Importance The coronavirus disease 2019 (COVID-19) pandemic, due to the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide sudden and substantial increase in hospitalizations for pneumonia with multiorgan disease. This review discusses current evidence regarding the pathophysiology, transmission, diagnosis, and management of COVID-19. Observations SARS-CoV-2 is spread primarily via respiratory droplets during close face-to-face contact. Infection can be spread by asymptomatic, presymptomatic, and symptomatic carriers. The average time from exposure to symptom onset is 5 days, and 97.5% of people who develop symptoms do so within 11.5 days. The most common symptoms are fever, dry cough, and shortness of breath. Radiographic and laboratory abnormalities, such as lymphopenia and elevated lactate dehydrogenase, are common, but nonspecific. Diagnosis is made by detection of SARS-CoV-2 via reverse transcription polymerase chain reaction testing, although false-negative test results may occur in up to 20% to 67% of patients; however, this is dependent on the quality and timing of testing. Manifestations of COVID-19 include asymptomatic carriers and fulminant disease characterized by sepsis and acute respiratory failure. Approximately 5% of patients with COVID-19, and 20% of those hospitalized, experience severe symptoms necessitating intensive care. More than 75% of patients hospitalized with COVID-19 require supplemental oxygen. Treatment for individuals with COVID-19 includes best practices for supportive management of acute hypoxic respiratory failure. Emerging data indicate that dexamethasone therapy reduces 28-day mortality in patients requiring supplemental oxygen compared with usual care (21.6% vs 24.6%; age-adjusted rate ratio, 0.83 [95% CI, 0.74-0.92]) and that remdesivir improves time to recovery (hospital discharge or no supplemental oxygen requirement) from 15 to 11 days. In a randomized trial of 103 patients with COVID-19, convalescent plasma did not shorten time to recovery. Ongoing trials are testing antiviral therapies, immune modulators, and anticoagulants. The case-fatality rate for COVID-19 varies markedly by age, ranging from 0.3 deaths per 1000 cases among patients aged 5 to 17 years to 304.9 deaths per 1000 cases among patients aged 85 years or older in the US. Among patients hospitalized in the intensive care unit, the case fatality is up to 40%. At least 120 SARS-CoV-2 vaccines are under development. Until an effective vaccine is available, the primary methods to reduce spread are face masks, social distancing, and contact tracing. Monoclonal antibodies and hyperimmune globulin may provide additional preventive strategies. Conclusions and Relevance As of July 1, 2020, more than 10 million people worldwide had been infected with SARS-CoV-2. Many aspects of transmission, infection, and treatment remain unclear. Advances in prevention and effective management of COVID-19 will require basic and clinical investigation and public health and clinical interventions.

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Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review.

The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.

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COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.

Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

https://www.bmj.com/content/bmj/369/bmj.m1328.full.pdf

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Automated detection of COVID-19 cases using deep neural networks with X-ray images.

COVID-19 has spread globally since its discovery in Hubei province, China in December 2019. A combination of computed tomography imaging, whole genome sequencing, and electron microscopy were initially used to screen and identify SARS-CoV-2, the viral etiology of COVID-19. The aim of this review article is to inform the audience of diagnostic and surveillance technologies for SARS-CoV-2 and their performance characteristics. We describe point-of-care diagnostics that are on the horizon and encourage academics to advance their technologies beyond conception. Developing plug-and-play diagnostics to manage the SARS-CoV-2 outbreak would be useful in preventing future epidemics.

