Comparative pathology, molecular pathogenicity, immunological features, and genetic characterization of three highly pathogenic human coronaviruses (MERS-CoV, SARS-CoV, and SARS-CoV-2).

The last two decades have witnessed the emergence of three deadly coronaviruses (CoVs) in humans: severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There are still no reliable and efficient therapeutics to manage the devastating consequences of these CoVs. Of these, SARS-CoV-2, the cause of the currently ongoing coronavirus disease 2019 (COVID-19) pandemic, has posed great global health concerns. The COVID-19 pandemic has resulted in an unprecedented crisis with devastating socio-economic and health impacts worldwide. This highlights the fact that CoVs continue to evolve and have the genetic flexibility to become highly pathogenic in humans and other mammals. SARS-CoV-2 carries a high genetic homology to the previously identified CoV (SARS-CoV), and the immunological and pathogenic characteristics of SARS-CoV-2, SARS-CoV, and MERS contain key similarities and differences that can guide therapy and management. This review presents salient and updated information on comparative pathology, molecular pathogenicity, immunological features, and genetic characterization of SARS-CoV, MERS-CoV, and SARS-CoV-2; this can help in the design of more effective vaccines and therapeutics for countering these pathogenic CoVs.

Predictors of mortality in patients of COVID-19 pneumonia in intensive care unit: An observational study in a tertiary care hospital, Lucknow, India

Introduction: Coronavirus Disease-2019 (COVID-19) is caused by the Severe Acute Respiratory Coronavirus-2 (SARS-CoV-2) which is an enveloped positive-sense single-stranded RNA virus. Initial steps of the infection involve binding of the spike protein(S) of the virus to Angiotensin Converting Enzyme-2 (ACE-2) receptor on the mucosal surfaces of various organs like lungs, kidney, heart, intestine. Pathogenesis of complications are still poorly understood. Aim: This study was designed to find out the baseline biochemical parameters at the time of admission which may predict outcome in COVID-19 patients. Materials and Methods: This observational study was conducted in a dedicated COVID-19 hospital, Dr. Ram Manohar Lohia Institute of Medical Sciences (Dr. RMLIMS), Lucknow, Uttar Pradesh, India, from 1st July, 2020 to 30th November, 2020 . A total of 109 moderate to severe COVID-19 pneumonia patients who required Intensive Care Unit (ICU) admission, were enrolled. Based on their outcome, patients were divided into two groups: “Survived” and “Expired”. Biochemical characteristics of patients were compared among the two groups using univariate and multivariate analysis. Results: On Univariate analysis Coagulation profile, Prothrombin Time (PT), International Normalised Ratio (INR), Activated Partial Thromboplastin Time (APTT) and D-Dimer vales were raised significantly in the expired group. Among other acute phase reactants Lactate Dehydrogenase (LDH), C-Reactive Protein (CRP), Interleukin-6 (IL-6), and Creatinine Phosphokinase-MB (CPKMB) were raised in expired group and this difference was significant statistically too. On Multivariate analysis among all acute phase reactant only IL-6 was increased significantly. All other variables were found to be non-significantly associated with mortality, statistically (p-value<0.05). Conclusion: Baseline biochemical parameters have prognostic values in COVID-19 patients. Raised IL-6 levels can be viewed as an independent predictor of mortality among COVID-19 patients at the time of admission in ICU.

Impact of Co-morbidities on outcome of COVID-19 patients: An observational study among patients admitted to intensive care unit

