Predictors of non-invasive ventilation failure in adult patients with cardiac dysfunction presenting with community-acquired pneumonia: an Egyptian multicenter prospective study

Non-invasive ventilation (NIV) might be successful if carefully selected in adult patients with cardiac dysfunction presenting with community-acquired pneumonia. The main objective of this study was to identify the early predictors of NIV failure. Adult patients with left ventricle ejection fraction (LV EF) <50% admitted to the intensive care unit (ICU) with community-acquired pneumonia and acute respiratory failure were enrolled in this multicenter prospective study after obtaining informed consents (study registrationID: ISRCTN14641518). Non-invasive ventilation failure was defined as the requirement of intubation after initiation of NIV. All patients were assessed using the Acute Physiology and Chronic Health Evaluation II (APACHE II) and sequential organ failure assessment (SOFA) scores at admission, while their Heart rate Acidosis Consciousness Oxygenation and Respiratory rate (HACOR) and lung ultrasound (LUS) scores in addition to blood lactate were assessed at NIV initiation and 12 and 24 hours later. A total of 177 patients were prospectively enrolled from February 2019 to July 2020. Of them, 53 (29.9%) had failed NIV. The mean age of the study cohort was 64.1+or- 12.6 years, with a male predominance (73.4%) and a mean LV EF of 36.4 +or- 7.8%. Almost 55.9% of the studied patients had diabetes mellitus, 45.8% had chronic systemic hypertension, 73.4% had ischemic heart disease, 20.3% had chronic kidney disease, and 9.6% had liver cirrhosis. No significant differences were observed between the NIV success and NIV failure groups regarding underlying morbidities or inflammatory markers. Patients who failed NIV were significantly older and had higher mean SOFA and APACHE II scores than those with successful NIV. We also found that NIV failure was associated with longer ICU stay (p < 0.001), higher SOFA scores at 48 hours (p < 0.001) and higher mortality (p < 0.001) compared with the NIV success group. In addition, SOFA (Odds Ratio (OR): 4.52, 95% Confidence Interval (CI): 2.59-7.88, p < 0.001), HACOR (OR: 2.01, 95% CI: 0.97-4.18, p = 0.036) and LUS (OR: 1.33, 95% CI: 1.014-1.106, p = 0.027) scores and blood lactate levels (OR: 9.35, 95% CI: 5.32-43.26, p < 0.001) were independent factors for NIV failure. High initial HACOR and SOFA scores, persistent hyperlactatemia and non-decrementing LUS score were associated with early NIV failure in patients with cardiac dysfunction presenting with community-acquired pneumonia, and could be used as clinical and paraclinical variables for early decision making regarding invasive ventilation.

Probiotic-Based Bacteriocin: Immunity Supplementation Against Viruses. An Updated Review.

Viral infections are a major cause of severe, fatal diseases worldwide. Recently, these infections have increased due to demanding contextual circumstances, such as environmental changes, increased migration of people and product distribution, rapid demographic changes, and outbreaks of novel viruses, including the COVID-19 outbreak. Internal variables that influence viral immunity have received attention along with these external causes to avert such novel viral outbreaks. The gastrointestinal microbiome (GIM), particularly the present probiotics, plays a vital role in the host immune system by mediating host protective immunity and acting as an immune regulator. Bacteriocins possess numerous health benefits and exhibit antagonistic activity against enteric pathogens and immunobiotics, thereby inhibiting viral infections. Moreover, disrupting the homeostasis of the GIM/host immune system negatively affects viral immunity. The interactions between bacteriocins and infectious viruses, particularly in COVID-19, through improved host immunity and physiology are complex and have not yet been studied, although several studies have proven that bacteriocins influence the outcomes of viral infections. However, the complex transmission to the affected sites and siRNA defense against nuclease digestion lead to challenging clinical trials. Additionally, bacteriocins are well known for their biofunctional properties and underlying mechanisms in the treatment of bacterial and fungal infections. However, few studies have shown the role of probiotics-derived bacteriocin against viral infections. Thus, based on the results of the previous studies, this review lays out a road map for future studies on bacteriocins for treating viral infections.

