Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity.

Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported. Here, we aim to identify possible common factors in such adverse reactions to enable strategies that reduce the incidence of such reactions by using patient data to classify and characterise those at risk. We examined patient medical histories and data documenting postvaccination effects and outcomes. The data analyses were conducted using a range of statistical approaches followed by a series of machine learning classification algorithms. In most cases, a group of similar features was significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, taking other medications, type-2 diabetes, hypertension, allergic history and heart disease are the most significant pre-existing factors associated with the risk of poor outcome. In addition, long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were the most significant clinical predictors. The machine learning classifiers that are trained with medical history were also able to predict patients with complication-free vaccination and have an accuracy score above 90%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches.

Truncated Cauchy Power Weibull-G Class of Distributions: Bayesian and Non-Bayesian Inference Modelling for COVID-19 and Carbon Fiber Data

The Truncated Cauchy Power Weibull-G class is presented as a new family of distributions. Unique models for this family are presented in this paper. The statistical aspects of the family are explored, including the expansion of the density function, moments, incomplete moments (IMOs), residual life and reversed residual life functions, and entropy. The maximum likelihood (ML) and Bayesian estimations are developed based on the Type-II censored sample. The properties of Bayes estimators of the parameters are studied under different loss functions (squared error loss function and LINEX loss function). To create Markov-chain Monte Carlo samples from the posterior density, the Metropolis–Hasting technique was used with posterior density. Using non-informative and informative priors, a full simulation technique was carried out. The maximum likelihood estimator was compared to the Bayesian estimators using Monte Carlo simulation. To compare the performances of the suggested estimators, a simulation study was carried out. Real-world data sets, such as strength measured in GPA for single carbon fibers and impregnated 1000-carbon fiber tows, maximum stress per cycle at 31,000 psi, and COVID-19 data were used to demonstrate the relevance and flexibility of the suggested method. The suggested models are then compared to comparable models such as the Marshall–Olkin alpha power exponential, the extended odd Weibull exponential, the Weibull–Rayleigh, the Weibull–Lomax, and the exponential Lomax distributions.

Designing a multi-epitope vaccine candidate to combat MERS-CoV by employing an immunoinformatics approach.

Currently, no approved vaccine is available against the Middle East respiratory syndrome coronavirus (MERS-CoV), which causes severe respiratory disease. The spike glycoprotein is typically considered a suitable target for MERS-CoV vaccine candidates. A computational strategy can be used to design an antigenic vaccine against a pathogen. Therefore, we used immunoinformatics and computational approaches to design a multi-epitope vaccine that targets the spike glycoprotein of MERS-CoV. After using numerous immunoinformatics tools and applying several immune filters, a poly-epitope vaccine was constructed comprising cytotoxic T-cell lymphocyte (CTL)-, helper T-cell lymphocyte (HTL)-, and interferon-gamma (IFN-γ)-inducing epitopes. In addition, various physicochemical, allergenic, and antigenic profiles were evaluated to confirm the immunogenicity and safety of the vaccine. Molecular interactions, binding affinities, and the thermodynamic stability of the vaccine were examined through molecular docking and dynamic simulation approaches, during which we identified a stable and strong interaction with Toll-like receptors (TLRs). In silico immune simulations were performed to assess the immune-response triggering capabilities of the vaccine. This computational analysis suggested that the proposed vaccine candidate would be structurally stable and capable of generating an effective immune response to combat viral infections; however, experimental evaluations remain necessary to verify the exact safety and immunogenicity profile of this vaccine.

Network-based transcriptomic analysis identifies the genetic effect of COVID-19 to chronic kidney disease patients: A bioinformatics approach.

COVID-19 has emerged as global health threats. Chronic kidney disease (CKD) patients are immune-compromised and may have a high risk of infection by the SARS-CoV-2. We aimed to detect common transcriptomic signatures and pathways between COVID-19 and CKD by systems biology analysis. We analyzed transcriptomic data obtained from peripheral blood mononuclear cells (PBMC) infected with SARS-CoV-2 and PBMC of CKD patients. We identified 49 differentially expressed genes (DEGs) which were common between COVID-19 and CKD. The gene ontology and pathways analysis showed the DEGs were associated with "platelet degranulation", "regulation of wound healing", "platelet activation", "focal adhesion", "regulation of actin cytoskeleton" and "PI3K-Akt signalling pathway". The protein-protein interaction (PPI) network encoded by the common DEGs showed ten hub proteins (EPHB2, PRKAR2B, CAV1, ARHGEF12, HSP90B1, ITGA2B, BCL2L1, E2F1, TUBB1, and C3). Besides, we identified significant transcription factors and microRNAs that may regulate the common DEGs. We investigated protein-drug interaction analysis and identified potential drugs namely, aspirin, estradiol, rapamycin, and nebivolol. The identified common gene signature and pathways between COVID-19 and CKD may be therapeutic targets in COVID-19 patients with CKD comorbidity.

