Myasthenia Gravis: Novel Findings and Perspectives on Traditional to Regenerative Therapeutic Interventions.

The prevalence of myasthenia gravis (MG), an autoimmune disorder, is increasing among all subsets of the population leading to an elevated economic and social burden. The pathogenesis of MG is characterized by the synthesis of autoantibodies against the acetylcholine receptor (AChR), low-density lipoprotein receptor-related protein 4 (LRP4), or muscle-specific kinase at the neuromuscular junction, thereby leading to muscular weakness and fatigue. Based on clinical and laboratory examinations, the research is focused on distinguishing MG from other autoimmune, genetic diseases of neuromuscular transmission. Technological advancements in machine learning, a subset of artificial intelligence (AI) have been assistive in accurate diagnosis and management. Besides, addressing the clinical needs of MG patients is critical to improving quality of life (QoL) and satisfaction. Lifestyle changes including physical exercise and traditional Chinese medicine/herbs have also been shown to exert an ameliorative impact on MG progression. To achieve enhanced therapeutic efficacy, cholinesterase inhibitors, immunosuppressive drugs, and steroids in addition to plasma exchange therapy are widely recommended. Under surgical intervention, thymectomy is the only feasible alternative to removing thymoma to overcome thymoma-associated MG. Although these conventional and current therapeutic approaches are effective, the associated adverse events and surgical complexity limit their wide application. Moreover, Restivo et al. also, to increase survival and QoL, further recent developments revealed that antibody, gene, and regenerative therapies (such as stem cells and exosomes) are currently being investigated as a safer and more efficacious alternative. Considering these above-mentioned points, we have comprehensively reviewed the recent advances in pathological etiologies of MG including COVID-19, and its therapeutic management.

Evaluating the impact of multiple factors on the control of COVID-19 epidemic: A modelling analysis using India as a case study.

The currently ongoing COVID-19 outbreak remains a global health concern. Understanding the transmission modes of COVID-19 can help develop more effective prevention and control strategies. In this study, we devise a two-strain nonlinear dynamical model with the purpose to shed light on the effect of multiple factors on the outbreak of the epidemic. Our targeted model incorporates the simultaneous transmission of the mutant strain and wild strain, environmental transmission and the implementation of vaccination, in the context of shortage of essential medical resources. By using the nonlinear least-square method, the model is validated based on the daily case data of the second COVID-19 wave in India, which has triggered a heavy load of confirmed cases. We present the formula for the effective reproduction number and give an estimate of it over the time. By conducting Latin Hyperbolic Sampling (LHS), evaluating the partial rank correlation coefficients (PRCCs) and other sensitivity analysis, we have found that increasing the transmission probability in contact with the mutant strain, the proportion of infecteds with mutant strain, the ratio of probability of the vaccinated individuals being infected, or the indirect transmission rate, all could aggravate the outbreak by raising the total number of deaths. We also found that increasing the recovery rate of those infecteds with mutant strain while decreasing their disease-induced death rate, or raising the vaccination rate, both could alleviate the outbreak by reducing the deaths. Our results demonstrate that reducing the prevalence of the mutant strain, improving the clearance of the virus in the environment, and strengthening the ability to treat infected individuals are critical to mitigate and control the spread of COVID-19, especially in the resource-constrained regions.

Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach.

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.

Homogeneous ensemble models for predicting infection levels and mortality of COVID-19 patients: Evidence from China

Background Persistence of long-term COVID-19 pandemic is putting high pressure on healthcare services worldwide for several years. This article aims to establish models to predict infection levels and mortality of COVID-19 patients in China. Methods Machine learning models and deep learning models have been built based on the clinical features of COVID-19 patients. The best models are selected by area under the receiver operating characteristic curve (AUC) scores to construct two homogeneous ensemble models for predicting infection levels and mortality, respectively. The first-hand clinical data of 760 patients are collected from Zhongnan Hospital of Wuhan University between 3 January and 8 March 2020. We preprocess data with cleaning, imputation, and normalization. Results Our models obtain AUC = 0.7059 and Recall (Weighted avg) = 0.7248 in predicting infection level, while AUC=0.8436 and Recall (Weighted avg) = 0.8486 in predicting mortality ratio. This study also identifies two sets of essential clinical features. One is C-reactive protein (CRP) or high sensitivity C-reactive protein (hs-CRP) and the other is chest tightness, age, and pleural effusion. Conclusions Two homogeneous ensemble models are proposed to predict infection levels and mortality of COVID-19 patients in China. New findings of clinical features for benefiting the machine learning models are reported. The evaluation of an actual dataset collected from January 3 to March 8, 2020 demonstrates the effectiveness of the models by comparing them with state-of-the-art models in prediction.

Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.

Modeling the effect of Fangcang shelter hospitals on the control of COVID-19 epidemic.

The ongoing COVID-19 pandemic has posed a tremendous threat to the public and health authorities. Wuhan, as one of the cities experiencing the earliest COVID-19 outbreak, has successfully tackled the epidemic finally. The main reason is the implementing of Fangcang shelter hospitals, which rapidly and massively scale the health system's capacity to treat COVID-19 confirmed cases with mild symptoms. To give insights on what degree Fangcang shelter hospitals have contained COVID-19 in Wuhan, we proposed a piecewise smooth model regarding the patient triage scheme and the bed capacities of Fangcang shelter hospitals and designated hospitals. We used data on the cumulative number of confirmed cases, recovered cases, deaths, and data on the number of hospitalized individuals in Fangcang shelter hospitals and designated hospitals in Wuhan to parameterize the targeted model. Our results showed that diminishing the bed capacity or delaying the opening time of Fangcang shelter hospitals, both would result in worsening the epidemic by increasing the total number of infectives and hospitalized individuals and the effective reproduction number R e ( t ) . The findings demonstrated that Fangcang shelter hospitals avoided 17,013 critical infections and 17,823 total infections while it saved 7 days during the process of controlling the effective reproduction number R e ( t ) < 1 . Our study highlighted the critical role of Fangcang shelter hospitals in curbing and eventually stopping COVID-19 outbreak in Wuhan, China. These findings may provide a valuable reference for decision-makers in regarding ramping up the health system capacity to isolate groups of people with mild symptoms in areas of widespread infection.

The prevalence and influencing factors for compassion fatigue among nurses in Fangcang shelter hospitals: A cross-sectional study.

AIM: We aim to investigate the prevalence and associated factors for compassion fatigue among nurses in Fangcang Shelter Hospitals in Wuhan. Studies have shown that compassion fatigue was more common among nurses than other health-care providers, and its predictors were also different. In recent years, most studies have investigated compassion fatigue in emergency and oncology nurses, whereas there is little information on compassion fatigue among nurses from the frontline of Fangcang Shelter Hospitals during the COVID-19 pandemic. METHODS: A descriptive, cross-sectional design was used in this study. An online survey was conducted among nurses (n = 972) of five Fangcang Shelter Hospitals in Wuhan, Hubei province, China, from 6 March to 10 March 2020. A self-administered questionnaire including demographic information, work-related information, General Health Questionnaire, Perceived Stress Scale and Compassion Fatigue Scale was used. RESULTS: The prevalence of compassion fatigue among nurses in Fangcang Shelter Hospitals was moderate, and most cases were mild. There was a significant relationship between compassion fatigue and work-related factors, mental health and perceived stress among nurses working in Fangcang Shelter Hospitals. CONCLUSIONS: Various factors contribute to compassion fatigue, including lower job satisfaction and job adaptability, less praise from patients, more fear of infection and more perceived stress. A good working atmosphere, organizational support and psychological consultation are essential to alleviate nurses' compassion fatigue during the anti-epidemic period.

Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm.

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.

Predicting Progression of COVID-19 Infection to Prioritize Medical Resource Allocation: A Novel Triage Model Based on Patient Characteristics and Symptoms at Presentation.