Introduction: Coronavirus Disease-2019 (COVID-19) has been a major cause of apprehension, morbidity, and mortality in 2020. It had been postulated that associated co-morbid conditions in COVID-19 patients increase the severity of COVID-19 which leads to six times more chances of hospitalisation than patients without co-morbid condition. Mortality is also 12 times higher in such patients. Aim: To find out the association between co-morbidities and mortalities due to COVID-19 pneumonia. Materials and Methods: A prospective, observational study was conducted in a tertiary teaching institute of North India which was designated Level 3 (L-3) facility for treatment of COVID-19 patients. All 109 COVID-19 patients confirmed by Reverse Transcriptase - Polymerase Chain Reaction(RT-PCR), admitted in Intensive Care Unit (ICU) from 1st July 2020 to 30th November 2020 formed the sample of the study. Data were taken regarding past history, clinical histories and examinations and ICU care and treatments. Based on their final outcome at the end of ICU care, patients were divided into 2 groups-group 1 (Non-survivor or Expired) and group 2 (Survived) and intergroup differences were studied. results: COVID-19 infection was about three times more common in males. Severe category of COVID-19 patients had higher mortality (59.2 % of severe category expired during hospital course, 1.7% patients expired in moderate category group). Most common co-morbidities were hypertension (n=48, 46.8%) and diabetes (n=51, 44%). Multivariate analysis showed that co-morbidities in the form of chronic liver disease (OR -0.127(0.024-0.681, p-value 0.016)) and post tubercular sequel (OR 0.036(0.003-0.442, p-value 0.009)) were less likely to occur in COVID-19 patients who survived, thus making these co-morbidities significant contributor to the adverse outcomes in COVID-19 patients. More number of co-morbidities in a patient were associated with higher chance of mortality and this trend was significant statistically (p-value<0.001). conclusion: Patients with multiple co-morbidities, chronic liver disease and post tubercular sequel were associated with higher mortality in COVID-19 patients.

Clinico-demographic, hematological, and comorbidity profile of COVID-19 patients admitted at tertiary care center

Introduction: Severe acute respiratory syndrome coronavirus 2 has caused a worldwide pandemic. This study was aimed to describe the clinico-demographic, hematological, and comorbidity profile in a group of coronavirus disease 2019 (COVID-19) patients at a tertiary care center in north India. Materials and Methods: We conducted a prospective, single-center collection of data regarding clinico-epidemiological, hematological parameters, and comorbidity profile of COVID-19 patients admitted at a tertiary care facility. Results: Data from 200 patients with COVID-19 were collected and analyzed. The median age of the patients was 48 years, with 63% males, and 78% patients were from urban area. Ten patients were smokers while 11 patients were alcoholic. The occupation of 20.6% of patients was related directly or indirectly with medical or allied professions. The most common mode of transmission of COVID-19 was direct close contact with microbiologically confirmed patients. Fever (n = 93;46.5%) was the most common presenting symptom and the median duration of onset of symptoms before admission were 3.5 days (range 2-6 days). In hematological profile, lymphopenia (n = 48;24%) had been the most common documented finding. Comorbidities were present in 39 (19.5%) patients, of which diabetes mellitus (n = 25;12.5%) was the most common. Furthermore, the case fatality rate in our study was 1.5%. Conclusion: Among all patients of COVID-19 at our center, the characteristic findings included high proportion of male patients with younger to middle-age group, diabetes as most common comorbidity and people who were directly or indirectly in contact with the health-care system were more prone for developing the disease.

SARS-CoV-2/COVID-19 pandemic - an update. (Special Issue: SARS-CoV-2.)

The coronavirus disease - 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus virus - 2 (SARS-CoV-2), set it foots in China during December 2019 as a high-alert public health emergency. This malady had thereafter spread rapidly across the globe in more than 215 countries, affecting more than 50 million people and causing the death of nearly 1.3 million as of 9th November, 2020 and resulted in a massive panic, fear, and economic crashes in most of the world. A better understanding of the disease, the virus, structural biology, clinical manifestations, risk factors, transmission, diagnosis, treatment, and management can be extrapolated from the literature review of the research up to date. In addition, deliberations on animal linkages, spillover and zoonotic implications for exploring the actual origin of the disease and all possible animal-human interfaces, intermediate host;diagnosis for devising specific and sensitive tests of ease, accessibility and affordability;advances in the development of safe and effective vaccines and therapeutics for prevention and treatment;management of COVID-19 practicable in all countries;application of traditional or regularly used modalities including plant-based products and medicinal herbs against SARS-COV-2;nutritious dietary foods against this disease;and socio-economic impacts of COVID-19 can provide valuable information on these various aspects. Most of the research currently focuses on disease, development of a vaccine or therapeutic modalities. But the future mortality rate and virulence of virus not only depends on the evolution of the virus, but also on how we develop preventive measures and effective treatment as well as in advance preparedness. The present review highlights salient aspects of SARS-CoV-2/COVID-19, pathology, risk factors, transmission, diagnosis, potential treatment, and alternative/supportive therapeutic options.

