Influenza Humans Commons^™

Hu308, A Selective Cannabinoid Type-2 Receptor Agonist, Mitigates Sars-Cov-2 Spike Protein-Induced Acute Lung Injury In Mice, Janette Lockett, Gregory Nicholson, Nicholas Richards, Ryan Washington, Nagaraja Nagre

Department of Biomedical and Translational Sciences Faculty Publications

Introduction

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to pose major health challenges despite effective vaccination efforts. The sustained occurrence of breakthrough infections and emerging variants of the virus highlights the need for additional therapeutic strategies. Given the anti-inflammatory role of the cannabinoid type 2 receptor (CB2R), we examined the effect of CB2R activation in SARS-CoV-2 spike protein subunit 1 (S1SP)-induced acute lung injury (ALI).

Methods

ALI was induced in mice by intratracheal (i.t.) administration of S1SP, followed by treatment with the CB2R agonist HU308 (5 mg/kg, intraperitoneal: i.p.) 1 h …

Targeting Ptp4a3 And Kvx-053 Mitigates Alcohol-Amplified Sars-Cov-2 Spike Protein-Induced Acute Lung Injury, Pavel A. Solopov, Ruben Manuel Luciano Colunga Biancatelli, Elizabeth R. Sharlow, John S. Lazo, John D. Catavas

Bioelectrics Publications

Introduction: Chronic alcohol exposure is associated with increased vulnerability to acute respiratory distress syndrome (ARDS), and emerging evidence suggests that this susceptibility extends to the severe respiratory manifestations of COVID- 19. The SARS-CoV-2 Spike Protein S1 subunit (S1SP) can independently trigger robust inflammatory and vascular injury responses in the lung, and our prior work demonstrated that chronic alcohol consumption markedly intensifies these effects in mice. PTP4A3 phosphatase is a key regulator of inflammation and endothelial barrier function. In this study, we investigated whether the PTP4A3 inhibitor KVX-053 can counteract alcohol-enhanced S1SP-induced lung injury in K18-hACE2 mice.

Methods: Animals were maintained …

Detecting Sars-Cov-2 In Ct Scans Using Vision Transformer And Graph Neural Network, Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng

Electrical & Computer Engineering Faculty Publications

The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer (ViTGNN), an advanced hybrid model designed to enhance SARS-CoV-2 detection by combining Graph Neural Networks (GNNs) for feature extraction with Vision Transformers (ViTs) for classification. Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was …

A Data-Driven Sliding-Window Pairwise Comparative Approach For The Estimation Of Transmission Fitness Of Sars-Cov-2 Variants And The Construction Of The Evolution Fitness Landscape, Md Jubair Pantho, Richard Annan, Landen Alexander Bauder, Sophia Huang, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Estimating the transmission fitness of SARS-CoV-2 variants and understanding their evolutionary fitness trends are important for epidemiological forecasting. Existing methods are often constrained by their parametric natures and do not satisfactorily align with the observations during COVID-19. Here, we introduce a sliding-window data-driven pairwise comparison method, the differential population growth rate (DPGR) that uses viral strains as internal controls to mitigate sampling biases. DPGR is applicable in time windows in which the logarithmic ratio of two variant subpopulations is approximately linear. We apply DPGR to genomic surveillance data and focus on variants of concern (VOCs) in multiple countries and regions. …

Cath-Ddg: Towards Robust Mutation Effect Prediction On Protein-Protein Interactions Out Of Cath Homologous Superfamily, Guanglei Yu, Xuehua Bi, Teng Ma, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Protein-protein interactions (PPIs) are fundamental aspects in understanding biological processes. Accurately predicting the effects of mutations on PPIs remains a critical requirement for drug design and disease mechanistic studies. Recently, deep learning models using protein 3D structures have become predominant for predicting mutation effects. However, significant challenges remain in practical applications, in part due to the considerable disparity in generalization capabilities between easy and hard mutations. Specifically, a hard mutation is defined as one with its maximum TM-score < 0.6 when compared to the training set. Additionally, compared to physics-based approaches, deep learning models may overestimate performance due to potential data leakage.

Results: We propose new training/test splits that mitigate data leakage according to the CATH homologous superfamily. Under the constraints of physical …