Background: The COVID-19 global pandemic has posed unprecedented challenges to health care systems all over the world. The speed of the viral spread results in a tsunami of patients, which begs for a reliable screening tool using readily available data to predict disease progression. Methods: Multicenter retrospective cohort study was performed to develop and validate a triage model. Patient demographic and non-laboratory clinical data were recorded. Using only the data from Zhongnan Hospital, step-wise multivariable logistic regression was performed, and a prognostic nomogram was constructed based on the independent variables identifies. The discrimination and calibration of the model were validated. External independent validation was performed to further address the utility of this model using data from Jinyintan Hospital. Results: A total of 716 confirmed COVID-19 cases from Zhongnan Hospital were included for model construction. Men, increased age, fever, hypertension, cardio-cerebrovascular disease, dyspnea, cough, and myalgia are independent risk factors for disease progression. External independent validation was carried out in a cohort with 201 cases from Jinyintan Hospital. The area under the curve (AUC) was 0.787 (95% confidence interval [CI]: 0.747-0.827) in the training group and 0.704 (95% CI: 0.632-0.777) in the validation group. Conclusions: We developed a novel triage model based on basic and clinical data. Our model could be used as a pragmatic screening aid to allow for cost efficient screening to be carried out such as over the phone, which may reduce disease propagation through limiting unnecessary contact. This may help allocation of limited medical resources.

Mycobacterium vaccae Nebulization in the Treatment of COVID-19: A Randomized, Double-Blind, Placebo-Controlled Trial.

Background: Severe acute respiratory syndrome coronavirus 2 infection is associated with strong infectiousness and has no effective therapy. We aimed to explore the efficacy and safety of Mycobacterium vaccae nebulization in the treatment of Coronavirus Disease 2019 (COVID-19). Methods: In this randomized, double-blind, placebo-controlled clinical trial, we included 31 adult patients with moderate COVID-19 who were admitted to the Fourth People's Hospital of Nanning (Nanning, China) between January 22, 2020 and February 17, 2020. Patients were randomly divided into two groups: group A (standard care group) and group B (M. vaccae in combination with standard care group). The primary outcome was the time interval from admission to viral RNA negative conversion (oropharyngeal swabs were used in this study). Secondary outcomes included chest computed tomography (CT), mortality, length of hospital stay, complications during treatment, and so on. Patients were followed up to 4 weeks after discharge (reexamination of viral RNA, chest CT, etc.). Results: Nucleic acid test negative conversion time in group B was shorter than that in group A (2.9 days [2.7-8.7] vs. 6.8 days [3.3-13.8]; p = 0.045). No death and no conversion to severe or critical cases were observed in both groups. Two weeks after discharge, neither "relapse" nor "return to positive" cases were found. Four weeks after discharge, it was found that there was no case of " relapse " or "return to positive" in group B, and 1 patient in group A showed "return to positive", but there was no clinical manifestation and imaging progression. No adverse reactions related to M. vaccae were found during observation period. Conclusion:M. vaccae treatment might shorten the time interval from admission to viral RNA negative conversion, which might be beneficial to the prevention and treatment of COVID-19. Clinical Trial Registration: ChiCTR2000030016.

Prevalence and Characteristics of Hypoxic Hepatitis in COVID-19 Patients in the Intensive Care Unit: A First Retrospective Study.

Purpose: Coronavirus disease 2019 (COVID-19) has been associated with acute liver injury in reports worldwide. But no studies to date have described hypoxic hepatitis (HH) in patients with COVID-19. We aim to identify the prevalence of and possible mechanisms of HH in COVID-19 patients in the Intensive Care Unit (ICU). Methods: This retrospective study was conducted on 51 patients with confirmed SARS-CoV-2 infection in the ICU at Zhongnan Hospital of Wuhan University from December 21, 2019, to March 11, 2020. Information on clinical features of enrolled patients was collected for analysis. Results: HH was observed in 5.88% of the ICU patients with SARS-CoV-2 infection. All HH patients were progressing to respiratory failure and peak alanine aminotransferase (ALT) values were 1665, 1414, and 1140 U/L during hospitalization, respectively. All patients with HH died as a result of the deterioration of multiple organ failure (MOF). The dynamic changes of ALT, aspartate transaminase (AST), and total bilirubin (TBIL) levels were more dramatic in HH groups. Levels of TBIL, C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6(IL-6) showed statistically significant elevation in HH cases compared with that in non-HH cases (P < 0.001). Besides, the median survival time of the HH group was significantly shorter than the non-HH group (P < 0.05). Conclusions: In ICU, HH was not a rare condition in patients with severe COVID-19 and has a high mortality. The main causes of HH are respiratory and cardiac failure and may be associated with the immune-mediated inflammatory response. Clinicians should search for any underlying hemodynamic or respiratory instability even in patients with normal ALT levels on admission.

