Effects of COVID-19 on Synaptic and Neuronal Degeneration.

Neurons are the basic building blocks of the human body's neurological system. Atrophy is defined by the disintegration of the connections between cells that enable them to communicate. Peripheral neuropathy and demyelinating disorders, as well as cerebrovascular illnesses and central nervous system (CNS) inflammatory diseases, have all been linked to brain damage, including Parkinson's disease (PD). It turns out that these diseases have a direct impact on brain atrophy. However, it may take some time after the onset of one of these diseases for this atrophy to be clearly diagnosed. With the emergence of the Coronavirus disease 2019 (COVID-19) pandemic, there were several clinical observations of COVID-19 patients. Among those observations is that the virus can cause any of the diseases that can lead to brain atrophy. Here we shed light on the research that tracked the relationship of these diseases to the COVID-19 virus. The importance of this review is that it is the first to link the relationship between the Coronavirus and diseases that cause brain atrophy. It also indicates the indirect role of the virus in dystrophy.

A Novel Multicolor-thresholding Auto-detection Method to Detect the Location and Severity of Inflammation in Confirmed SARS-COV-2 Cases using Chest X-Ray Images.

OBJECTIVES: Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread around the world. It has been determined that the disease is very contagious and can cause Acute Respiratory Distress (ARD). Medical imaging has the potential to help identify, detect, and quantify the severity of this infection. This work seeks to develop a novel auto-detection technique for verified COVID-19 cases that can detect aberrant alterations in traditional X-ray pictures. METHODS: Nineteen separately colored layers were created from X-ray scans of patients diagnosed with COVID-19. Each layer represents objects that have a similar contrast and can be represented by a single color. In a single layer, objects with similar contrasts are formed. A single color image was created by extracting all the objects from all the layers. The prototype model could recognize a wide range of abnormal changes in the image texture based on color differentiation. This was true even when the contrast values of the detected unclear abnormalities varied slightly. RESULTS: The results indicate that the proposed novel method is 91% accurate in detecting and grading COVID-19 lung infections compared to the opinions of three experienced radiologists evaluating chest X-ray images. Additionally, the method can be used to determine the infection site and severity of the disease by categorizing X-rays into five severity levels. CONCLUSION: By comparing affected tissue to healthy tissue, the proposed COVID-19 auto-detection method can identify locations and indicate the severity of the disease, as well as predict where the disease may spread.

Forecasting the Post-Pandemic Effects of the SARS-CoV-2 Virus Using the Bullwhip Phenomenon Alongside Use of Nanosensors for Disease Containment and Cure.

The COVID-19 pandemic has the tendency to affect various organizational paradigm alterations, which civilization hasyet to fully comprehend. Personal to professional, individual to corporate, and across most industries, the spectrum of transformations is vast. Economically, the globe has never been more intertwined, and it has never been subjected to such widespread disruption. While many people have felt and acknowledged the pandemic's short-term repercussions, the resultant paradigm alterations will certainly have long-term consequences with an unknown range and severity. This review paper aims at acknowledging various approaches for the prevention, detection, and diagnosis of the SARS-CoV-2 virus using nanomaterials as a base material. A nanostructure is a material classification based on dimensionality, in proportion to the characteristic diameter and surface area. Nanoparticles, quantum dots, nanowires (NW), carbon nanotubes (CNT), thin films, and nanocomposites are some examples of various dimensions, each acting as a single unit, in terms of transport capacities. Top-down and bottom-up techniques are used to fabricate nanomaterials. The large surface-to-volume ratio of nanomaterials allows one to create extremely sensitive charge or field sensors (electrical sensors, chemical sensors, explosives detection, optical sensors, and gas sensing applications). Nanowires have potential applications in information and communication technologies, low-energy lightning, and medical sensors. Carbon nanotubes have the best environmental stability, electrical characteristics, and surface-to-volume ratio of any nanomaterial, making them ideal for bio-sensing applications. Traditional commercially available techniques have focused on clinical manifestations, as well as molecular and serological detection equipment that can identify the SARS-CoV-2 virus. Scientists are expressing a lot of interest in developing a portable and easy-to-use COVID-19 detection tool. Several unique methodologies and approaches are being investigated as feasible advanced systems capable of meeting the demands. This review article attempts to emphasize the pandemic's aftereffects, utilising the notion of the bullwhip phenomenon's short-term and long-term effects, and it specifies the use of nanomaterials and nanosensors for detection, prevention, diagnosis, and therapy in connection to the SARS-CoV-2.

The Potential Impact of COVID-19 Virus on the Heart and the Circulatory System.