Bioinformatics and system biology approaches to identify the diseasome and comorbidities complexities of SARS-CoV-2 infection with the digestive tract disorders.

Coronavirus Disease 2019 (COVID-19), although most commonly demonstrates respiratory symptoms, but there is a growing set of evidence reporting its correlation with the digestive tract and faeces. Interestingly, recent studies have shown the association of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection with gastrointestinal symptoms in infected patients but any sign of respiratory issues. Moreover, some studies have also shown that the presence of live SARS-CoV-2 virus in the faeces of patients with COVID-19. Therefore, the pathophysiology of digestive symptoms associated with COVID-19 has raised a critical need for comprehensive investigative efforts. To address this issue we have developed a bioinformatics pipeline involving a system biological framework to identify the effects of SARS-CoV-2 messenger RNA expression on deciphering its association with digestive symptoms in COVID-19 positive patients. Using two RNA-seq datasets derived from COVID-19 positive patients with celiac (CEL), Crohn's (CRO) and ulcerative colitis (ULC) as digestive disorders, we have found a significant overlap between the sets of differentially expressed genes from SARS-CoV-2 exposed tissue and digestive tract disordered tissues, reporting 7, 22 and 13 such overlapping genes, respectively. Moreover, gene set enrichment analysis, comprehensive analyses of protein-protein interaction network, gene regulatory network, protein-chemical agent interaction network revealed some critical association between SARS-CoV-2 infection and the presence of digestive disorders. The infectome, diseasome and comorbidity analyses also discover the influences of the identified signature genes in other risk factors of SARS-CoV-2 infection to human health. We hope the findings from this pathogenetic analysis may reveal important insights in deciphering the complex interplay between COVID-19 and digestive disorders and underpins its significance in therapeutic development strategy to combat against COVID-19 pandemic.

Stem cell therapies and benefaction of somatic cell nuclear transfer cloning in COVID-19 era.

BACKGROUND: The global health emergency of COVID-19 has necessitated the development of multiple therapeutic modalities including vaccinations, antivirals, anti-inflammatory, and cytoimmunotherapies, etc. COVID-19 patients suffer from damage to various organs and vascular structures, so they present multiple health crises. Mesenchymal stem cells (MSCs) are of interest to treat acute respiratory distress syndrome (ARDS) caused by SARS-CoV-2 infection. MAIN BODY: Stem cell-based therapies have been verified for prospective benefits in copious preclinical and clinical studies. MSCs confer potential benefits to develop various cell types and organoids for studying virus-human interaction, drug testing, regenerative medicine, and immunomodulatory effects in COVID-19 patients. Apart from paving the ways to augment stem cell research and therapies, somatic cell nuclear transfer (SCNT) holds unique ability for a wide range of health applications such as patient-specific or isogenic cells for regenerative medicine and breeding transgenic animals for biomedical applications. Being a potent cell genome-reprogramming tool, the SCNT has increased prominence of recombinant therapeutics and cellular medicine in the current era of COVID-19. As SCNT is used to generate patient-specific stem cells, it avoids dependence on embryos to obtain stem cells. CONCLUSIONS: The nuclear transfer cloning, being an ideal tool to generate cloned embryos, and the embryonic stem cells will boost drug testing and cellular medicine in COVID-19.

Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images

The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in several studies to detect COVID-19 symptoms due to the success in chest radiography image classification, cost efficiency, lack of expert radiologists, and the need for faster processing in the pandemic area. Most of the promising algorithms proposed in different studies are based on pre-trained deep learning models. Such open-source models and lack of variation in the radiology image-capturing environment make the diagnosis system vulnerable to adversarial attacks such as fast gradient sign method (FGSM) attack. This study therefore explored the potential vulnerability of pre-trained convolutional neural network algorithms to the FGSM attack in terms of two frequently used models, VGG16 and Inception-v3. Firstly, we developed two transfer learning models for X-ray and CT image-based COVID-19 classification and analyzed the performance extensively in terms of accuracy, precision, recall, and AUC. Secondly, our study illustrates that misclassification can occur with a very minor perturbation magnitude, such as 0.009 and 0.003 for the FGSM attack in these models for X-ray and CT images, respectively, without any effect on the visual perceptibility of the perturbation. In addition, we demonstrated that successful FGSM attack can decrease the classification performance to 16.67% and 55.56% for X-ray images, as well as 36% and 40% in the case of CT images for VGG16 and Inception-v3, respectively, without any human-recognizable perturbation effects in the adversarial images. Finally, we analyzed that correct class probability of any test image which is supposed to be 1, can drop for both considered models and with increased perturbation;it can drop to 0.24 and 0.17 for the VGG16 model in cases of X-ray and CT images, respectively. Thus, despite the need for data sharing and automated diagnosis, practical deployment of such program requires more robustness.