Significance of nucleic acid positive anal swab in COVID-19 patients

We compared the clinical characteristics of patients with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) positive and negative anal swabs during coronavirus disease 2019 (COVID-19) recovery and investigated the clinical significance and influence factors of anal swab detection.This study retrospectively analyzed 23 moderate COVID-19 patients in the recovery phase. They were divided into anal swab positive group (n = 13) (negative for pharyngeal swabs but positive for anal swabs) and anal swab negative group (n = 10) (negative for pharyngeal and anal swabs). The epidemiology, clinical symptoms, time of pharyngeal swabs turning negative, and laboratory results were compared.The time of pharyngeal swabs turning negative in the anal swab positive group was 6 (5–8.5) days, significantly longer than that in the anal swab negative group (1 (1–4.25) days), P = 0.0002). The platelet count of the anal swab positive group was significantly lower than that of the anal swab negative group (198 (135–235) × 109/L vs 240.5 (227–264.75) × 109/L, P = 0.0248). No significant difference was observed between the two groups in other variables.The time of pharyngeal swab turning negative in anal swab positive patients is longer than that in anal swab negative patients. The platelet count can be used as an indicator for viral infection evaluation. For patients with a longer time of pharyngeal swabs turning negative, the combined testing of the anal swab and platelet counts may help to avoid pharyngeal swab false negatives, premature discharge, and the possibility of fecal-oral transmission.

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study.

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

Analysis of inflammatory parameters and disease severity for 88 hospitalized COVID-19 patients in Wuhan, China.

Background and aim: The outbreak of coronavirus disease 2019 (COVID-19) is quickly turning into a pandemic. We aimed to further clarify the clinical characteristics and the relationship between these features and disease severity. Methods: In this retrospective single-center study, demographic, clinical and laboratory data were collected and analyzed among moderate, severe and critically ill group patients. Results: 88 hospitalization patients confirmed COVID-19 were enrolled in this study. The average age of the patients was 57.11 years (SD, ±15.39). Of these 88 patients, the median body mass index (BMI) was 24.03 (IQR, 21.64-26.61; range 15.05-32.39), the median duration from disease onset to hospital admission were 11 days (IQR, 6.50-14.50). 46.59% patients had one or more comorbidities, with hypertension being the most common (26.14%), followed by diabetes mellitus (12.50%) and coronary atherosclerotic heart disease (CAD) (7.95%). Common symptoms at onset of disease were fever (71.59%), cough (59.09%), dyspnea (38.64%) and fatigue (29.55%). 88 patients were divided into moderate (47 [53.41%]), severe (32 [36.36%]) and critically ill (9 [10.23%]) groups. Compared with severe and moderate patients, lymphocytopenia occurred in 85.71% critically ill patients, and serum IL-2R, IL-6, IL-8, TNF-α, LDH, and cTnI were also increased in 71.42%, 83.33%, 57.14%, 71.43%, 100% and 42.86% in critically ill patients. Through our analysis, the age, comorbidities, lymphocyte count, eosinophil count, ferritin, CRP, LDH, PT and inflammatory cytokines were statistically significant along with the disease severity. Conclusion: We found some clinical characteristic and inflammatory cytokines could reveal the severity of COVID-19 during the outbreak phage. Our research could assist the clinicians recognize severe and critically ill patients timely and focus on the expectant treatment for each patient.

