Infectious Mononucleosis Presenting with Loss of Taste and Smell During the SARS-CoV-2 Pandemic?

A 53-year-old woman presented during the SARS-CoV-2 pandemic with an 18-day history of pyrexia, myalgia, progressive dyspnoea and loss of taste and smell after a close contact had tested positive for SARS-CoV-2. In this period two swabs had been negative for SARS-CoV-2. Clinical examination was normal. During this admission a third SARS-CoV-2 swab was negative, and investigations showed mildly elevated inflammatory markers, mildly deranged liver function, atypical lymphocytes on a blood film and a normal chest x-ray. Her Epstein-Barr virus serology was positive and thus the diagnosis was infectious mononucleosis. LEARNING POINTS: SARS-CoV-2 is not the only virus to cause loss of taste/smell and so other differential diagnoses should be considered.Loss of taste/smell is a subjective symptom, and therefore caution should be exercised in the context of an upper respiratory tract infection.

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.

Serial population-based serosurveys for COVID-19 in two neighbourhoods of Karachi, Pakistan.

OBJECTIVE: To determine population-based estimates of coronavirus disease 2019 (COVID-19) in a densely populated urban community of Karachi, Pakistan. METHODS: Three cross-sectional surveys were conducted in April, June and August 2020 in low- and high-transmission neighbourhoods. Participants were selected at random to provide blood for Elecsys immunoassay for detection of anti-severe acute respiratory syndrome coronavirus-2 antibodies. A Bayesian regression model was used to estimate seroprevalence after adjusting for the demographic characteristics of each district. RESULTS: In total, 3005 participants from 623 households were enrolled in this study. In Phase 2, adjusted seroprevalence was estimated as 8.7% [95% confidence interval (CI) 5.1-13.1] and 15.1% (95% CI 9.4-21.7) in low- and high-transmission areas, respectively, compared with 0.2% (95% CI 0-0.7) and 0.4% (95% CI 0-1.3) in Phase 1. In Phase 3, it was 12.8% (95% CI 8.3-17.7) and 21.5% (95% CI 15.6-28) in low- and high-transmission areas, respectively. The conditional risk of infection was 0.31 (95% CI 0.16-0.47) and 0.41 (95% CI 0.28-0.52) in low- and high-transmission neighbourhoods, respectively, in Phase 2. Similar trends were observed in Phase 3. Only 5.4% of participants who tested positive for COVID-19 were symptomatic. The infection fatality rate was 1.66%, 0.37% and 0.26% in Phases 1, 2 and 3, respectively. CONCLUSION: Continuing rounds of seroprevalence studies will help to improve understanding of secular trends and the extent of infection during the course of the pandemic.

Neutropenic Enterocolitis and Rapid Spontaneous Resolution of Portal Venous Gas: A Non-Respiratory Manifestation of COVID-19.

The COVID-19 pandemic is affecting millions across the globe. The population of immunosuppressed individuals are at greatest risk of morbidity and mortality. Data on COVID-19 induced illness in the immunocompromised host are sparse. We aim to highlight the possibility of atypical and non-respiratory presentations of COVID-19 (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2) in immunosuppressed individuals as our case reveals a rare COVID-19 associated GI presentation of neutropenic enterocolitis with bloody diarrhea.

Atypical Manifestation of COVID-19-Induced Myocarditis.

We present a case of a 39-year-old male who presented with chest pain without fever or respiratory symptoms. Troponins were elevated and electrocardiogram (ECG) was inconclusive for ST-elevation myocardial infarction (STEMI). Angiography revealed normal coronaries and the patient was found to be coronavirus disease 2019 (COVID-19) positive; he was diagnosed with COVID-19 myocarditis. With the global pandemic, more cases are emerging regarding myocardial injury induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although COVID-19 manifests primarily as respiratory disease, few cases of cardiac injury without respiratory involvement or febrile illness have been reported. This case illustrates that COVID-19 can present atypically and affect an isolated non-respiratory organ system.