A probe into performance analysis of real-time forecasting of endemic infectious diseases using machine learning and deep learning algorithms

The current work aims at probing the performance of real-time forecasting of endemic infectious diseases by means of machine learning and deep learning techniques. An LSTM-based time series forecasting framework and machine learning-based framework are proposed for forecasting the endemic infectious diseases in real time. With recent outbreaks of Ebola, Zika, cholera, and COVID 2019, a question is being raised on our alertness as well as preparedness toward controlling the spread of these pandemics. Accurate and reliable prediction occurrences of these diseases are compulsory for the health personals to enable timely response in handling these outbreaks. The diversities of the communities make it more complex along with the humongous data generated due to the convergence of SMAC technologies. The data generated due to this complex network is nonlinear and non-stationary. Processing of this data requires an effort from a multidimensional perspective. The current work proposed the utilization of machine learning and deep learning-based long short-term memory (LSTM) techniques for the assessment of time series forecasting of casualties in case of cholera outbreak that happened recently in Yemen. The feasibility of these two techniques is probed using performance evaluation metrics. The core objective of using these two techniques is in considering nonlinear and non-stationary behavior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

Novel coronavirus (Sars-cov-2): Molecular biology, pathogenesis, pathobiology and advances in treatment of covid-19 patients– an update

The novel coronavirus (CoV), earlier named 2019-nCoV, and later as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has now created havoc and panic across the globe by its severe ongoing pandemic. This virus has to date as of 23rd November 2020, killed nearly 1.4 million persons out of more than 59 million confirmed positive cases, while spreading rapidly in more than 215 countries and territories. Taxonomically, SARS-CoV-2 has been characterized in genus Betacoronavirus, which contains non-segmented positive-sense, single-stranded (ss) RNA genome of ~30 kb. The first two open reading frames (ORFs), ORF1a and ORF1b, of SARS-CoV-2, encode 16 non-structural proteins (nsp1-nsp16), whereas other ORFs encodes four main structural proteins (sp) [spike (s) by ORF2, envelope (E) by ORF4, membrane (M) by ORF5, nucleoprotein (N) by ORF9], and accessory proteins essential for the virus fitness, pathogenesis and host immunity evasion. Sequence alignments of SARS-CoV-2 with genomes of various coronaviruses showed 58% identity in the non-structural protein (nsp)-coding region, 43% with the structural protein (sp)-coding region and 54% with the whole genome. The full-length genome sequence of the 2019-nCoV sample showed only up to 79.60% similarity with SARS CoV, but up to 96% similarity with bat coronavirus (bat coronavirus RaTG13). This gives strong evidence that 2019-nCoV has originated from the bat. The genomic and evolutionary evidence of another coronavirus species from pangolins also show higher similarity to SARS-CoV at the whole-genome level. Apart from RaTG13, Pangolin-CoV is the most closely related CoV to SARS-CoV-2. During infection, the viral S protein interacts with the receptor protein of the human cell membrane, known as angiotensin-converting enzyme II (ACE2). Presently, SARS-CoV-2 vaccines and drugs are not available, for which researchers are trying hard to develop to tackle rising tide of COVID-19-pandemic. Early diagnosis, contact tracing, strict prevention and control measures, biosecurity, personal biosafety, disinfection and sanitization practices, social distancing are aiding in prevention with SARS-CoV-2 infection. Boosting immunity by intaking the balanced and nutritious food, nutraceuticals, herbs, and following physical exercises along with avoiding stress conditions enhance the fighting power of the body against SARS-CoV-2 infection and limiting the severity of COVID-19. The present article describes salient knowledge on SARS-CoV-2 structure, genomic organization, pathogenesis, pathobiology, and advances and progress being made to treat COVID-19 patients. © 2020, Editorial board of Journal of Experimental Biology and Agricultural Sciences. All rights reserved.