Diagnosing COVID-19: The Disease and Tools for Detection

Dear Editors, Neuromyelitis optica spectrum disorder (NMOSD), preferentially affecting the optic nerves and spinal cord, is a representative inflammatory demyelinating disease of the central nervous system (CNS). The presence of antibodies targeting the water channel aquaporin 4 (AQP4) can be demonstrated in the serum of most patients.1 The exact etiology and pathogenesis of NMOSD have not been completely identified, and it is most commonly considered an idiopathic autoimmune condition, only a few cases have revealed that it can coexist with underlying malignancies and even emerge as a phenotype of paraneoplastic neurological syndrome (PNS).2,3 Herein, we describe a 46yearold woman who developed longitudinal extensive transverse myelitis (LETM) seropositivity for AQP4 antibody and a pathologically confirmed cholangiocarcinoma, which has not been reported before. A 46yearold woman complained of acute progressive bilateral lower extremity numbness and weakness, along with dysuria and constipation. Neurological examination revealed paraplegia (Medical Research Council [MRC] grade 0 in both legs) and remarkable sensory dysfunction below the nipple plane. A positive Babinski sign was observed on the left side. Extensive laboratory workup was unrevealing, including tumor markers, pathogenic, inflammatory, and immunological assessments. Spinal magnetic resonance imaging (MRI) showed longitudinally extensive T2 hyperintense lesions involving the cervical and thoracic cord without enhancement (Figure 1). Chest computerized tomography and brain MRI were unremarkable. At the same time, abdominal ultrasound detected a mass in the left lateral segment of the liver. Cholangiocarcinoma was highly suspected according to the contrastenhanced MRI (Figure 1). Cerebrospinal fluid analysis revealed mild pleocytosis (8/μl) and a slightly elevated protein level (49.4 mg/dl). The oligoclonal band was negative. Further investigation demonstrated positive antibodies against AQP4 in serum (titer, 1:10) using a cellbased assay. The diagnosis of NMOSD was made. The patient was treated with intravenous methylprednisolone (1000 mg/day for 3 days, followed by a tapering scheme) and plasma exchange (3 cycles, every other day). Longterm immunosuppressive therapy was suggested, but the patient refused. The patient was then referred for surgery. The hepatic mass was removed, and postoperative pathological examination indicated a poorly differentiated intrahepatic cholangiocarcinoma. Immunohistochemical staining of the neoplastic tissue samples was performed, demonstrating the absence of AQP4 expression on the surface of tumor cells (Figure 1). Unfortunately, no substantial improvement was observed after immunotherapy and oncotherapy (MRC grade 0 in both legs). Eighteen months later, at the latest visit, partial recovery of neurological disability was achieved (MRC grade 2 in both legs), and no neurological relapse and tumor recurrence were reported. The patient declined to repeat the AQP4 antibody test and spinal MRI during the followup. The LETM with seropositive AQP4 antibodies in this patient made the definite diagnosis of NMOSD based on the 2015 diagnostic criteria.1 Interestingly, asymptomatic cancer was detected through systemic screening. It was widely accepted that NMOSD often coexisted with systemic autoimmune diseases (e.g., Sjögren syndrome) with a strong association between them.1 However, accompanied by tumors in patients with NMOSD occur occasionally. In previous studies, the frequency of underlying neoplasms in NMOSD ranged from 3.2% to 7.1%, and the most commonly reported types were breast, lung, and genitourinary (mostly ovary) tumors.4 To the best of our knowledge, this is the first case report of AQP4 antibodypositive NMOSD with concomitant cholangiocarcinoma. The correlation between NMOSD and concurrent tumors remains uncertain. AQP4 is highly expressed in some tumors and may have a role in its migration. AQP4 antibodies are produced by the immune system to prevent tumors from spreading.5 However, in some cases, AQP4 antibodies may attack CNS by mistake, and patients will manifest as NMOSD. Positive AQP4 staining of tumor cells may mean that the tumors are the major cause of NMOSD in these patients.6 In other words, AQP4 antibody may be a type of paraneoplastic autoantibody, while NMOSD can be considered one of the phenotypes of PNS. On the other hand, negative AQP4 staining of tumor cells may mean that neoplasms are only coincidental findings independent of NMOSD, as in our patient. Moreover, a limited number of PNS with cholangiocarcinoma cases also support this incidental finding.7

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/cns.14042

Aquaporin 4 antibody‐positive neuromyelitis optica spectrum disorder with cholangiocarcinoma: Casual or causal association?

