ABSTRACT
BackgroundEvaluating the prognosis of COVID-19 patients who may be at risk of mortality using the simple chest X-ray (CXR) severity scoring systems provides valuable insights for treatment decisions. This study aimed to assess how well the simplified Radiographic Assessment of Lung Edema (RALE) score could predict the death of critically ill COVID-19 patients in Vietnam. MethodsFrom July 30 to October 15, 2021, we conducted a cross-sectional study on critically ill COVID-19 adult patients at an intensive care centre in Vietnam. We calculated the areas under the receiver operator characteristic (ROC) curve (AUROC) to determine how well the simplified RALE score could predict hospital mortality. In a frontal CXR, the simplified RALE score assigns a score to each lung, ranging from 0 to 4. The overall severity score is the sum of points from both lungs, with a maximum possible score of 8. We also utilized ROC curve analysis to find the best cut-off value for this score. Finally, we utilized logistic regression to identify the association of simplified RALE score with hospital mortality. ResultsOf 105 patients, 40.0% were men, the median age was 61.0 years (Q1-Q3: 52.0-71.0), and 79.0% of patients died in the hospital. Most patients exhibited bilateral lung opacities on their admission CXRs (99.0%; 100/102), with the highest occurrence of opacity distribution spanning three (18.3%; 19/104) to four quadrants of the lungs (74.0%; 77/104) and a high median simplified RALE score of 8.0 (Q1-Q3: 6.0-8.0). The simplified RALE score (AUROC: 0.747 [95% CI: 0.617-0.877]; cut-off value [≥]5.5; sensitivity: 93.9%; specificity: 45.5%; PAUROC <0.001) demonstrated a good discriminatory ability in predicting hospital mortality. After adjusting for confounding factors such as age, gender, Charlson Comorbidity Index, serum interleukin-6 level upon admission, and admission severity scoring systems, the simplified RALE score of [≥]5.5 (adjusted OR: 18.437; 95% CI: 3.215-105.741; p =0.001) was independently associated with an increased risk of hospital mortality. ConclusionsThis study focused on a highly selected cohort of critically ill COVID-19 patients with a high simplified RALE score and a high mortality rate. Beyond its good discriminatory ability in predicting hospital mortality, the simplified RALE score also emerged as an independent predictor of hospital mortality.
Subject(s)
COVID-19 , EdemaABSTRACT
Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.
Subject(s)
COVID-19ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6,324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.
Subject(s)
COVID-19ABSTRACT
Origin of the COVID-19 virus has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic. Recent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences. This paper investigates the COVID-19 virus origin by using artificial intelligence (AI) and raw genomic sequences of the virus. More than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods. The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes. This provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 virus. At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.