SARS-CoV-2 mutation hotspots incidence in different geographic regions

SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is RNA virus with a positive-sense single-strand that belongs to the beta-coronavirus group that causes COVID-19 (Coronavirus Disease 2019) which originally emerged in China. Viruses with RNA genomes are known by a high mutation rate potential. The mutation rate determines genome variability and evolution of the virus;therefore, allowing viruses to evade the immune system, gain more infectivity potentials, virulence modifications, and probably resistance development to antivirals. A total of 311 SARS-CoV-2 virus whole genome sequences have been retrieved from the GISAID database from 1st of January 2020 to 31th of August 2020. The sequences were analyzed for sequence purity and multiple sequence alignment together with reference sequence was conducted through using Clustal Omega that is imbedded in Jalview software and Blast tools. We recorded the occurrence of 4 newly incident high frequently occurring mutations in all six geographic regions, namely at positions 2416, 18877, 23401, and 27964. The majority of all recorded hotspots were detected in Asia, Europe, and North America. The findings of our study suggest that the SARS-CoV-2 is in continuous evolution. For the impact of these mutations, further investigations are required and to understand whether these mutations would lead to the appearance of Drug-resistance viral strains, strains with increased infectivity and pathogenicity, and also their effect on the vaccine development and immunogenesis.

Deep Neural Network for Virus Mutation Prediction: A Comprehensive Review

Artificial intelligence (AI) and Deep Learning Algorithms are potential methods for preventing the alarmingly widespread RNA viruses and ensuring pandemic safety, they have become an integrative part of the modern scientific methodology, offering automated procedures for the prediction of a phenomenon based on past observations, unraveling underlying patterns in data and providing insights about the problem. With the continuous growth in the number of RNA Virus COVID-19 patients, likely, doctors and healthcare personnel won’t be helpful in treating every case. Thus, data scientists can help in the battle against RNA Viruses Mutations by implementing more innovative solutions in order to accomplish controlling severe acute respiratory syndrome quickly RNA Viruses are viruses that are made up of strands of RNA. This work studies the induction of machine learning models and motivating their design and purpose whenever possible. In the second part of this work, we analyze and discuss the biological data in the eyes of deep learning models. The core of our contributions rests in the role of machine learning in viruses pandemics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Relationship between Spirituality, Nature Connectedness, and Burnout of Schoolteachers during Online Classes amid Covid-19 Pandemic: The Moderating Role of Gender

The substantial changes in the workplace caused by the Covid-19 pandemic restrictions have contributed to teacher burnout. The purpose of this study was to gain an understanding of the relationships between spirituality, connectedness to nature, and burnout in schoolteachers, as well as to investigate the mediating part of spirituality in the relationship between connectedness to nature and burnout, and the moderating role of gender. This study was conducted using a quantitative method, with a sample size of 123 schoolteachers in Malaysia. Data analysis using partial least squares-structural equation modeling (PLS-SEM) revealed that schoolteachers who had a strong connection to nature were less likely to experience burnout. Spirituality acted as a buffer in the relationship between connection to nature and burnout. The results also differed according to gender for the nature connectedness-burnout relationship. Going forward, the findings of this study offer practitioners better insights about the importance of selected factors, including nature concreteness and spirituality as a promising avenue for reducing burnout among schoolteachers during online classes amid the Covid-19 pandemic. © 2022 Society for Research and Knowledge Management. All rights reserved.

Acute Corneal Graft Rejection Following COVID-19 Vaccination (preprint)

Purpose To describe a case of acute corneal endothelial rejection as early as 3 days following the second dose of the Moderna messenger RNA-1273 corona virus-19 (COVID-19) vaccine.Observations: An 81-year-old patient who had a penetrating keratoplasty 1 month earlier for a pseudophakic bullous keratopathy developed acute corneal graft rejection 3 days after the second dose of COVID-19 vaccine. Despite 4 weeks after intensive treatment, the corneal graft eventually failed.Conclusions and Importance : Although a direct causative effect is difficult to establish, the timeline of events beginning after the COVID-19 vaccination supports that it might have triggered the rejection episode. Consistent advice must be given to corneal transplant surgeons and patients regarding such possibility and the importance of the follow up after having the vaccination.

Evolving Risk of Acute Kidney Injury in COVID-19 Hospitalized Patients: A Single Center Retrospective Study.