Asymptomatic community spread of coronavirus disease 2019( COVID 19) outbreak prediction using logistic regression

Corona virus disease (COVID-19) pandemic has become a major threat to the entire world. Antidotes and proper medications are still not found and determined to get cure from such virus. The report from World Health Organization (WHO) remits the COVID-19 as severe acute respiratory syndrome (SARS). Such virus is transmitted into human body via a respiratory droplets. Even, major symptoms for coronavirus patience are - tiredness, severe fever and dry cough but in most of the cases such symtoms are not found. This variety of coronavirus symptoms are termed as asymptomatic symptoms. The identification for such disease is very important into human body so that this can be stopped as community spread and reduces the effect of this as global pandemic. This paper provides an extensive study and predicts the outbreak of this disease with the aid of classification techniques of under machine learning. So that, the number of cases related to COVID-19 can be identified and subsequent arrangements have been made from the respective governments and medical doctors for future. Initially, this prediction model is implemented for short-term interval and later, such model based on internet of thing and machine learning, can also be set for estimating into long-term intervals for global as well as Indian perspective. Thelogistic regression and decisiontree techniqueshave been used for such cases predictions for this epidemic. © 2020 Ubiquity Press. All rights reserved.

SARS-CoV-2 / COVID-19 pandemic – an update

The coronavirus disease – 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus virus – 2 (SARS-CoV-2), set it foots in China during December 2019 as a high-alert public health emergency. This malady had thereafter spread rapidly across the globe in more than 215 countries, affecting more than 50 million people and causing the death of nearly 1.3 million as of 9th November, 2020 and resulted in a massive panic, fear, and economic crashes in most of the world. A better understanding of the disease, the virus, structural biology, clinical manifestations, risk factors, transmission, diagnosis, treatment, and management can be extrapolated from the literature review of the research up to date. In addition, deliberations on animal linkages, spillover and zoonotic implications for exploring the actual origin of the disease and all possible animal-human interfaces, intermediate host;diagnosis for devising specific and sensitive tests of ease, accessibility and affordability;advances in the development of safe and effective vaccines and therapeutics for prevention and treatment;management of COVID-19 practicable in all countries;application of traditional or regularly used modalities including plant-based products and medicinal herbs against SARS-COV-2;nutritious dietary foods against this disease;and socio-economic impacts of COVID-19 can provide valuable information on these various aspects. Most of the research currently focuses on disease, development of a vaccine or therapeutic modalities. But the future mortality rate and virulence of virus not only depends on the evolution of the virus, but also on how we develop preventive measures and effective treatment as well as in advance preparedness. The present review highlights salient aspects of SARS-CoV-2 / COVID-19, pathology, risk factors, transmission, diagnosis, potential treatment, and alternative / supportive therapeutic options. © 2020, Editorial board of Journal of Experimental Biology and Agricultural Sciences. All rights reserved.

COVID-19 and the world with co-morbidities of heart disease, hypertension and diabetes

Newly emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) pandemic has now spread across the globe in past few months while affecting 26 million people and leading to more than 0.85 million deaths as on 2nd September, 2020. Severity of SARS-CoV-2 infection increases in COVID-19 patients due to pre-existing health co-morbidities. This mini-review has focused on the three significant co-morbidities viz., heart disease, hypertension, and diabetes, which are posing high health concerns and increased mortality during this ongoing pandemic. The observed co-morbidities have been found to be associated with the increasing risk factors for SARS-CoV-2 infection and COVID-19 critical illness as well as to be associated positively with the worsening of the health condition of COVID-19 suffering individuals resulting in the high risk for mortality. SARS-CoV-2 enters host cell via angiotensin-converting enzyme 2 receptors. Regulation of crucial cardiovascular functions and metabolisms like blood pressure and sugar levels are being carried out by ACE2. This might be one of the reasons that contribute to the higher mortality in COVID-19 patients having co-morbidities. Clinical investigations have identified higher levels of creatinine, cardiac troponin I, alanine aminotransferase, NT-proBNP, creatine kinase, D-dimer, aspartate aminotransferase and lactate dehydrogenase in patients who have succumbed to death from COVID-19 as compared to recovered individuals. More investigations are required to identify the modes behind increased mortality in COVID-19 patients having co-morbidities of heart disease, hypertension, and diabetes. This will enable us to design and develop suitable therapeutic strategies for reducing the mortality. More attention and critical care need to be paid to such high risk patients suffering from co-morbidities during COVID-19 pandemic.