The association between markers of liver injury and clinical outcomes in patients with COVID-19 in Wuhan.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) is a critical challenge for public health. The effect of COVID-19 on liver injury has not been fully established. AIMS: To evaluate the dynamic changes in liver function and the relationship between liver damage and prognosis in patients with COVID-19. METHODS: Retrospective analysis of clinical data of 675 patients with COVID-19 in Zhongnan Hospital of Wuhan University from January 3 to March 8, 2020. Patients were classified as having normal or abnormal liver function and liver injury. RESULTS: Of 675 patients, 253 (37.5%) had abnormal liver function during hospitalisation, and 52 (7.7%) had liver injury. The dynamic changes of ALT and AST levels were more significant in patients with liver injury and in those who died. AST >3-fold upper limit of normal (ULN) had the highest risk of death and mechanical ventilation. Compared to patients with normal AST levels, mortality and risk of mechanical ventilation significantly increased 19.27-fold (95% confidence interval [CI], 4.89-75.97; P < 0.0001) and 116.72-fold (95% CI, 31.58-431.46; P < 0.0001), respectively, in patients with AST above 3-fold ULN. Increased leucocytes, decreased lymphocytes and female sex were independently associated with liver injury. CONCLUSIONS: The dynamic changes in liver function may have a significant correlation with the severity and prognosis of COVID-19. Increased index of liver injury was closely related to mortality and need for mechanical ventilation. Therefore, these indicators should be closely monitored during hospitalisation.

Clinical and CT features of mild-to-moderate COVID-19 cases after two sequential negative nucleic acid testing results: a retrospective analysis (preprint)

Background Numerous studies have been focused on the clinical and imaging features among COVID-19 patients positive for SARS-CoV-2, but the data after negative is limited. We aims to describe these features after negative respiratory nucleic acid testing results.Methods From January 31 to February 28, 2020, 51 mild-to-moderate COVID-19 cases (median: 34.0 years and 47.1% male) were retrospectively enrolled. Demographic, clinical, laboratory, and CT imaging data were collected before and after two sequential negative results for respiratory SARS-CoV-2 .Results After negative for respiratory SARS-CoV-2, the clinical symptoms continued to recover and the abnormal imaging were observed for all of the moderate cases. 77.4% of the moderate patients had multi-lobar involvement and lesions were more frequent in the lower lobes. The most common CT imaging manifestations were ground glass opacity (51.6%) and fibrous stripes (54.8%%). Among 12 out of 31 moderate patients with repeated chest CT scan after negative for SARS-CoV-2, 7 patients (58.3%) with ground-glass absorption reduced over 60% within one week, but there were still 4 cases (13.8%) with absorption less than 5%.Conclusions The clinical symptoms and abnormal imaging persisted but continued to recover after negative for respiratory SARS-CoV-2.

The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak - an update on the status.

An acute respiratory disease, caused by a novel coronavirus (SARS-CoV-2, previously known as 2019-nCoV), the coronavirus disease 2019 (COVID-19) has spread throughout China and received worldwide attention. On 30 January 2020, World Health Organization (WHO) officially declared the COVID-19 epidemic as a public health emergency of international concern. The emergence of SARS-CoV-2, since the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002 and Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012, marked the third introduction of a highly pathogenic and large-scale epidemic coronavirus into the human population in the twenty-first century. As of 1 March 2020, a total of 87,137 confirmed cases globally, 79,968 confirmed in China and 7169 outside of China, with 2977 deaths (3.4%) had been reported by WHO. Meanwhile, several independent research groups have identified that SARS-CoV-2 belongs to ß-coronavirus, with highly identical genome to bat coronavirus, pointing to bat as the natural host. The novel coronavirus uses the same receptor, angiotensin-converting enzyme 2 (ACE2) as that for SARS-CoV, and mainly spreads through the respiratory tract. Importantly, increasingly evidence showed sustained human-to-human transmission, along with many exported cases across the globe. The clinical symptoms of COVID-19 patients include fever, cough, fatigue and a small population of patients appeared gastrointestinal infection symptoms. The elderly and people with underlying diseases are susceptible to infection and prone to serious outcomes, which may be associated with acute respiratory distress syndrome (ARDS) and cytokine storm. Currently, there are few specific antiviral strategies, but several potent candidates of antivirals and repurposed drugs are under urgent investigation. In this review, we summarized the latest research progress of the epidemiology, pathogenesis, and clinical characteristics of COVID-19, and discussed the current treatment and scientific advancements to combat the epidemic novel coronavirus.