Heart attacks, arrhythmias, and cardiomyopathy are all linked to the 2019 coronavirus disease (COVID-19), which has been identified as a risk factor for cardiovascular disease. Nothing can be held accountable in the current state of affairs. Undiagnosed chronic systolic heart failure (CSHF) develops when the heart's second half of the cardiac cycle does not function properly. As a result, the heart's blood pumping function is interrupted. Stress-induced cardiomyopathy may be caused by a variety of factors inside the body (SICM). Cytokine storm and microvascular dysfunction are among the issues. There is inflammation in the heart muscle, which may lead to stress-induced cardiomyopathy. A major part of our study is going to be devoted to understanding the effects of coronavirus on the cardiovascular system and blood vessels. A lot of time and effort has been put into figuring out the health effects of radiation exposure. The heart and circulatory system are shown to be affected by the coronavirus in this research. COVID-19 is shown to influence persons with heart disease, heart failure, arrhythmias, microvascular angiopathy, and cardiac damage in this study.

The Potential Contribution of Biopolymeric Particles in Lung Tissue Regeneration of COVID-19 Patients.

The lung is a vital organ that houses the alveoli, which is where gas exchange takes place. The COVID-19 illness attacks lung cells directly, creating significant inflammation and resulting in their inability to function. To return to the nature of their job, it may be essential to rejuvenate the afflicted lung cells. This is difficult because lung cells need a long time to rebuild and resume their function. Biopolymeric particles are the most effective means to transfer developing treatments to airway epithelial cells and then regenerate infected lung cells, which is one of the most significant symptoms connected with COVID-19. Delivering biocompatible and degradable natural biological materials, chemotherapeutic drugs, vaccines, proteins, antibodies, nucleic acids, and diagnostic agents are all examples of these molecules' usage. Furthermore, they are created by using several structural components, which allows them to effectively connect with these cells. We highlight their most recent uses in lung tissue regeneration in this review. These particles are classified into three groups: biopolymeric nanoparticles, biopolymeric stem cell materials, and biopolymeric scaffolds. The techniques and processes for regenerating lung tissue will be thoroughly explored.

Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19.

The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world's healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak's spread, and restore full functionality to the world's healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID-19 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep- and transfer-learning techniques to develop accurate, general, and robust models for detecting COVID-19. The developed models utilize either convolutional neural networks or transfer-learning models or hybridize them with powerful machine-learning techniques to exploit their full potential. For experimentation, we applied the proposed models to two data sets: the COVID-19 Radiography Database from Kaggle and a local data set from Asir Hospital, Abha, Saudi Arabia. The proposed models achieved promising results in detecting COVID-19 cases and discriminating them from normal and other viral pneumonia with excellent accuracy. The hybrid models extracted features from the flatten layer or the first hidden layer of the neural network and then fed these features into a classification algorithm. This approach enhanced the results further to full accuracy for binary COVID-19 classification and 97.8% for multiclass classification.

Deep sequence modelling for predicting COVID-19 mRNA vaccine degradation.

The worldwide coronavirus (COVID-19) pandemic made dramatic and rapid progress in the year 2020 and requires urgent global effort to accelerate the development of a vaccine to stop the daily infections and deaths. Several types of vaccine have been designed to teach the immune system how to fight off certain kinds of pathogens. mRNA vaccines are the most important candidate vaccines because of their capacity for rapid development, high potency, safe administration and potential for low-cost manufacture. mRNA vaccine acts by training the body to recognize and response to the proteins produced by disease-causing organisms such as viruses or bacteria. This type of vaccine is the fastest candidate to treat COVID-19 but it currently facing several limitations. In particular, it is a challenge to design stable mRNA molecules because of the inefficient in vivo delivery of mRNA, its tendency for spontaneous degradation and low protein expression levels. This work designed and implemented a sequence deep model based on bidirectional GRU and LSTM models applied on the Stanford COVID-19 mRNA vaccine dataset to predict the mRNA sequences responsible for degradation by predicting five reactivity values for every position in the sequence. Four of these values determine the likelihood of degradation with/without magnesium at high pH (pH 10) and high temperature (50 degrees Celsius) and the fifth reactivity value is used to determine the likely secondary structure of the RNA sample. The model relies on two types of features, namely numerical and categorical features, where the categorical features are extracted from the mRNA sequences, structure and predicted loop. These features are represented and encoded by numbers, and then, the features are extracted using embedding layer learning. There are five numerical features depending on the likelihood for each pair of nucleotides in the RNA. The model gives promising results because it predicts the five reactivity values with a validation mean columnwise root mean square error (MCRMSE) of 0.125 using LSTM model with augmentation and the codon encoding method. Codon encoding outperforms Base encoding in MCRMSE validation error using the LSTM model meanwhile Base encoding outperforms codon encoding due to less over-fitting and the difference between the training and validation loss error is 0.008.

SARS-CoV-2: Insight in genome structure, pathogenesis and viral receptor binding analysis - An updated review.

