Pulmonary thromboembolism associated with coronavirus infection: A possible correlation-case series

We present a case series of patients with pulmonary embolism of unknown etiology who did not have any risk factors. According to the findings, the most likely cause of the pulmonary embolism was undiagnosed, asymptomatic, or mild Corona Virus disease-2019 (COVID-19) infections in the recent past. In the current post-pandemic era, where there has been a surge of sudden unexplained deaths and pulmonary embolism cases, this case series emphasizes the importance of pulmonary embolism evaluation in patients seeking medical care for dyspnea. Physicians should be aware of the possibility of pulmonary embolism as a late complication in patients with mild, asymptomatic, or undiagnosed COVID-19 infection. Copyright © the Author(s), 2022.

Predictive accuracy of neutrophil-to-lymphocyte ratio on severity and outcomes in COVID-19 patients: A retrospective study

Background/Aims: Neutrophil-to-lymphocyte ratio (NLR) is a proven marker in coronavirus disease 2019 (COVID-19) severity and mortality. However, the utility of a sequential NLR 2 (on day 5) in comparison to baseline NLR in predicting clinical outcomes and severity remains largely unexplored. Methods: This was a hospital-based retrospective observational study. Results: Higher mortality (19.9% vs. 48%) and a more severe disease (14.8% vs. 21%) were observed with elevated NLR 1 and NLR 2, respectively. NLR 2 at a cutoff of 9.88 was a better predictor of mortality, when compared to NLR 1 at 5.67, and NLR 2 has a strong correlation with mortality rates in COVID-19. Conclusion: Our study demonstrated that NLR 1 and NLR 2 were more reliable predictors of mortality than disease severity;in comparison, NLR 2 is a more accurate predictor of mortality than NLR 1. The study unravels the potential role of a sequential NLR 2, to have a better correlation in predicting the clinical severity and outcomes. The potential role of NLR 2 in assessing the interim progression of the disease and thereby initiating specific interventions at critical junctures to influence the outcome is unveiled and merits exploration in detail by larger studies. © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Admission serological markers can be used to predict significant cardiac involvement in children with paediatric multisystem inflammatory syndrome

Introduction: Paediatric multisystem inflammatory syndrome (PIMS) began to present in April 2020 midway through the covid-19 pandemic. Occurring 2-4 weeks after initial covid-19 infection, patients presented with persistent fever, evidence of inflammation and single or multiorgan dysfunction1. The Yorkshire and Humber congenital heart disease network is made up of the Leeds congenital heart unit and 18 peripheral hospitals2.With limited local paediatric cardiology availability, the vast majority of children presenting with PIMS required transfer to Leeds. This presentation aims to describe the cohort of children that were seen within the network as well as to identify any markers of significant cardiac involvement which could beusedto reduce the frequency of unnecessary inter hospital transfers. Methods: This was a retrospective case notes review of all patients treated within the Yorkshire and Humber network with symptoms of PIMS between 1st May and 30th November 2020. Patients were classified as to whether or not they had significant cardiac involvement (defined as at least one of: inotrope requirement, ejection fraction <50%, pericardial effusion, coronary artery changes and significant ECG abnormalities). Cardiac markers were analysed at presentation and throughout the hospital admission including plasma NT pro-BNP, LDH, CRP, d-dimer and troponin. Statistical tests (Fisher's exact test for categorical variables, ttest for continuous variables) were used to identify which factors were indicative of significant cardiac involvement (SCI). Results: 22 patients met the inclusion criteria (Table 1). 14/22 patients (63.6%) were judged to have SCI. Markers that were found to be indicative of SCI included CRP and plasma NT pro-BNP (Table 2). Furthermore, when using a threshold of 2000ng/L, plasma NT pro-BNP was found to be 71% sensitive and 80% specific for SCI. In addition, when combined with a CRP threshold of 100mg/L, there was a positive predictive value of 85% and negative predictive value of 75%. Conclusions: PIMS is an important new syndrome affecting paediatric patients across the Yorkshire and Humber region. A significant proportion of the affected patients have cardiac involvement and require management in a specialist centre. Early identification of these patients using serological markers facilitates rapid treatment preventing long term sequelae whilst also reducing unnecessary interhospital transfers.