Background and Objectives: Within a year, COVID-19 has advanced from an outbreak to a pandemic, spreading rapidly and globally with devastating impact. The pathophysiological link between COVID-19 and acute kidney injury (AKI) is currently being debated among scientists. While some studies have concluded that the mechanisms of AKI in COVID-19 patients are complex and not fully understood, others have claimed that AKI is a rare complication of COVID-19-related disorders. Considering this information gap and its possible influence on COVID-19-associated AKI management, our study aimed to explore the prevalence of AKI and to identify possible risk factors associated with AKI development among COVID-19 hospitalized patients. Materials and Methods: A retrospective cohort study included 83 laboratory-confirmed COVID-19 patients hospitalized at the isolation department in a tertiary hospital in Zagazig City, Egypt between June and August 2020. Patients younger than 18 years of age, those diagnosed with end-stage kidney disease, or those on nephrotoxic medications were excluded. All study participants had a complete blood count, liver and renal function tests, hemostasis parameters examined, inflammatory markers, serum electrolytes, routine urinalysis, arterial blood gas, and non-enhanced chest and abdominal computer tomography (CT) scans. Results: Of the 83 patients, AKI developed in 24 (28.9%) of them, of which 70.8% were in stage 1, 8.3% in stage 2, and 20.8% in stage 3. Patients with AKI were older than patients without AKI, with hypertension and diabetes being the most common comorbidities. Risk factors for AKI include increased age, hypertension, diabetes mellitus, and a higher sequential organ failure assessment (SOFA) score. Conclusions: AKI occurs in a considerable percentage of patients with COVID-19, especially in elderly males, those with hypertension, diabetes, and a higher sequential organ failure assessment (SOFA) score. Hence, the presence of AKI should be taken into account as an important index within the risk spectrum of disease severity for COVID-19 patients.

Next Generation Sequence Prediction Intelligent System for SARS-COV-2 Using Deep Learning Neural Network

Viral mutations can occur that prevent antibody neutralization, an event known as viral escape, which can disrupt vaccine manufacturing. Viruses' potential to develop and escape the body's immune system, as well as the infection cause, is known as viral escape, and it continues to be a stumbling block in the development of treatments and vaccines. Understanding the rules of virus mutations can help in the development of a therapeutic plan. Using machine learning algorithms that designed for natural language processing was emulated for viral escape. The mutations that protect viral infectivity, but make a virus show up distinctive from the immune system, comparable to word changes that protect the language structure of a sentence, but change its meanings. In this work the seq2seq LSTM neural network language models applied on two datasets of different viruses like SARS-CoV-2 and HIV. The prediction model achieves accuracy 97 % for HIV validation dataset and 99.6% for coronavirus strain validation dataset. It shows superior results over other prediction techniques as well. © 2021 IEEE.

Effectiveness of Distance Education Systems in the COVID Pandemic: A Case Study from the University of Jeddah

COVID-19 and recently COVID-20 are spreading dramatically worldwide. Vaccines have been made available. Preventive actions are still the most effective in confronting this infectious disease. One of the preventive actions is the social distancing between people aiming to decrease the infection. Social distancing is very useful in many fields characterized by a large number of connections, like education. In this paper, we examine the effectiveness of the distance education delivered a the University of Jeddah, on the BlackBoard (BB), in the age of the COVID pandemic. A survey is conducted with a total of 791 student participants. The questionnaire has three main variables related to distance education, examinations, and other environmental factors. Differen master's/undergraduate students are involved. All education levels are als involved as well as different GPAs. Results show that 89% of the participants agree on BB's effectiveness in the pandemic age. Also, 60.6% o the participants express the effectiveness of BB regardless of the pandemic continuity. Cronbach's alpha equals 0.9. Students are willing to use distance technology in the testing and examinations process more than using the in the education process. A positive correlation exists between the three variables at a confidence interval of 95%. ANOVA tests show response differences based on colleges, GPA, educational level, and educational stage. These results are significant as the University may decide to continue on these services after the pandemic ends. (C) 2021 INT TRANS J ENG MANAG SCI TECH.

Using artificial intelligence to risk stratify COVID-19 patients based on chest X-ray findings.