Background Whether viral hepatitis increases the risk of cholangiocarcinoma (CCA) has been controversial. The reasons for the differences between previous research results may be related to the differences in sample size, region, living environment and course of disease. A meta-analysis is needed to clarify the correlation between them and select the key population for early screening of CCA. Meta-analysis was used to explore the relationship between viral hepatitis and the risk of CCA, so as to provide evidence for the prevention and treatment of CCA. Methods We systematically searched EmBase, SinoMed, PubMed, Web of Science China National Knowledge Infrastructure, and Wanfang databases. The quality of the included literature was evaluated using the Newcastle Ottawa Scale. Before merging the effect quantities, the data was first subjected to heterogeneity testing. Heterogeneity testing was evaluated using I2 (the proportion of heterogeneity variation to overall variation). Subgroup analysis was used to identify sources of heterogeneity in this study. The effect odds ratio (OR) of various studies was extracted or calculated for consolidation. Beta’s rank correlation, Egger’s Law of Return and funnel plot were used to test publication bias. Conduct subgroup analysis based on the regions included in the literature. Results A total of 2,113 articles were retrieved, and a total of 38 articles were included in the meta-analysis. There are 29 case-control studies and 9 Cohort study, including 333,836 cases and 4,042,509 controls. The combined risk estimate of all studies showed a statistically significant increased risk of CCA, extrahepatitis and intrahepatitis incidence with hepatitis B virus (HBV) infection (OR =1.75, OR =1.49, and OR =2.46, respectively). The combined risk estimate of all studies showed a statistically significant increased risk of CCA, extrahepatitis and intrahepatitis incidence with hepatitis C virus (HCV) infection (OR =1.45, OR =2.00, and OR =2.81, respectively). The research points of HCV and CCA were asymmetric, indicating that there may be publication bias in the study of HCV and CCA. Conclusions HBV and HCV infection could increase the risk of CCA. Therefore, in clinical practice, attention should be paid to CCA screening and early prevention of HBV and HCV infected patients.

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Viral hepatitis increases the risk of cholangiocarcinoma: a systematic review and meta-analysis

It has been generally reported that elderly individuals, especially those with underlying disease, are most vulnerable to SARS-CoV-2 infection. After being infected with SARS-CoV-2, elderly patients, typically aged over 65, are more prone to severe symptoms and conditions, requiring care at the intensive care unit (ICU) [1, 2]. The elderly COVID-19 patients usually have poor outcomes with higher mortality than other age groups. A recent report from studying 339 elderly COVID-19 patients found that more than 70% of the enrolled patients were in severe or critical conditions, while 42.8% developed complication with bacterial infection. Acute respiratory distress syndrome (ARDS) was reported in 21.0% of these patients. The mortality rate was 19.2% [3]. Another retrospective study on 113 deceased patients found that more than 83% of them were aged 60 or older [4]. This may be related to the weakened immune system and respiratory function of the elderly. Accordingly, the CT imaging manifestation of elderly patients appears severer and more progressive than that of young and middle-aged patients. The inflammation is mostly subpleural and often involving bilateral lungs, mainly presenting as ground-glass opacities (GGO) and consolidations in pulmonary CT. As the disease is rapidly progressive, diffuse bilateral lesions could develop in 4–7 days [5].

Imaging Features of COVID-19 in Elder People

The novel coronavirus that caused pandemic in 2020 started from a number of cases of unexplained pneumonia in the city of Wuhan, Hubei Province, China, in December of 2019. Unlike the other human coronavirus previously reported, this new strain of coronavirus is much more contagious and rapidly spread in the city of Wuhan and subsequently various regions of China from the epicenter of Wuhan. Within several weeks, many countries of Asia, Europe, North America, and Oceania reported the confirmed cases with the worldwide total number quickly rising to over 5.35 million and more than 343,000 deaths on May 24, 2020. The World Health Organization (WHO) initially named this novel coronavirus as “2019 Novel Coronavirus” (2019-nCoV) on January 12, 2020. National Health Commission of China named the pneumonia caused by 2019-nCoV as Novel Coronavirus Pneumonia (NCP) on January 20, 2020. In the meantime, National Health Commission of China issued an announcement to include this disease in the category B infectious diseases as stipulated in the Law of the People’s Republic of China on the Prevention and Treatment of Infectious Diseases, but to manage this disease according to Class A infectious diseases. On February 11, 2020, Tedros Adhanom Ghebreyesus, director general of WHO, announced the revised name of the pneumonia caused by novel coronavirus as “Coronavirus Disease 2019” (COVID-19). At the same time, the International Committee for Virus Classification named the novel coronavirus “Severe Acute Respiratory Syndrome Coronavirus 2” (SARS-CoV-2). Given the high incidence of SARS-CoV-2 infection and the rapid increase of the cases globally, WHO officially declared on March 11, 2020 in Geneva that the COVID-19 caused by SARS-CoV-2 has the characteristics of a global pandemic. At present, the origin of SARS-CoV-2 is still under investigation.

Bin Lin

Papers

Aquaporin 4 antibody‐positive neuromyelitis optica spectrum disorder with cholangiocarcinoma: Casual or causal association?

Viral hepatitis increases the risk of cholangiocarcinoma: a systematic review and meta-analysis

Imaging Features of COVID-19 in Elder People

Overview of the COVID-19