Incidence, risk factors and pre-emptive screening for COVID-19 associated pulmonary aspergillosis in an era of immunomodulant therapy.

PURPOSE: COVID-19 associated pulmonary aspergillosis (CAPA) is associated with increased morbidity and mortality in ICU patients. We investigated the incidence of, risk factors for and potential benefit of a pre-emptive screening strategy for CAPA in ICUs in the Netherlands/Belgium during immunosuppressive COVID-19 treatment. MATERIALS AND METHODS: A retrospective, observational, multicentre study was performed from September 2020-April 2021 including patients admitted to the ICU who had undergone diagnostics for CAPA. Patients were classified based on 2020 ECMM/ISHAM consensus criteria. RESULTS: CAPA was diagnosed in 295/1977 (14.9%) patients. Corticosteroids were administered to 97.1% of patients and interleukin-6 inhibitors (anti-IL-6) to 23.5%. EORTC/MSGERC host factors or treatment with anti-IL-6 with or without corticosteroids were not risk factors for CAPA. Ninety-day mortality was 65.3% (145/222) in patients with CAPA compared to 53.7% (176/328) without CAPA (p = 0.008). Median time from ICU admission to CAPA diagnosis was 12 days. Pre-emptive screening for CAPA was not associated with earlier diagnosis or reduced mortality compared to a reactive diagnostic strategy. CONCLUSIONS: CAPA is an indicator of a protracted course of a COVID-19 infection. No benefit of pre-emptive screening was observed, but prospective studies comparing pre-defined strategies would be required to confirm this observation.

Recovery Algorithms for Pooled RT-qPCR based Covid-19 Screening

We consider the problem of sparse signal recovery in a non-adaptive pool-test setting using quantitative measurements from a non-linear model. The quantitative measurements are obtained using the reverse transcription (quantitative) polymerase chain reaction (RT-qPCR) test, which is the standard test used to detect Covid-19. Each quantitative measurement refers to the cycle threshold, a proxy for the viral load in the test sample. We propose two novel, robust recovery algorithms based on alternating direction method of multipliers and block coordinate descent to recover the individual sample cycle thresholds and hence determine the sick individuals, given the pooled sample cycle thresholds and the pooling matrix. We numerically evaluate the normalized mean squared error, false positive rate, false negative rate, and the maximum sparsity levels up to which error-free recovery is possible. We also demonstrate the advantage of using quantitative measurements (as opposed to binary outcomes) in non-adaptive pool testing methods in terms of the testing rate using publicly available data on Covid-19 testing. The simulation results show the effectiveness of the proposed algorithms. IEEE

A Machine Learning-Based Tool for Exploring Covid-19 Scientific Literature

The advent of the COVID-19 pandemic caused by the Sars-CoV2 virus has caused serious damage in different areas. This has prompted thousands of researchers from different disciplines (biology, medicine, artificial intelligence, economics, etc.) to publish a very large number of scientific articles in a very short period, to answer questions related to this pandemic. This abundance of literature, however, raised another problem. It has indeed become extremely difficult for a researcher or a decision-maker to stay up to date with the latest scientific advances or to locate scientific articles related to a specific aspect of this pandemic. In this paper, we present an intelligent tool based on Machine learning, which automatically organizes a large dataset of Covid-19 related scientific literature and visualizes them in a way that helps these people navigating easily through this dataset and locating the sought documents easily. The documents are first pre-processed and transformed into numerical features. Then, those features are passed through a deep denoising autoencoder followed by Uniform Manifold Approximation and Projection technique (UMAP) to reduce their dimensionality into a 2D space. The projected data are then clustered with Agglomerative Clustering Algorithm. This is followed by a topic modeling step which we performed using Latent Dirichlet Allocation (LDA), in order to assign a label to each cluster. Finally, the documents are visualized to the user in an interactive interface that we developed. The experiments we conducted proved that our tool is efficient and useful. © 2021 IEEE.

Clinical characteristics of patients with COVID-19 in Asian countries

Severe Acute Respiratory Syndrome Coronavirus 2 [SARS-CoV-2] has been identified as the cause of an outbreak of respiratory illness in Wuhan, China beginning December 2019. As of 14th July 2020, the pandemic recorded 12,964,809 confirmed cases with 570,288 deaths globally. A thorough review of the clinical characteristics of this infection will help in understanding this widespread but seemingly localizing disease. This research aimed to review the clinical characteristic of Covid-19 including the risk factors, clinical manifestations, laboratory and radiological findings and complications exhibited in Asia. A narrative review was conducted along with 48 research articles published in Scopus journals in 2020 in Asian countries on clinical characteristics of the disease. It was concluded that despite being in the same region, clinical characteristics of COVID-19 varies among the Asian countries. The mortality rate among Asian countries was varied with the competency of public health measures as one of the vital contributing factors. The variable findings on the clinical characteristics may be due to inconsistent quality of methodology in recent researches which was a rapid response to the pandemic. Although these studies are necessary to manage the current global emergency, more high-quality researches are needed to provide a valid and reliable scientific understanding of the disease. © 2020, All India Institute of Medical Sciences. All rights reserved.