Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. IMPORTANCE COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).Copyright © 2021 Rando et al.

Accuracy of the Traditional COVID-19 Phone Triaging System and Phone Triage-Driven Deep Learning Model.

OBJECTIVES: During the COVID-19 pandemic, a quick and reliable phone-triage system is critical for early care and efficient distribution of hospital resources. The study aimed to assess the accuracy of the traditional phone-triage system and phone triage-driven deep learning model in the prediction of positive COVID-19 patients. SETTING: This is a retrospective study conducted at the family medicine department, Cairo University. METHODS: The study included a dataset of 943 suspected COVID-19 patients from the phone triage during the first wave of the pandemic. The accuracy of the phone triaging system was assessed. PCR-dependent and phone triage-driven deep learning model for automated classifications of natural human responses was conducted. RESULTS: Based on the RT-PCR results, we found that myalgia, fever, and contact with a case with respiratory symptoms had the highest sensitivity among the symptoms/ risk factors that were asked during the phone calls (86.3%, 77.5%, and 75.1%, respectively). While immunodeficiency, smoking, and loss of smell or taste had the highest specificity (96.9%, 83.6%, and 74.0%, respectively). The positive predictive value (PPV) of phone triage was 48.4%. The classification accuracy achieved by the deep learning model was 66%, while the PPV was 70.5%. CONCLUSION: Phone triage and deep learning models are feasible and convenient tools for screening COVID-19 patients. Using the deep learning models for symptoms screening will help to provide the proper medical care as early as possible for those at a higher risk of developing severe illness paving the way for a more efficient allocation of the scanty health resources.

Explainable Data Analytics for Disease and Healthcare Informatics

With advancements in technology, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. Examples of these valuable data include healthcare and disease data such as privacy-preserving statistics on patients who suffered from diseases like the coronavirus disease 2019 (COVID-19). Analyzing these data can be for social good. For instance, data analytics on the healthcare and disease data often leads to the discovery of useful information and knowledge about the disease. Explainable artificial intelligence (XAI) further enhances the interpretability of the discovered knowledge. Consequently, the explainable data analytics helps people to get a better understanding of the disease, which may inspire them to take part in preventing, detecting, controlling and combating the disease. In this paper, we present an explainable data analytics system for disease and healthcare informatics. Our system consists of two key components. The predictor component analyzes and mines historical disease and healthcare data for making predictions on future data. Although huge volumes of disease and healthcare data have been generated, volumes of available data may vary partially due to privacy concerns. So, the predictor makes predictions with different methods. It uses random forest With sufficient data and neural network-based few-shot learning (FSL) with limited data. The explainer component provides the general model reasoning and a meaningful explanation for specific predictions. As a database engineering application, we evaluate our system by applying it to real-life COVID-19 data. Evaluation results show the practicality of our system in explainable data analytics for disease and healthcare informatics. © 2021 ACM.

Dentistry Students' Perception of the Online Anatomy Subject in Times of COVID-19

In 2020, the COVID-19 pandemic forced all university teaching to be taught online, including the teaching of human anatomy. The objective of this work was to evaluate the students' perception regarding digital resources and active strategies used in the online version of the human anatomy subject. The sample consisted of 77 first-year dentistry students who were studying anatomy. For data collection, a self-application questionnaire and three focus groups with semi-structured questions were used. Quantitative data were analyzed with descriptive statistics and qualitative data using grounded theory. The qualitative analysis determined 6 relevant categories expressed by the students: possibility of working collaboratively, feedback spaces, type of information, use of the material, perception of the academic and the time factor. Regarding the quantitative analysis, the manual of Applied Anatomy for Dentistry Students was the best evaluated digital resource (p<0.005), which was statistically significant. It was followed by the Visible Body 3D atlas as the second best evaluated digital resource (p < 0.005). While the analysis of clinical cases and the making of drawings were the best evaluated active strategies, they were statistically significant (p<0.005). The most recommended digital resource by the students, was the manual (30.4 %) followed by the Visible Body 3D atlas (28.5 %). The most recommended active strategy was group labeling of manual models (37.5 %). Students positively perceive the virtualization of the subject, highlighting the role of the teacher through feedback and peer interaction.

Perception of Teachers and Students About the Experience in the use of the Hyflex Model in Anatomy

The COVID-19 pandemic forced universities to abruptly teach their subjects in an online or semi-face-to-face format. This is how the use of the Hyflex educational model emerged as an alternative. The objective of this study was to know the perception of students and teachers about the experience in the use of Hyflex in anatomy. Mixed-type descriptive study was carried out, with a convenience sample of 115 students and 7 teachers who participated in Applied Anatomy during the year 2021 in the Skills Rooms of the Simulation Hospital of the Andres Bello University, Vina del Mar, Chile. Data collection was through a self-application questionnaire with a 5-level Likert-type scale and a focus group with a script of 9 semi-structured questions. The quantitative data were analyzed with descriptive statistics and the application of the Mann-White test to compare between groups with a P < 0.05. Qualitative data were analyzed using grounded theory to identify main categories and subcategories. Both teachers and students had a good perception of the use of Hyflex. In both groups, the highlight was that it enabled access to online content and activities that complemented face-to-face activities. However, both groups agreed that they do not learn more in the online format than in person. In addition, in the focus group interviews two main categories emerged, satisfaction (highlighting the subcategories content understanding, administrative aspects, access to information) and modality (quality of learning, performance, participation, reception of information and social aspects). Hyflex is an alternative to teach anatomy content, although students and teachers perceive that attendance is essential to provide an adequate learning experience. Copyright © 2022, Universidad de la Frontera. All rights reserved.