BACKGROUND: Deep learning-based radiological image analysis could facilitate use of chest x-rays as a triaging tool for COVID-19 diagnosis in resource-limited settings. This study sought to determine whether a modified commercially available deep learning algorithm (M-qXR) could risk stratify patients with suspected COVID-19 infections. METHODS: A dual track clinical validation study was designed to assess the clinical accuracy of M-qXR. The algorithm evaluated all Chest-X-rays (CXRs) performed during the study period for abnormal findings and assigned a COVID-19 risk score. Four independent radiologists served as radiological ground truth. The M-qXR algorithm output was compared against radiological ground truth and summary statistics for prediction accuracy were calculated. In addition, patients who underwent both PCR testing and CXR for suspected COVID-19 infection were included in a co-occurrence matrix to assess the sensitivity and specificity of the M-qXR algorithm. RESULTS: 625 CXRs were included in the clinical validation study. 98% of total interpretations made by M-qXR agreed with ground truth (p = 0.25). M-qXR correctly identified the presence or absence of pulmonary opacities in 94% of CXR interpretations. M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary opacities were 94%, 95%, 99%, and 88% respectively. M-qXR correctly identified the presence or absence of pulmonary consolidation in 88% of CXR interpretations (p = 0.48). M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary consolidation were 91%, 84%, 89%, and 86% respectively. Furthermore, 113 PCR-confirmed COVID-19 cases were used to create a co-occurrence matrix between M-qXR's COVID-19 risk score and COVID-19 PCR test results. The PPV and NPV of a medium to high COVID-19 risk score assigned by M-qXR yielding a positive COVID-19 PCR test result was estimated to be 89.7% and 80.4% respectively. CONCLUSION: M-qXR was found to have comparable accuracy to radiological ground truth in detecting radiographic abnormalities on CXR suggestive of COVID-19.

Integrated Platform Addressing the Finger-Prick Blood Processing Challenges of Point-of-Care Electrical Biomarker Testing.

Continued advances in label-free electrical biosensors pave the way to simple, rapid, cost-effective, high-sensitivity, and quantitative biomarker testing at the point-of-care setting that would profoundly transform healthcare. However, implementation in routine diagnostics is faced with significant challenges associated with the inherent requirement for biofluid sample processing before and during testing. We present here a simple yet robust autonomous finger-prick blood sample processing platform integrated with nanoscale field-effect transistor biosensors and demonstrate the feasibility of measuring the SARS-CoV-2 nucleocapsid protein. The 3D-printed platform incorporates a high-yield blood-to-plasma separation module and a delay valve designed to terminate the assay at a specific time. The platform is driven by hydrostatic pressure to efficiently and automatically dispense plasma and washing/measurement buffer to the nanosensors. Our model study demonstrates the feasibility of detecting down to 1.4 pg/mL of the SARS-CoV-2 nucleocapsid protein within 25 min and with only minimal operator intervention.

Emergence, evolution, and vaccine production approaches of SARS-CoV-2 virus: Benefits of getting vaccinated and common questions.

The emergence of coronavirus disease 2019 (COVID-19) pandemic in Wuhan city, China at the end of 2019 made it urgent to identify the origin of the causal pathogen and its molecular evolution, to appropriately design an effective vaccine. This study analyzes the evolutionary background of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or SARS-2) in accordance with its close relative SARS-CoV (SARS-1), which was emerged in 2002. A comparative genomic and proteomic study was conducted on SARS-2, SARS-1, and Middle East respiratory syndrome coronavirus (MERS), which was emerged in 2012. In silico analysis inferred the genetic variability among the tested viruses. The SARS-1 genome harbored 11 genes encoding 12 proteins, while SARS-2 genome contained only 10 genes encoding for 10 proteins. MERS genome contained 11 genes encoding 11 proteins. The analysis also revealed a slight variation in the whole genome size of SARS-2 comparing to its siblings resulting from sequential insertions and deletions (indels) throughout the viral genome particularly ORF1AB, spike, ORF10 and ORF8. The effective indels were observed in the gene encoding the spike protein that is responsible for viral attachment to the angiotensin-converting enzyme 2 (ACE2) cell receptor and initiating infection. These indels are responsible for the newly emerging COVID-19 variants αCoV, ßCoV, γCoV and δCoV. Nowadays, few effective COVID-19 vaccines developed based on spike (S) glycoprotein were approved and become available worldwide. Currently available vaccines can relatively prevent the spread of COVID-19 and suppress the disease. The traditional (killed or attenuated virus vaccine and antibody-based vaccine) and innovated vaccine production technologies (RNA- and DNA-based vaccines and viral vectors) are summarized in this review. We finally highlight the most common questions related to COVID-19 disease and the benefits of getting vaccinated.