The value of a cure: an asset pricing perspective

We provide an estimate of the value of a cure using the joint behavior of stock prices and a vaccine progress indicator during the ongoing COVID-19 pandemic. Our indicator is based on the chronology of stage-by-stage progress of individual vaccines and related news. We construct a general equilibrium regime-switching model of repeated pandemics and stages of vaccine progress wherein the representative agent withdraws labor and alters consumption endogenously to mitigate health risk. The value of a cure in the resulting asset-pricing framework is intimately linked to the relative labor supply across states. The observed stock market response to vaccine progress serves to identify this quantity, allowing us to use the model to estimate the economywide welfare gain that would be attributable to a cure. In our estimation, and with standard preference parameters, the value of the ability to end the pandemic is worth 5-15% of total wealth. This value rises substantially when there is uncertainty about the frequency and duration of pandemics. Agents place almost as much value on the ability to resolve the uncertainty as they do on the value of the cure itself. This effect is stronger - not weaker - when agents have a preference for later resolution of uncertainty. The policy implication is that understanding the fundamental biological and social determinants of future pandemics may be as important as resolving the immediate crisis.

Toward Data-Model-Agnostic Autonomous Machine-Generated Data Labeling and Annotation Platform: COVID-19 Autoannotation Use Case

Quick, early, and precise detection is important for diagnosis to control the spread of COVID-19 infection. Artificial Intelligence (AI) technology could certainly be used as a modulating tool to ease the detection, and help with the preventive steps further. Convolutional neural networks (CNNs) have achieved state-of-the-art performance in many visual recognition tasks. Nevertheless, most of these state-of-the-art networks highly rely on the availability of a high amount of labeled data, being an essential step in supervised machine learning tasks. Conventionally, this manual, mundane, and time-consuming process of annotating images is done by humans. Learning to localize or detect COVID-19 infection masks in our specific case study typically requires the collection of CT scan data that has been labeled with bounding boxes or similar annotations, which generally is limited. A technique that could perform such learning with much less annotations, and transfer the learned proposals that are algorithm-driven to generate more synthetic annotated samples would be helpful And quite valuable. We present such a technique inspired by weakly trained mask region based convolutional neural networks (R-CNN) architecture for localization, in which the number of images with their pixel-level masks can be a small proportion of the total dataset, and then further improvise CNNs by inversely generating dense annotations on-the-go using an algorithmic-based computational approach. We focus on alleviating the bottleneck associated with deep learning models needing annotated data for training in an intuitive reverse engineering fashion through this work. Our proposed solution can certainly provide the prospect of automated labeling on-the-fly, thereby reducing much of the manual work. As a result, one can quickly train a precise COVID-19 infection detector with the leverage of autonomous frame-by-frame machine generated annotations. The model achieved mean precision accuracy (%) of 0.99, 0.931, and 0.8 for train, validation, and test set, respectively. The results demonstrate that the proposed method can be adopted in a clinical setting for assisting radiologists, and also our fully autonomous approach can be generalized to any detection/recognition tasks at ease. IEEE

The Risk of Being a Fallen Angel and the Corporate Dash for Cash in the Midst of COVID

Data on firm-loan-level daily credit line drawdowns in the United States expose a corporate "dash for cash" induced by the COVID-19 pandemic. In the first phase of the crisis, which was characterized by extreme precaution and heightened aggregate risk, all firms drew down bank credit lines and raised cash levels. In the second phase, which followed the adoption of stabilization policies, only the highest-rated firms switched to capital markets to raise cash. Consistent with the risk of becoming a fallen angel, the lowest-quality BBB-rated firms behaved more similarly to non-investment grade firms. The observed corporate behavior reveals the significant impact of credit risk on corporate cash holdings.