Simple Symptom-Based Prediction of COVID-19: A Single-Center Study of Outpatient Fever Clinic in Japan.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) symptoms are not fully understood in non-hospitalized individuals in Japan, and COVID-19 differentiation by symptoms alone remained challenging. Therefore, this study aimed to examine COVID-19 prediction from symptoms using real-world data in an outpatient fever clinic. METHODS: We compared the symptoms of COVID-19-positive and negative patients who visited the outpatient fever clinic at Imabari City Medical Association General Hospital and tested for COVID-19 from April 2021 to May 2022. This retrospective single-center study enrolled 2,693 consecutive patients. RESULTS: COVID-19-positive patients had a higher frequency of close contact with COVID-19-infected patients compared with COVID-19-negative patients. Moreover, patients with COVID-19 had high-grade fever at the clinic compared with patients without COVID-19. Additionally, the most common symptom in patients with COVID-19 was sore throat (67.3%), followed by cough (62.0%), which was approximately twice as common in patients without COVID-19. COVID-19 was more frequently identified in patients having a fever (≥37.5℃) with a sore throat, a cough, or both. The positive COVID-19 rate reached approximately half (45%) when three symptoms were present. CONCLUSION: These results suggested that COVID-19 prediction by combinations of simple symptoms and close contact with COVID-19-infected patients might be useful and lead to recommendations for testing of COVID-19 in symptomatic individuals.

Deep convolutional neural networks for detection of abnormalities in chest X-rays trained on the very large dataset.

One of the main challenges in the current pandemic is the detection of coronavirus. Conventional techniques (PT-PCR) have their limitations such as long response time and limited accessibility. On the other hand, X-ray machines are widely available and they are already digitized in the health systems. Thus, their usage is faster and more available. Therefore, in this research, we evaluate how well deep CNNs do when it comes to classifying normal versus pathological chest X-rays. Compared to the previous research, we trained our network on the largest number of images, 103,468 in total, including 5 classes such as COPD signs, COVID, normal, others and Pneumonia. We achieved COVID accuracy of 97% and overall accuracy of 81%. Additionally, we achieved classification accuracy of 84% for categorization into normal (78%) and abnormal (88%).

Virology, clinical features and diagnosis of covid 19: Review analysis

COVID-19 requires unprecedented mobilization of the health systems to prevent the rapid spread of this unique virus, which spreads via respiratory droplet and causes respiratory disease. There is an urgent need for an accurate and rapid test method to quickly identify many infected patients and asymptomatic carriers to prevent virus transmission and assure timely treatment of the patients. This article aims as an outcome of review of the evidence on viral load and its virulence of SARS-CoV2,so that it will help in further understanding the fact useful for investigating and managing the COVID-19 cases. A search of available evidence was conducted in pub-med "COVID-19 viral load and virulence" and its associated characters world-wide and Google Scholar to capture the most recently published articles. The WHO and Centre for Disease Control and Prevention (CDC) database of publications on novel coronavirus were also screened for relevant publications. s of 55 articles were screened by two authors and 15 were included in this study based on the inclusion criteria. SARS-coV2, the causative agent of COVID-19 falls under the coronavirus family but it has higher infectivity compared to SARS and MERS with higher reproduction numbers(Ro). Virulence has been found to be different throughout the world,however lower compared to SARS and MERS,till date. The most common clinical features have been found to be cough and fever. RT - PCR remains the most sensitive and specific method for the diagnosis of COVID-19 although it is time consuming, costly and requires highly skilled human resources. Hence, newer modalities like RT-LAMP can be alternative for point of care diagnosis as this is both cost effective and requires less skilled human resources. Despite recent advances in disease diagnosis and treatment outcomes using latest technological advances in molecular biology, the global pandemic COVID-19 remains a major headache for governments across the world due to limited testing capacity and lack of appropriate treatment and vaccine. Copyright © 2020, Kathmandu University. All rights reserved.

Present therapeutic and diagnostic approaches for SARS-CoV-2 infection

The novel Coronavirus (nCoV), severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2), has shaken the whole world and posed significant challenges to the global healthcare system for more than a year. The scientific community across the globe is trying to combat this virus by developing a safe vaccine that can provide long-term immunity against the virus. The other means of overcoming its pathogenicity is to treat the infected people with available drugs and/or novel therapeutic strategies. The available drugs were previously designed to combat viral infections and come with tested safety. This appears to be the most practical approach as a quick response to the highly infectious pandemic with high morbidity and mortality. Although many repurposed drugs like favipiravir and hydroxychloroquine have been tried, they have been proven toxic and/or less efficacious. This has led the world to find urgent therapeutic interventions (traditional and novel), to help decrease the severity of COVID-19 infection and aim towards recovery. This chapter of the book will discuss the most up-to-date published data with respect to prevention and treatment of COVID-19 infection. Diagnosis also plays an important part in controlling the pandemic caused by the virus. A cheap, accurate and fast identification test for the virus is the need of the hour. This chapter will also throw light on the various diagnostic procedures available for the identification of SARS-CoV-2, till date, along with their advantages and disadvantages. © 2022 Elsevier Inc. All rights reserved.