Angiotensin Converting Enzyme-2 (ACE-2) role in disease and future in research

Coronavirus Disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Like the 2002-2003 epidemic severe acute respiratory syndrome coronavirus (SARS-CoV), angiotensin converting enzyme-2 (ACE-2) has been identified as the SARS-CoV-2 receptor.1-3 The virus docks into host cell via its spike protein binding to ACE-2 and undergoes proteolytic cleavage by TMPRSS2 protease to facilitate membrane fusion. The spike protein binding to ACE-2 has been shown to be stronger in the novel SARS-CoV-2 virus.1 This review will present an overview of ACE-2 biology.

Detection of COVID-19 from chest CT images using selected fourier transform features

In this paper, a novel method is proposed for COVID-19 detection from chest images. The proposed method uses some important features from both spatial and the Fourier transform of the input images. The binary particle swarm optimization is used to select the most relevant features. Two common classifiers are used for testing;support vector machine and k-nearest neighbor. Results show that the k-nearest neighbor outperforms support vector machine. The accuracy of the proposed method outperforms other algorithms in the literature. The accuracy of the proposed method approximately equals 91% when using the proposed features combined with the binary particle swarm optimization (BPSO). The sensitivity exceeds 89%, and also outperforms that proposed in previous work. Specificity is also maintained. These important findings may represent physicians' importance in decreasing diagnosis time and cost using automated systems. These systems may be useful for physicians in case of resource limitation. © 2021 Little Lion Scientific

In Silico Mining of Terpenes from Red-Sea Invertebrates for SARS-CoV-2 Main Protease (M^pro) Inhibitors

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent for the COVID-19 pandemic, which generated more than 1.82 million deaths in 2020 alone, in addition to 83.8 million infections. Currently, there is no antiviral medication to treat COVID-19. In the search for drug leads, marine-derived metabolites are reported here as prospective SARS-CoV-2 inhibitors. Two hundred and twenty-seven terpene natural products isolated from the biodiverse Red-Sea ecosystem were screened for inhibitor activity against the SARS-CoV-2 main protease (M^pro) using molecular docking and molecular dynamics (MD) simulations combined with molecular mechanics/generalized Born surface area binding energy calculations. On the basis of in silico analyses, six terpenes demonstrated high potency as M^pro inhibitors with DELTAG_binding <= -40.0 kcal/mol. The stability and binding affinity of the most potent metabolite, erylosides B, were compared to the human immunodeficiency virus protease inhibitor, lopinavir. Erylosides B showed greater binding affinity towards SARS-CoV-2 M^pro than lopinavir over 100 ns with DELTAG_binding values of -51.9 vs. -33.6 kcal/mol, respectively. Protein-protein interactions indicate that erylosides B biochemical signaling shares gene components that mediate severe acute respiratory syndrome diseases, including the cytokine- and immune-signaling components BCL2L1, IL2, and PRKC. Pathway enrichment analysis and Boolean network modeling were performed towards a deep dissection and mining of the erylosides B target-function interactions. The current study identifies erylosides B as a promising anti-COVID-19 drug lead that warrants further in vitro and in vivo testing.

COVID-19: pathogenesis, advances in treatment and vaccine development and environmental impact-an updated review.

Diseases negatively impact the environment, causing many health risks and the spread of pollution and hazards. A novel coronavirus, severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has led to a recent respiratory syndrome epidemic in humans. In December 2019, the sudden emergence of this new coronavirus and the subsequent severe disease it causes created a serious global health threat and hazards. This is in contrast to the two aforementioned coronaviruses, SARS-CoV-2 (in 2002) and middle east respiratory syndrome coronavirus MERS-CoV (in 2012), which were much more easily contained. The World Health Organization (WHO) dubbed this contagious respiratory disease an "epidemic outbreak" in March 2020. More than 80 companies and research institutions worldwide are working together, in cooperation with many governmental agencies, to develop an effective vaccine. To date, six authorized vaccines have been registered. Up till now, no approved drugs and drug scientists are racing from development to clinical trials to find new drugs for COVID-19. Wild animals, such as snakes, bats, and pangolins are the main sources of coronaviruses, as determined by the sequence homology between MERS-CoV and viruses in these animals. Human infection is caused by inhalation of respiratory droplets. To date, the only available treatment protocol for COVID-19 is based on the prevalent clinical signs. This review aims to summarize the current information regarding the origin, evolution, genomic organization, epidemiology, and molecular and cellular characteristics of SARS-CoV-2 as well as the diagnostic and treatment approaches for COVID-19 and its impact on global health, environment, and economy.

