Enhancing Student Engagement in Online Learning through Facial Expression Analysis and Complex Emotion Recognition using Deep Learning (preprint)

In response to the COVID-19 pandemic, traditional physical classrooms have transitioned to online environments, necessitating effective strategies to ensure sustained student engagement. A significant challenge in online teaching is the absence of real-time feedback from teachers on students learning progress. This paper introduces a novel approach employing deep learning techniques based on facial expressions to assess students engagement levels during online learning sessions. Human emotions cannot be adequately conveyed by a student using only the basic emotions, including anger, disgust, fear, joy, sadness, surprise, and neutrality. To address this challenge, proposed a generation of four complex emotions such as confusion, satisfaction, disappointment, and frustration by combining the basic emotions. These complex emotions are often experienced simultaneously by students during the learning session. To depict these emotions dynamically,utilized a continuous stream of image frames instead of discrete images. The proposed work utilized a Convolutional Neural Network (CNN) model to categorize the fundamental emotional states of learners accurately. The proposed CNN model demonstrates strong performance, achieving a 95% accuracy in precise categorization of learner emotions.

Awareness and Use of Digital Learning Before and During COVID-19

The COVID-19 pandemic has caused a massive shift in the worldwide educational teaching-learning system. All educational activities have shifted digitally. Due to this sudden shift, digital learning has experienced significant change. The current study aims to analyze the pre-COVID-19 and post-COVID-19 perceptions and usage of digital learning among school children. Also, how COVID-19 impacted their digital learning knowledge. Two surveys were conducted, one before the COVID-19 outbreak and the other after the outbreak. The study's findings show that students' digital learning usage increased significantly after the outbreak of COVID-19, and they are more satisfied with the digital learning facilities. Students' knowledge about digital learning is also increased. However, after COVID-19, the students' motivation dropped. The majority of respondents had never used digital learning before COVID-19. The widespread usage of digital learning has also raised barriers. The barriers outweighed the benefits of digital learning.

Baricitinib Treatment of Coronavirus Disease 2019 Is Associated With a Reduction in Secondary Infections.

We performed a secondary analysis of the National Institutes of Health-sponsored Adaptive COVID-19 Treatment Trial (ACTT-2) randomized controlled trial and found that baricitinib was associated with a 50% reduction in secondary infections after controlling for baseline and postrandomization patient characteristics. This finding provides a novel mechanism of benefit for baricitinib and supports the safety profile of this immunomodulator for the treatment of coronavirus disease 2019.

Temporal Improvements in COVID-19 Outcomes for Hospitalized Adults: A Post Hoc Observational Study of Remdesivir Group Participants in the Adaptive COVID-19 Treatment Trial.

BACKGROUND: The COVID-19 standard of care (SOC) evolved rapidly during 2020 and 2021, but its cumulative effect over time is unclear. OBJECTIVE: To evaluate whether recovery and mortality improved as SOC evolved, using data from ACTT (Adaptive COVID-19 Treatment Trial). DESIGN: ACTT is a series of phase 3, randomized, double-blind, placebo-controlled trials that evaluated COVID-19 therapeutics from February 2020 through May 2021. ACTT-1 compared remdesivir plus SOC to placebo plus SOC, and in ACTT-2 and ACTT-3, remdesivir plus SOC was the control group. This post hoc analysis compared recovery and mortality between these comparable sequential cohorts of patients who received remdesivir plus SOC, adjusting for baseline characteristics with propensity score weighting. The analysis was repeated for participants in ACTT-3 and ACTT-4 who received remdesivir plus dexamethasone plus SOC. Trends in SOC that could explain outcome improvements were analyzed. (ClinicalTrials.gov: NCT04280705 [ACTT-1], NCT04401579 [ACTT-2], NCT04492475 [ACTT-3], and NCT04640168 [ACTT-4]). SETTING: 94 hospitals in 10 countries (86% U.S. participants). PARTICIPANTS: Adults hospitalized with COVID-19. INTERVENTION: SOC. MEASUREMENTS: 28-day mortality and recovery. RESULTS: Although outcomes were better in ACTT-2 than in ACTT-1, adjusted hazard ratios (HRs) were close to 1 (HR for recovery, 1.04 [95% CI, 0.92 to 1.17]; HR for mortality, 0.90 [CI, 0.56 to 1.40]). Comparable patients were less likely to be intubated in ACTT-2 than in ACTT-1 (odds ratio, 0.75 [CI, 0.53 to 0.97]), and hydroxychloroquine use decreased. Outcomes improved from ACTT-2 to ACTT-3 (HR for recovery, 1.43 [CI, 1.24 to 1.64]; HR for mortality, 0.45 [CI, 0.21 to 0.97]). Potential explanatory factors (SOC trends, case surges, and variant trends) were similar between ACTT-2 and ACTT-3, except for increased dexamethasone use (11% to 77%). Outcomes were similar in ACTT-3 and ACTT-4. Antibiotic use decreased gradually across all stages. LIMITATION: Unmeasured confounding. CONCLUSION: Changes in patient composition explained improved outcomes from ACTT-1 to ACTT-2 but not from ACTT-2 to ACTT-3, suggesting improved SOC. These results support excluding nonconcurrent controls from analysis of platform trials in rapidly changing therapeutic areas. PRIMARY FUNDING SOURCE: National Institute of Allergy and Infectious Diseases.