Diagnosis of COVID-19 Pneumonia from Chest X-ray Scans using Modified MobileNet Architecture

COVID-19 disease has been laid out across the world recently as a global pandemic. Generally, rapid antigen tests have been performed to detect this dangerous disease at an early stage. Due to the increased number of false classification rate caused by rapid antigen tests, real time reverse transcription polymerase chain reaction (rRT-PCR) tests have been used as a conventional pathogenic testing tool. However, the efficacy of rRT-PCR tests have been affected by the several mutations in SARS-CoV-2 virus. Therefore, in this paper, a modified MobileNet-based intelligent methodology using chest X-ray (CXR) scans has been put forward to diagnose the COVID-19 disease precisely and early. The propounded method has been applied on benchmark chest X-ray dataset exploratory results establish the usefulness of the propounded approach. © 2021 IEEE.

Development and characterization of a quantitative ELISA to detect anti-SARS-CoV-2 spike antibodies.

A novel clinical assay for the detection and quantitation of antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was adapted from an in-house, research-based enzyme-linked immunosorbent assay (ELISA). Development and validation were performed under regulatory guidelines, and the test obtained emergency use authorization (EUA) from the New York State Department of Health (NYSDOH) and the Food and Drug Administration (FDA). The Mount Sinai coronavirus disease 2019 (COVID-19) antibody assay is an orthogonal, quantitative direct ELISA test which detects antibodies reactive to the receptor binding domain (RBD) and the spike protein of the novel SARS-CoV-2. The assay is performed on 96-well plates coated with either SARS-CoV-2 recombinant RBD or spike proteins. The test is divided into two stages, a qualitative screening assay against RBD and a quantitative assay against the full-length spike protein. The test uses pooled high titer serum as a reference standard. Negative pre-COVID-19 and positive post-COVID-19, PCR-confirmed specimens were incorporated in each ELISA test run, and the assays were performed independently at two different locations. The Mount Sinai COVID-19 serology performed with high sensitivity and specificity, 92.5% (95% CI: 0.785-0.980) and 100% (CI: 0.939-1.000) respectively. Between-run precision was assessed with a single run repeated over 22 days; and within-run precision was assessed with 10 replicates per day over 22 days. Both were within reported acceptance criteria (CV ≤ 20%). This population-based study reveals the applicability and reliability of this novel orthogonal COVID-19 serology test for the detection and quantitation of antibodies against SARS-CoV-2, allowing a broad set of clinical applications, including the broad evaluation of SARS-CoV-2 seroprevalence and antibody profiling in different population subsets.

Thrombosis of the Portal Vein and Superior Mesenteric Vein in a Patient With Subclinical COVID-19 Infection.

More than 122 million cases of COVID-19 infection have been documented, and hundreds of thousands are being added every day. Several co-morbidities are associated with COVID-19, among which hypercoagulability has garnered the attention of many doctors and researchers. Most cases of vascular thrombosis are noted in intensive care unit (ICU) patients with serious disease; among these, many cases of deep venous thrombosis and pulmonary embolism have been noted. A few cases of portal vein thrombosis have also been documented in ICU patients with severe COVID-19. Here, we present a case of a portal vein and superior mesenteric vein thrombosis in a patient with subclinical COVID-19 infection. Through this case report, we intend to increase the research horizon and wish to help diagnose co-morbidities associated with COVID-19 at an earlier stage.

Landscape of humoral immune responses against SARS-CoV-2 in patients with COVID-19 disease and the value of antibody testing.

A new pandemic is ongoing in several parts of the world. The agent responsible is the newly emerged severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The symptoms associated with this virus are known as the coronavirus disease-2019 (COVID-19). In this review, we summarize the published data on virus specific antibodies in hospitalized patients with COVID-19 disease, patients recovered from the disease and the individuals who are asymptomatic with SARS-CoV-2 infections. The review highlights the following: i) an adjunct role of antibody tests in the diagnosis of COVID-19 in combination with RT-PCR; ii) status of antibodies from COVID-19 convalescent patients to select donors for plasma therapy; iii) the potential confounding effects of other coronaviruses, measles, mumps and rubella in antibody testing due to homology of certain viral genes; and iv) the role of antibody testing for conducting surveillance in populations, incidence estimation, contact tracing and epidemiologic studies.

Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning.

Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is much more challenging to discriminate COVID-19 from typical viral pneumonia as COVID-19 is also a kind of virus. In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, precision of 0.95, and F1-score of 0.94. The model is available in https://github.com/Zhengfudan/COVID-19-Diagnosis-and-Pneumonia-Classification .

The AndroCoV Clinical Scoring for COVID-19 Diagnosis: A Prompt, Feasible, Costless, and Highly Sensitive Diagnostic Tool for COVID-19 Based on a 1757-Patient Cohort.

Introduction A major barrier for successful therapeutic approaches for COVID-19 is the inability to diagnose COVID-19 during the viral replication stage, when drugs with potential antiviral activity could demonstrate efficacy and preclude progression to more severe stages. Reasons that hamper an earlier diagnosis of COVID-19 include the unspecific and mild symptoms during the first stage, the delay in the diagnosis and specific management caused by the requirement of a real-time reverse transcriptase-polymerase chain reaction (RT-PCR) for SARS-CoV-2 for the diagnosis of COVID-19, and the insufficient sensitivity of the RT-PCR-SARS-CoV-2, converse to what is recommended for a screening test during an outbreak. More sensitive and earlier diagnostic tools for COVID-19 should be unraveled as a key strategy for a breakthrough change in the disease course and response to specific therapies, particularly those that target the blockage of viral shedding. We aimed to create an accurate, sensitive, easy-to-perform, and intuitive clinical scoring for the diagnosis of COVID-19 without the need for an RT-PCR-SARS-CoV-2 (termed The AndroCoV Clinical Scoring for COVID-19 Diagnosis), resulting from a 1,757 population cohort, to eventually encourage the management of patients with a high pre-clinical likelihood of presenting COVID-19, independent of an RT-PCR-SARS-COV-2 test, to avoid delays and loss of appropriate timing for potential therapies. Methods This is a post-hoc analysis of clinical data prospectively collected of the Pre-AndroCoV and AndroCov Trials, which resulted in scorings for the clinical diagnosis of COVID-19 based on the likelihood of presenting with actual COVID-19 according to the number of symptoms, presence of anosmia, and known positive household contact. Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and accuracy were calculated for subjects screened in two different periods and both periods together, for females, males, and both, in a total of nine different scenarios, according to combinations of one, two, or three or more symptoms or the presence of anosmia in subjects without known positive household contacts, and no symptoms, one, two, or three or more symptoms, or presence of anosmia or ageusia in subjects with known positive household contacts. Scorings that yielded the highest pre-test probability, sensitivity, and accuracy were selected. Results Of the 1,757 patients screened, 1,284 were diagnosed with COVID-19. The scoring that required: (1) two or more symptoms, or anosmia or ageusia alone, for subjects without known contact; or (2) one or more symptoms, including anosmia or ageusia alone, when with known positive contacts presented the highest accuracy (80.4%) among all combinations attempted, and higher sensitivity (85.7%) than RT-PCR-SARS-CoV-2 commercially available kit tests. Conclusion The AndroCoV clinical scoring for COVID-19 diagnosis was demonstrated to be a feasible, easy, costless, and sensitive diagnostic tool for the clinical diagnosis of COVID-19. Because the clinical diagnosis of COVID-19 avoids delays in specific treatments, particularly for high-risk populations, prevents false-negative diagnosis, and reduces diagnostic costs, this diagnostic tool should be considered as an option for COVID-19 diagnosis, at least while SARS-CoV-2 is the prevailing circulating virus and vaccination rate is below the required for herd immunity.

COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images.

The Coronavirus Disease 2019 (COVID-19) outbreak has a tremendous impact on global health and the daily life of people still living in more than two hundred countries. The crucial action to gain the force in the fight of COVID-19 is to have powerful monitoring of the site forming infected patients. Most of the initial tests rely on detecting the genetic material of the coronavirus, and they have a poor detection rate with the time-consuming operation. In the ongoing process, radiological imaging is also preferred where chest X-rays are highlighted in the diagnosis. Early studies express the patients with an abnormality in chest X-rays pointing to the presence of the COVID-19. On this motivation, there are several studies cover the deep learning-based solutions to detect the COVID-19 using chest X-rays. A part of the existing studies use non-public datasets, others perform on complicated Artificial Intelligent (AI) structures. In our study, we demonstrate an AI-based structure to outperform the existing studies. The SqueezeNet that comes forward with its light network design is tuned for the COVID-19 diagnosis with Bayesian optimization additive. Fine-tuned hyperparameters and augmented dataset make the proposed network perform much better than existing network designs and to obtain a higher COVID-19 diagnosis accuracy.