Saliva metabolomic profile of COVID-19 patients associates with disease severity.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) is strongly linked to dysregulation of various molecular, cellular, and physiological processes that change abundance of different biomolecules including metabolites that may be ultimately used as biomarkers for disease progression and severity. It is important at early stage to readily distinguish those patients that are likely to progress to moderate and severe stages. OBJECTIVES: This study aimed to investigate the utility of saliva and plasma metabolomic profiles as a potential parameter for risk stratifying COVID-19 patients. METHOD: LC-MS/MS-based untargeted metabolomics were used to profile the changes in saliva and plasma metabolomic profiles of COVID-19 patients with different severities. RESULTS: Saliva and plasma metabolites were screened in 62 COVID-19 patients and 18 non-infected controls. The COVID-19 group included 16 severe, 15 moderate, 16 mild, and 15 asymptomatic cases. Thirty-six differential metabolites were detected in COVID-19 versus control comparisons. SARS-CoV-2 induced metabolic derangement differed with infection severity. The metabolic changes were identified in saliva and plasma, however, saliva showed higher intensity of metabolic changes. Levels of saliva metabolites such as sphingosine and kynurenine were significantly different between COVID-19 infected and non-infected individuals; while linoleic acid and Alpha-ketoisovaleric acid were specifically increased in severe compared to non-severe patients. As expected, the two prognostic biomarkers of C-reactive protein and D-dimer were negatively correlated with sphingosine and 5-Aminolevulinic acid, and positively correlated with L-Tryptophan and L-Kynurenine. CONCLUSION: Saliva disease-specific and severity-specific metabolite could be employed as potential COVID-19 diagnostic and prognostic biomarkers.

UAE e-learning sentiment analysis framework

This research project predicts and infers real-time insights on public mental health relevant to education during and after the COVID-19 pandemic by modeling, deploying, and testing an end-to-end spatiotemporal sentiment analysis framework. Moreover, the project aims to analyze the sentiments and emotions of the public;from Twitter, toward the current context of the e-learning process factored by aspects and emotions. The framework consists of four predictive models based on statistical analysis and machine learning to analyze the UAE education-related Twitter dataset. The first analytics is spatiotemporal analytics, which describes an event at a specific time and specific location. Spatiotemporal analytics is used as the base for the remaining three analytics: Aspect-based Sentiment Analysis, sentiment analysis, and emotion analysis. Aspectbased Sentiment Analysis considers the words/terms related to relevant aspects and then identify the sentiment associated with them. Sentiment Analysis is used to extract the sentiment in a specific text. Emotion Analysis identifies the type of emotion felt by users in their tweets. All the analytics will be visualized into a responsive website that provides a prompt understanding of the public opinions and their feedback towards the e-learning process. As a result, a group of recommendations is generated based on the analytics' resulting emotion to enhance the mental health. © 2021 Association for Computing Machinery.