The novel coronavirus disease (COVID-19) a global pandemic outbreak is an emerging new virus accountable for respiratory illness caused by SARS-CoV-2, originated in Wuhan city, Hubei province China, urgently calls to adopt prevention and intervention strategies. Several viral epidemics such as severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002 to 2003 and H1N1 influenza in 2009 were reported since last two decades. Moreover, the Saudi Arabia was the epicenter for Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012. The CoVs are large family with single-stranded RNA viruses (+ssRNA). Genome sequence of 2019-nCoV, shows relatively different homology from other coronavirus subtypes, categorized in betacoronavirus and possibly found from strain of bats. The COVID-19 composed of exposed densely glycosylated spike protein (S) determines virus binding and infiltrate into host cells as well as initiate protective host immune response. Recently published reviews on the emerging SARS-CoV-2 have mainly focused on its structure, development of the outbreak, relevant precautions and management trials. Currently, there is an urgency of pharmacological intervention to combat this deadly infectious disease. Elucidation of molecular mechanism of COVID-19 becomes necessary. Based on the current literature and understanding, the aim of this review is to provide current genome structure, etiology, clinical prognosis as well as to explore the viral receptor binding together functional insight of SARS-CoV-2 infection (COVID-19) with treatment and preventive measures.

A Novel Computational Model for Detecting the Severity of Inflammation in Confirmed COVID-19 Patients Using Chest X-ray Images.

Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread all over the world. The disease is highly contagious, and it may lead to acute respiratory distress (ARD). Medical imaging can play an important role in classifying, detecting, and measuring the severity of the virus. This study aims to provide a novel auto-detection tool that can detect abnormal changes in conventional X-ray images for confirmed COVID-19 cases. X-ray images from patients diagnosed with COVID-19 were converted into 19 different colored layers. Each layer represented objects with similar contrast that could be defined as a specific color. The objects with similar contrasts were formed in a single layer. All the objects from all the layers were extracted as a single-color image. Based on the differentiation of colors, the prototype model was able to recognize a wide spectrum of abnormal changes in the image texture. This was true even if there was minimal variation of the contrast values of the detected uncleared abnormalities. The results indicate that the proposed novel method can detect and determine the degree of lung infection from COVID-19 with an accuracy of 91%, compared to the opinions of three experienced radiologists. The method can also efficiently determine the sites of infection and the severity of the disease by classifying the X-rays into five levels of severity. Thus, the proposed COVID-19 autodetection method can identify locations and indicate the degree of severity of the disease by comparing affected tissue with healthy tissue, and it can predict where the disease may spread.

Aminoglycosides as potential inhibitors of SARS-CoV-2 main protease: an in silico drug repurposing study on FDA-approved antiviral and anti-infection agents.

BACKGROUND: The emergence and spread of SARS-CoV-2 throughout the world has created an enormous socioeconomic impact. Although there are several promising drug candidates in clinical trials, none is available clinically. Thus, the drug repurposing approach may help to overcome the current pandemic. METHODS: The main protease (Mpro) of SARS-CoV-2 is crucial for cleaving nascent polypeptide chains. Here, FDA-approved antiviral and anti-infection drugs were screened by high-throughput virtual screening (HTVS) followed by re-docking with standard-precision (SP) and extra-precision (XP) molecular docking. The most potent drug's binding was further validated by free energy calculations (Prime/MM-GBSA) and molecular dynamics (MD) simulation. RESULTS: Out of 1397 potential drugs, 157 showed considerable affinity toward Mpro. After HTVS, SP, and XP molecular docking, four high-affinity lead drugs (Iodixanol, Amikacin, Troxerutin, and Rutin) with docking energies -10.629 to -11.776kcal/mol range were identified. Among them, Amikacin exhibited the lowest Prime/MM-GBSA energy (-73.800kcal/mol). It led us to evaluate other aminoglycosides (Neomycin, Paramomycin, Gentamycin, Streptomycin, and Tobramycin) against Mpro. All aminoglycosides were bound to the substrate-binding site of Mpro and interacted with crucial residues. Altogether, Amikacin was found to be the most potent inhibitor of Mpro. MD simulations of the Amikacin-Mpro complex suggested the formation of a complex stabilized by hydrogen bonds, salt bridges, and van der Waals interactions. CONCLUSION: Aminoglycosides may serve as a scaffold to design potent drug molecules against COVID-19. However, further validation by in vitro and in vivo studies is required before using aminoglycosides as an anti-COVID-19 agent.

Targeting hub genes and pathways of innate immune response in COVID-19: A network biology perspective.

The current pandemic of 2019 novel coronavirus disease (COVID-19) caused by a novel virus strain, 2019-nCoV/SARS-CoV-2 have posed a serious threat to global public health and economy. It is largely unknown how the human immune system responds to this infection. A better understanding of the immune response to SARS-CoV-2 will be important to develop therapeutics against COVID-19. Here, we have used transcriptomic profile of human alveolar adenocarcinoma cells (A549) infected with SARS-CoV-2 and employed a network biology approach to generate human-virus interactome. Network topological analysis discovers 15 SARS-CoV-2 targets, which belongs to a subset of interferon (IFN) stimulated genes (ISGs). These ISGs (IFIT1, IFITM1, IRF7, ISG15, MX1, and OAS2) can be considered as potential candidates for drug targets in the treatments of COVID-19. We have identified significant interaction between ISGs and TLR3 agonists, like poly I: C, and imiquimod, and suggests that TLR3 agonists can be considered as a potential drug for drug repurposing in COVID-19. Our network centric analysis suggests that moderating the innate immune response is a valuable approach to target COVID-19.