels of IFN-γ and TNF-α possibly tilt the immune balance toward autoantibody generation rather than the formation of S1-RBD-Abs. Thus, it is important to identify individuals who are at risk of depression to determine immune status and facilitate the better management of COVID-19.

Seroprevalence of Anti-S1-RBD Antibodies in Pre-pandemic and Pandemic Subjects From Hail Region, KSA.

Background: Two years into the pandemic, yet the threat of new SARS-CoV-2 variants continues to loom large. Sustained efforts are required to fully understand the infection in asymptomatic individuals and those with complications. Identification, containment, care, and preventative strategies rely on understanding the varied humoral immune responses. Methods: An in-house ELISA was developed and standardized to screen for serum IgG antibodies against the SARS-CoV-2 S1-RBD protein as an antigen. This study aims to investigate the seroprevalence of serum antibodies against S1-RBD antigen in pre-pandemic (n = 120) and during the early pandemic period (n = 120) in subjects from the Hail region, KSA and to correlate it with clinical and demographic factors. Results: Samples collected from both male (n = 60) and female (n = 60) subjects during the pandemic in the age groups of 20-40 (0.31 ± 0.029 and 0.29 ± 0.024, respectively) and 41-60 years (0.35 ± 0.026 and 0.30 ± 0.025, respectively) showed significantly higher levels of serum antibodies against S-RBD antigen than the age-matched pre-pandemic samples [male (n = 60) and female (n = 60)]. Pandemic subjects exhibited significantly (p < 0.01) higher inhibition (80-88%) than age-matched pre-pandemic subjects (32-39%). Antibodies against S1-RBD antigen were detected in approximately 10% of the total pre-pandemic population (males and females). However, subjects > 60 years did not show antibodies. Conclusion: Antibody levels increased in samples collected during the pandemic, even though these subjects were not clinically COVID-19 positive. A small number of pre-pandemic subjects showed serum antibodies, suggesting prior exposure to other coronaviruses in the region. With dwindling neutralizing antibody levels and reduced vaccine efficacy against newer variants, it remains crucial to develop better assays for surveillance, management, and future research.

Challenges of home learning during movement control order among UiTM Pahang students

Due to the COVID-19 pandemic, the enforcement of the Movement Control Order (MCO) by theMalaysian government since March 2020 significantly impacted many sectors such as the economy, society, and others. MCO enforcement has made Malaysians spend most of their time staying at home, and even some have lost their income source. Another sector that has been greatly affected is the educational sector. Today’s landscape of education has changed dramatically with the phenomenal rise of virtual classes from home. Learning and teaching processes are undertaken remotely and on digital platforms to curb the spreading of the virus. This situation has affected the lesson and learning process from home to many of the several students in Malaysia. Therefore, this study investigates the challenges of home learning during MCO among students in the Universiti Teknologi MARA (UiTM) Pahang Branch. A simple random sampling technique was used to distribute the online survey questionnaires, involving a sample of 213 students. Besides, a descriptive statistic was used to study the students’ demographic characteristics according to the challenges. In contrast, logistic regression analysis was used to determine the factors associated with home learning challenges during MCO. Based on the findings, most male and female students were not well prepared for home learning during MCO, with a percentage of 71.60% and 69.70%, respectively. As a result, 79.81% agreed that home learning is more stressful than the physical classes on the campus. In comparison, 79.63% of Social Science and 83.02% of Science and Technology students claimed that the workload given is way more significant during online classes. Furthermore, this study concludes that the most associated challenges of home learning faced by the students during MCO are the abundance of workload and loss of interest in the subject.

Classification of Digital Modulated COVID-19 Images in the Presence of Channel Noise Using 2D Convolutional Neural Networks

The wireless environment poses a significant challenge to the propagation of signals. Different effects such as multipath scattering, noise, degradation, distortion, attenuation, and fading affect the distribution of signals adversely. Deep learning techniques can be used to differentiate among different modulated signals for reliable detection in a communication system. This study aims at distinguishing COVID-19 disease images that have been modulated by different digital modulation schemes and are then passed through different noise channels and classified using deep learning models. We proposed a comprehensive evaluation of different 2D Convolutional Neural Network (CNN) architectures for the task of multiclass (24-classes) classification of modulated images in the presence of noise and fading. It is used to differentiate between images modulated through Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16- and 64-Quadrature Amplitude Modulation and passed through Additive White Gaussian Noise, Rayleigh, and Rician channels. We obtained mixed results under different settings such as data augmentation, disharmony between batch normalization (BN), and dropout (DO), as well as lack of BN in the network. In this study, we found that the best performing model is a 2D-CNN model using disharmony between BN and DO techniques trained using 10-fold cross-validation (CV) with a small value of DO before softmax and after every convolution and fully connected layer along with BN layers in the presence of data augmentation, while the least performing model is the 2D-CNN model trained using 5-fold CV without augmentation.