Towards Reinforcing Healthcare 4.0: A Green Real-Time IIoT Scheduling and Nesting Architecture for COVID-19 Large-Scale 3D Printing Tasks.

With declaring the highly transmissible COVID-19 as a pandemic, an unprecedented strain on healthcare infrastructures worldwide occurred. An enormous shortage in the personal protective equipment (PPE) and the spare parts (SP) for the mechanical ventilators ensued as a consequence of the failure of the centralized global supply chains. Additive manufacturing and Industrial Internet of Things (IIoT), as the pillars of Industry 4.0, arose as the robust noncentralized alternatives. When gathered and properly managed in the IIoT, 3D Printers (3DPs) can complement and support Healthcare 4.0 to face the current and future pandemics. Thus, this paper proposes a real-time green allocation and scheduling architecture designed and dedicated particularly for the large-scale distributed 3D printing tasks (3DPTs) of both PPE and SPs. Our proposed architecture comprises; a broker (B) and a cluster manager (CM). Dynamic status check for the 3DPs and admission control for 3DPTs are among the interconnected roles of CM. CM also performs task allocation and scheduling according to our proposed Online Ascending Load-Balancing Modified Best-Fit (OALMBF) allocation algorithm and Green Real-time Nesting Priority-Based Adaptive (GRNPA) scheduling algorithm. The performance of the proposed architecture was investigated under extremely high-load environments which resulted in a success ratio and a response rate of 99.9667% and 10.9665 seconds, respectively, for the 3000 3DPTs trial. These results proved the robustness and the scalability of our architecture that surpasses its state-of-the-art counterparts. Besides respecting the real-time requirements of the 3DPTs, the proposed architecture improves the utilization of the 3DPs and guarantees an even workload distribution.

COVID-19 in Human, Animal, and Environment: A Review.

The medical authority in China, especially in Wuhan city, reported on December 2019 a large number of highly fatal, rapidly spreading viral pneumonia caused by an unknown coronavirus. The common history of all the patients was their visiting a Wuhan's whole food store, where live animals and seafood are sold. Irrespective of the efforts of the Chinese authorities, the virus spread rapidly all over the world by travelers, provoking widespread attention by the media and panic. Many previous coronavirus epidemics had been recorded, such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), and the recently newly discovered epidemic is named coronavirus disease of 2019 (COVID-19). This disease is caused by SARS Coronavirus-2 (SARS-CoV-2), and this virus is antigenically related to the SARS virus (SARS-CoV), which had been detected in 2002, depending on clinical, serological, and molecular findings. There is rapid competition among the researchers to discover the source of the virus, understand the mechanism of the disease development, establish treatment strategies, and determine the factors affecting the incidence of infection and severity of the disease, and focus on the production of a vaccine. Coronaviruses are a group of single-stranded, positive-sense RNA genome viruses; its genome length varies from 26 to 32 kb. Coronavirus causes mild to severe respiratory disorders. In December 2019, several cases of pneumonia of unknown causes were found in Wuhan city, which is located in the Hubei province in China. Chinese health authorities investigated the problem and found that a new virus caused such infection and, using next-generation sequencing, found the 2019 novel coronavirus (2019-nCoV). It has been transferred from humans to humans and animals to humans (zoonotic). Coronaviruses cause multiple respiratory problems, varying from common cold to severe infections such as SARS. General symptoms of infection include fatigue, cough, and breathing problems such as shortness of breath, as described by World Health Organization. Serious cases may result in pneumonia, renal failure, and even death. We address current information about the new SARS Coronavirus-2 as well as the COVID-19 disease caused by it in this review.