Medicine and Health Sciences Commons^™

Msdrp: A Deep Learning Model Based On Multisource Data For Predicting Drug Response, Haochen Zhao, Xiaoyu Zhang, Qichang Zhao, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell …

Evaluation Of Cold Atmospheric Plasma For The Decontamination Of Flexible Endoscopes, R. C. Hervé, Michael G. Kong, Sudhir Bhatt, Hai-Lan Chen, E. E. Comoy, J-P. Deslys, T. J. Secker, C. W. Keevil

Bioelectrics Publications

Background: Despite adherence to standard protocols, residues including live microorganisms may remain on the various surfaces of reprocessed flexible endoscopes. Prions are infectious proteins notoriously difficult to eliminate.

Aim: We tested the potential of cold atmospheric plasma (CAP) for the decontamination of flexible endoscope various surfaces, measuring total proteins and prion-residual infectivity as an indicator of efficacy.

Methods: New PTFE endoscope channels and metal test surfaces spiked with test soil or prion-infected tissues were treated using different CAP-generating prototypes. Surfaces were then examined for the presence of residues using very sensitive fluorescence epi-microscopy. Prion residual infectivity was determined using the …

Opioid Use Disorder Prediction Using Machine Learning Of Fmri Data, A. Temtam, Liangsuo Ma, F. Gerard Moeller, M. S. Sadique, K. M. Iftekharuddin, Khan M. Iftekharuddin (Ed.), Weijie Chen (Ed.)

Electrical & Computer Engineering Faculty Publications

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical …

Prediction Of Rapid Early Progression And Survival Risk With Pre-Radiation Mri In Who Grade 4 Glioma Patients, Walia Farzana, Mustafa M. Basree, Norou Diawara, Zeina Shboul, Sagel Dubey, Marie M. Lockheart, Mohamed Hamza, Joshua D. Palmer, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Rapid early progression (REP) has been defined as increased nodular enhancement at the border of the resection cavity, the appearance of new lesions outside the resection cavity, or increased enhancement of the residual disease after surgery and before radiation. Patients with REP have worse survival compared to patients without REP (non-REP). Therefore, a reliable method for differentiating REP from non-REP is hypothesized to assist in personlized treatment planning. A potential approach is to use the radiomics and fractal texture features extracted from brain tumors to characterize morphological and physiological properties. We propose a random sampling-based ensemble classification model. The proposed …

Identifying The Serious Clinical Outcomes Of Adverse Reactions To Drugs By A Multi-Task Deep Learning Framework, Haochen Zhao, Peng Ni, Qichang Zhao, Xiao Liang, Di Ai, Shannon Erhardt, Jun Wang, Yaohang Li, Jiianxin Wang

Computer Science Faculty Publications

Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing computational methods primarily focus on predicting whether or not the drug is associated with an adverse reaction and do not consider the core issue of drug benefit-risk assessment-whether the treatment outcome is serious when adverse drug reactions occur. To this end, we categorize serious clinical outcomes caused by adverse reactions to drugs into seven distinct classes and present a deep learning framework, so-called GCAP, for predicting the …

An Acute Respiratory Distress Syndrome Drug Development Collaboration Stimulated By The Virginia Drug Discovery Consortium, John S. Lazo, Ruben M.L. Colunga-Biancatelli, Pavel A. Solopov, John D. Catravas

Bioelectrics Publications

The genesis of most older medicinal agents has generally been empirical. During the past one and a half centuries, at least in the Western countries, discovering and developing drugs has been primarily the domain of pharmaceutical companies largely built upon concepts emerging from organic chemistry. Public sector funding for the discovery of new therapeutics has more recently stimulated local, national, and international groups to band together and focus on new human disease targets and novel treatment approaches. This Perspective describes one contemporary example of a newly formed collaboration that was simulated by a regional drug discovery consortium. University of Virginia, …

A Comparison Of Chief Complaints, Specific Diagnoses, And Demographics Of Pediatric Urgent Care Visits Before And During The Covid-19 Pandemic: A Retrospective Study, Zaharoula A. Viennas, Julie Martin, Benjamin Klick, Tammy Speerhas, Turaj Vazifedan, Danielle Millspaugh, Jennifer Ferris, Margret Bedle, Lauren Paluch, Theresa Guins

Nursing Faculty Publications

There was an increased incidence of pediatric patients who presented with injuries caused by falls not related to sports or other recreational activities, as well as for animal bites, during the early pandemic period of April 2020. Education of parents and caregivers of young children is warranted to raise awareness of the even greater potential for falls and animal bites when children are confined at home for longer than typical periods of time, as occurred with the stay-at-home government orders during the initial period of the COVID-19 pandemic.

A Quality Improvement Project To Improve Management Of Urinary Tract Infections In A System Of Pediatric Urgent Care Centers, Benjamin Klick, Tammy Speerhas, Jessica Parrott, Jeffrey Bobrowitz, Anne Mcevoy, Debra Conrad, Theresa Guins

Nursing Faculty Publications

Background and objective: Urinary tract infections (UTIs) are a common problem in pediatric urgent care medicine. There are multiple quality improvement (QI) projects related to the management of UTIs documented in the pediatric literature. We developed a project to decrease the prescribing of ultimately unneeded antibiotics for possible UTIs in a pediatric urgent care setting. A similar project has not been described in the pediatric literature.

Methods: We first reviewed the charts of patients presenting to a system of pediatric urgent care centers with a possible UTI over a 2-year period. We then launched a QI project with three plan, …

Heart Disease Prediction Using Stacking Model With Balancing Techniques And Dimensionality Reduction, Ayesha Noor, Nadeem Javaid, Nabil Alrajeh, Babar Mansoor, Ali Khaqan, Safdar Hussain Bouk

School of Cybersecurity Faculty Publications

Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart disease is one of the leading causes of mortality worldwide, early detection is crucial. Emerging technologies like Machine Learning (ML) are currently being actively used by the biomedical, healthcare, and health prediction industries. PaRSEL, a new stacking model is proposed in this research, that combines four classifiers, Passive Aggressive Classifier (PAC), Ridge Classifier (RC), Stochastic Gradient Descent Classifier (SGDC), and eXtreme Gradient Boosting (XGBoost), at the base layer, and LogitBoost is deployed for the final predictions at the meta layer. The imbalanced and irrelevant features in the …

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette

Electrical & Computer Engineering Faculty Publications

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …

Virtual Surgical Planning In Craniomaxillofacial Surgery: A Structured Review, Kaye Verlarde, Rentor Cafino, Armando Isla Jr., Karen Mae Ty, Xavier-Lewis Palmer, Lucas Potter, Larry Nadorra, Luchin Valrian Pueblos, Lemuel Clark Velasco

Electrical & Computer Engineering Faculty Publications

Craniomaxillofacial (CMF) surgery is a challenging and very demanding field that involves the treatment of congenital and acquired conditions of the face and head. Due to the complexity of the head and facial region, various tools and techniques were developed and utilized to aid surgical procedures and optimize results. Virtual Surgical Planning (VSP) has revolutionized the way craniomaxillofacial surgeries are planned and executed. It uses 3D imaging computer software to visualize and simulate a surgical procedure. Numerous studies were published on the usage of VSP in craniomaxillofacial surgery. However, the researchers found inconsistency in the previous literature which prompted the …