Biomedical Engineering and Bioengineering Commons^™

Recombinant Human Plasma Gelsolin Reverses Increased Permeability Of The Blood-Brain Barrier Induced By The Spike Protein Of The Sars-Cov-2 Virus., Łukasz Suprewicz, Kiet A Tran, Ewelina Piktel, Krzysztof Fiedoruk, Paul A Janmey, Peter Galie, Robert Bucki

Henry M. Rowan College of Engineering Faculty Scholarship

BACKGROUND: Plasma gelsolin (pGSN) is an important part of the blood actin buffer that prevents negative consequences of possible F-actin deposition in the microcirculation and has various functions during host immune response. Recent reports reveal that severe COVID-19 correlates with reduced levels of pGSN. Therefore, using an in vitro system, we investigated whether pGSN could attenuate increased permeability of the blood-brain barrier (BBB) during its exposure to the portion of the SARS-CoV-2 spike protein containing the receptor binding domain (S1 subunit).

MATERIALS AND METHODS: Two- and three-dimensional models of the human BBB were constructed using the human cerebral microvascular endothelial …

Reduced Chest Computed Tomography Scan Length For Patients Positive For Coronavirus Disease 2019: Dose Reduction And Impact On Diagnostic Utility, Sara Principi, Stacy D. O'Connor, Frank Luba, Taly Gilat Schmidt

Biomedical Engineering Faculty Research and Publications

Objective

This work aimed to retrospectively evaluate the potential of dose reduction on chest computed tomography (CT) examinations by reducing the longitudinal scan length for patients positive for coronavirus disease 2019 (COVID-19).

Methods

This study used the Personalized Rapid Estimation of Dose in CT (PREDICT) tool to estimate patient-specific organ doses from CT image data. The PREDICT is a research tool that combines a linear Boltzmann transport equation solver for radiation dose map generation with deep learning algorithms for organ contouring. Computed tomography images from 74 subjects in the Medical Imaging Data Resource Center–RSNA International COVID-19 Open Radiology Database data …

Assessing Structural And Functional Brain Alterations And Work-Related Fatigue In Non-Hyposmic And Hyposmic Covid-19 Survivors, Rakibul Hafiz

Dissertations

In the year 2019, life began to change at the advent of a global pandemic caused by the novel coronavirus. Mask mandates and mass vaccinations have mitigated the effects significantly, yet cases keep rising with new variants, especially, in densely populated countries, like India. Recent neuroimaging evidence shows the virus can attack the central nervous system (CNS). However, exactly which brain regions undergo structural and functional changes remain largely unknown. Many patients experience 'loss of/reduced sense of smell' (i.e., hyposmic) and an alarming number of survivors develop persistent symptoms ('long-COVID') for several months after initial infection. Fatigue is the most …

Overcoming Supply Disruptions During Pandemics By Utilizing Found Hardware For Open Source Gentle Ventilation, S. Oberloier, N. Gallup, J. M. Pearce

Michigan Tech Publications

This article details the design of an open source emergency gentle ventilator (gentle-vent) framework that can be used in periods of scarcity. Although it is not a medical device, the system utilizes a wide range of commonly-available components that are combined using basic electronics skills to achieve the desired performance. The main function of the gentle-vent is to generate a calibrated pressure wave at the pump to provide support to the patient's breathing. Each gentle-vent permutation was tested using a DIY manometer as it would be utilized in the field in low-resource settings and validated with an open source VentMon. …

Deep-Precognitive Diagnosis: Preventing Future Pandemics By Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Chao Cheng, Jing Zhang, Tianyang Wang, Min Xu

Computer Vision Faculty Publications

Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling their rapid spread. Moreover, the development of a conventional CAD model is only possible after disease outbreaks and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, it is challenging to recognize them …

What Drug Candidates Exist For Treating Covid-19 Variants?, Kolton Hauck

Research on Capitol Hill

USU senior Kolton studies biological engineering and led this project with funding from a student grant. Kolton’s project has been mapping out all possible protein interactions from known SARS-CoV-2 variants and identifying possible drug treatments. Viruses function through these interactions between their proteins and the host cell’s proteins, and this project is especially valuable because the candidates identified are predicted to help treat all known variants, making them more likely to be effective against future mutations.Kolton’s interest in biological research stems from his own experience managing a genetic health disorder. Past medical research greatly benefited him, and he sees his …

Hyperparameter Optimization For Covid-19 Chest X-Ray Classification, Ibraheem Hamdi, Muhammad Ridzuan, Mohammad Yaqub

Computer Vision Faculty Publications

Despite the introduction of vaccines, Coronavirus disease (COVID-19) remains a worldwide dilemma, continuously developing new variants such as Delta and the recent Omicron. The current standard for testing is through polymerase chain reaction (PCR). However, PCRs can be expensive, slow, and/or inaccessible to many people. X-rays on the other hand have been readily used since the early 20th century and are relatively cheaper, quicker to obtain, and typically covered by health insurance. With a careful selection of model, hyperparameters, and augmentations, we show that it is possible to develop models with 83% accuracy in binary classification and 64% in multi-class …

Challenges In Covid-19 Chest X-Ray Classification: Problematic Data Or Ineffective Approaches?, Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarrete, Ibrahim Almakky, Mohammad Yaqub

Computer Vision Faculty Publications

The value of quick, accurate, and confident diagnoses cannot be undermined to mitigate the effects of COVID-19 infection, particularly for severe cases. Enormous effort has been put towards developing deep learning methods to classify and detect COVID-19 infections from chest radiography images. However, recently some questions have been raised surrounding the clinical viability and effectiveness of such methods. In this work, we carry out extensive experiments on a large COVID-19 chest X-ray dataset to investigate the challenges faced with creating reliable solutions from both the data and machine learning perspectives. Accordingly, we offer an in-depth discussion into the challenges faced …

Uv-Incorporated Sanitation Lining For Disinfection Of Carpentry Tools In Grade Schools, Nicole Dembiczak, Alex Paxitzis, Quinn Piorkowski, Ivana Shah

Williams Honors College, Honors Research Projects

This project will focus on the issues caused by a pandemic, particularly, sanitation issues and difficulties in a public area. After a considerable amount of research, it became evident that current sanitation methods are time inefficient, not always effective and wasteful. There are numerous misconceptions on how these methods work and skepticism of the products themselves. The pandemic caused by COVID-19 is an extremely relevant issue that has affected schools, hospitals and other high traffic areas. This project will focus on K-12 grade schools that struggle with quick, easy and effective cleaning methods in between and after class. We aim …