Analysis of Neuroimaging in COVID-19 Patients - A Correlation Between Lung Severity and Brain Findings

Background: Corona virus emerged in China in December 2019 and quickly spread over the world, causing a pandemic. The probable link between the occurrence of neurological abnormalities and the CT severity score (CTSS) in COVID-19 participants is less understood. The purpose of this study was to look at the neurological symptoms of COVID-19 on CT head and determine whether there was a link between thorax and brain imaging abnormalities in COVID-19 patients. Method(s): Total 135 Hospitalized COVID positive patients with acute neurological symptoms underwent both CT head and CT thorax during their hospital stay were included in the study. All the patients with neuroimaging were divided into 2 groups: first being patients with acute neuroimaging findings and the second being the patients with chronic/normal neuroimaging findings. Result(s): The most common CT head imaging findings in these individuals were acute ischemic infarcts in 54 (40%) and acute intracranial haemorrhage in 8 (6%). When compared to individuals with normal/chronic neurological results, a greater mean chest CTSS was found in patients with acute abnormalities on CT head (14.1 [SD-3.2] versus 6.5 [SD-3.3]). However, no statistical correlation could be shown between a greater CTSS and the occurrence of acute neurological disorders. Conclusion(s): There was no link between a greater CTSS and the occurrence of neurological disorders on CT scans. As a result, increased lung involvement severity may not be a good predictor of brain involvement in COVID patients. Copyright © 2022 Ubiquity Press. All rights reserved.

Remdesivir for the Prevention of Invasive Mechanical Ventilation or Death in Coronavirus Disease 2019 (COVID-19): A Post Hoc Analysis of the Adaptive COVID-19 Treatment Trial-1 Cohort Data.

This post hoc analysis of the Adaptive Coronavirus Disease 2019 (COVID-19) Treatment Trial-1 (ACTT-1) shows a treatment effect of remdesivir (RDV) on progression to invasive mechanical ventilation (IMV) or death. Additionally, we create a risk profile that better predicts progression than baseline oxygen requirement alone. The highest risk group derives the greatest treatment effect from RDV.

Are Some COVID-19 Vaccines Better Than Others? Interpreting and Comparing Estimates of Efficacy in Vaccine Trials.

Coronavirus disease 2019 (COVID-19) vaccine trials provide valuable insight into the safety and efficacy of vaccines, with individually randomized, placebo-controlled trials being the gold standard in trial design. However, a myriad of variables must be considered as clinical trial data are interpreted and used to guide policy decisions. These variables include factors such as the characteristics of the study population and circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains, the force of infection, the definition and ascertainment of endpoints, the timing of vaccine efficacy assessment, and the potential for performance bias. In this Viewpoints article, we discuss critical variables to consider when comparing efficacy measurements across current and future COVID-19 vaccine trials.

Using Adjuvants to Drive T Cell Responses for Next-Generation Infectious Disease Vaccines.

Using adjuvants to drive features of T cell responses to vaccine antigens is an important technological challenge in the design of new and improved vaccines against infections. Properties such as T helper cell function, T cell memory, and CD8+ T cell cytotoxicity may play critical roles in optimal and long-lived immunity through vaccination. Directly manipulating specific immune activation or antigen delivery pathways with adjuvants may selectively augment desired T cell responses in vaccination and may improve the effectiveness and durability of vaccine responses in humans. In this review we outline recently studied adjuvants in their potential for antigen presenting cell and T cell programming during vaccination, with an emphasis on what has been observed in studies in humans as available.

In silico Study to Evaluate the Antiviral Activity of Novel Structures against 3C-like Protease of Novel Coronavirus (COVID-19) and SARS-CoV.

BACKGROUND: Globally, over 4.3 million laboratory confirmed cases of COVID-19 have been reported from over 105 countries. No FDA approved antiviral is available for the treatment of this infection. Zhavoronkov et al., with their generative chemistry pipeline, have generated structures that can be potential novel drug-like inhibitors for COVID-19, provided they are validated. 3C-like protease (3CLP) is a homodimeric cysteine protease that is present in coronaviruses. Interestingly, 3CLP is 96.1% structurally similar between SARS-CoV and SARS-CoV-2. OBJECTIVE: To evaluate interaction of generated structures with 3CLP of SARS-CoV (RCSB PDB ID: 4MDS). METHODS: Crystal structure of human SARS-CoV with a non-covalent inhibitor with resolution: 1.598 Å was obtained and molecular docking was performed to evaluate the interaction with generated structures. The MM-GBSA and IFD-SP were performed to narrow down to the structures with better binding energy and IFD score. The ADME analysis was performed on top 5 hits and further MD simulation was employed for top 2 hits. RESULTS: In XP docking, IFD-SP and molecular dynamic simulation studies, the top 2 hits 32 and 61 showed interaction with key amino acid residue GLU166. Structure 61, also showed interaction with HIS164. These interactions of generated structure 32 and 61, with GLU166 and HIS164, indicate the binding of the selected drug within the close proximity of 3CLP. In the MD simulation, the protein- ligand complex of 4MDS and structure 61 was found to be more stable for 10ns. CONCLUSION: These identified structures can be further assessed for their antiviral activity to combat SARS-CoV and COVID-19.