Diverse Immunological Factors Influencing Pathogenesis in Patients with COVID-19: A Review on Viral Dissemination, Immunotherapeutic Options to Counter Cytokine Storm and Inflammatory Responses.

The pathogenesis of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still not fully unraveled. Though preventive vaccines and treatment methods are out on the market, a specific cure for the disease has not been discovered. Recent investigations and research studies primarily focus on the immunopathology of the disease. A healthy immune system responds immediately after viral entry, causing immediate viral annihilation and recovery. However, an impaired immune system causes extensive systemic damage due to an unregulated immune response characterized by the hypersecretion of chemokines and cytokines. The elevated levels of cytokine or hypercytokinemia leads to acute respiratory distress syndrome (ARDS) along with multiple organ damage. Moreover, the immune response against SARS-CoV-2 has been linked with race, gender, and age; hence, this viral infection's outcome differs among the patients. Many therapeutic strategies focusing on immunomodulation have been tested out to assuage the cytokine storm in patients with severe COVID-19. A thorough understanding of the diverse signaling pathways triggered by the SARS-CoV-2 virus is essential before contemplating relief measures. This present review explains the interrelationships of hyperinflammatory response or cytokine storm with organ damage and the disease severity. Furthermore, we have thrown light on the diverse mechanisms and risk factors that influence pathogenesis and the molecular pathways that lead to severe SARS-CoV-2 infection and multiple organ damage. Recognition of altered pathways of a dysregulated immune system can be a loophole to identify potential target markers. Identifying biomarkers in the dysregulated pathway can aid in better clinical management for patients with severe COVID-19 disease. A special focus has also been given to potent inhibitors of proinflammatory cytokines, immunomodulatory and immunotherapeutic options to ameliorate cytokine storm and inflammatory responses in patients affected with COVID-19.

Role of Inflammatory Cytokines in COVID-19 Patients: A Review on Molecular Mechanisms, Immune Functions, Immunopathology and Immunomodulatory Drugs to Counter Cytokine Storm.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a severe pandemic of the current century. The vicious tentacles of the disease have been disseminated worldwide with unknown complications and repercussions. Advanced COVID-19 syndrome is characterized by the uncontrolled and elevated release of pro-inflammatory cytokines and suppressed immunity, leading to the cytokine storm. The uncontrolled and dysregulated secretion of inflammatory and pro-inflammatory cytokines is positively associated with the severity of the viral infection and mortality rate. The secretion of various pro-inflammatory cytokines such as TNF-α, IL-1, and IL-6 leads to a hyperinflammatory response by recruiting macrophages, T and B cells in the lung alveolar cells. Moreover, it has been hypothesized that immune cells such as macrophages recruit inflammatory monocytes in the alveolar cells and allow the production of large amounts of cytokines in the alveoli, leading to a hyperinflammatory response in severely ill patients with COVID-19. This cascade of events may lead to multiple organ failure, acute respiratory distress, or pneumonia. Although the disease has a higher survival rate than other chronic diseases, the incidence of complications in the geriatric population are considerably high, with more systemic complications. This review sheds light on the pivotal roles played by various inflammatory markers in COVID-19-related complications. Different molecular pathways, such as the activation of JAK and JAK/STAT signaling are crucial in the progression of cytokine storm; hence, various mechanisms, immunological pathways, and functions of cytokines and other inflammatory markers have been discussed. A thorough understanding of cytokines' molecular pathways and their activation procedures will add more insight into understanding immunopathology and designing appropriate drugs, therapies, and control measures to counter COVID-19. Recently, anti-inflammatory drugs and several antiviral drugs have been reported as effective therapeutic drug candidates to control hypercytokinemia or cytokine storm. Hence, the present review also discussed prospective anti-inflammatory and relevant immunomodulatory drugs currently in various trial phases and their possible implications.

Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development.

BACKGROUND: Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. OBJECTIVE: Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. METHODS: We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. RESULTS: Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19-positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction. CONCLUSIONS: We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.