Medicine and Health Sciences Commons^™

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …

Identifying Patterns For Neurological Disabilities By Integrating Discrete Wavelet Transform And Visualization, Soo Yeon Ji, Sampath Jayarathna, Anne M. Perrotti, Katrina Kardiasmenos, Dong Hyun Jeong

Computer Science Faculty Publications

Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities …

Evidence Of Direct Interaction Between Cisplatin And The Caspase-Cleaved Prostate Apoptosis Response-4 Tumor Suppressor, Krishna K. Raut, Samjhana Pandey, Gyanendra Kharel, Steven M. Pascal

Chemistry & Biochemistry Faculty Publications

Prostate apoptosis response-4 (Par-4) tumor suppressor protein has gained attention as a potential therapeutic target owing to its unique ability to selectively induce apoptosis in cancer cells, sensitize them to chemotherapy and radiotherapy, and mitigate drug resistance. It has recently been reported that Par-4 interacts synergistically with cisplatin, a widely used anticancer drug. However, the mechanistic details underlying this relationship remain elusive. In this investigation, we employed an array of biophysical techniques, including circular dichroism spectroscopy, dynamic light scattering, and UV–vis absorption spectroscopy, to characterize the interaction between the active caspase-cleaved Par-4 (cl-Par-4) fragment and cisplatin. Additionally, elemental analysis was …

Triphlapan: Predicting Hla Molecules Binding Peptides Based On Triple Coding Matrix And Transfer Learning, Meng Wang, Chuqi Lei, Jianxin Wang, Yaohang Li, Min Li

Computer Science Faculty Publications

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding …

Infusing Machine Learning And Computational Linguistics Into Clinical Notes, Funke V. Alabi, Onyeka Omose, Omotomilola Jegede

Mathematics & Statistics Faculty Publications

Entering free-form text notes into Electronic Health Records (EHR) systems takes a lot of time from clinicians. A large portion of this paper work is viewed as a burden, which cuts into the amount of time doctors spend with patients and increases the risk of burnout. We will see how machine learning and computational linguistics can be infused in the processing of taking clinical notes. We are presenting a new language modeling task that predicts the content of notes conditioned on historical data from a patient's medical record, such as patient demographics, lab results, medications, and previous notes, with the …

Quantification Of Antiviral Drug Tenofovir (Tfv) By Surface-Enhanced Raman Spectroscopy (Sers) Using Cumulative Distribution Functions (Cdfs), Marguerite R. Butler, Jana Hrncirova, Meredith Clark, Sucharita Dutta, John B. Cooper

Chemistry & Biochemistry Faculty Publications

Surface-enhanced Raman spectroscopy (SERS) is an ultrasensitive spectroscopic technique that generates signal-enhanced fingerprint vibrational spectra of small molecules. However, without rigorous control of SERS substrate active sites, geometry, surface area, or surface functionality, SERS is notoriously irreproducible, complicating the consistent quantitative analysis of small molecules. While evaporatively prepared samples yield significant SERS enhancement resulting in lower detection limits, the distribution of these enhancements along the SERS surface is inherently stochastic. Acquiring spatially resolved SERS spectra of these dried surfaces, we have shown that this enhancement is governed by a power law as a function of analyte concentration. Consequently, by definition, …

Predicting The Need For Cardiovascular Surgery: A Comparative Study Of Machine Learning Models, Arman Ghavidel, Pilar Pazos, Rolando Del Aguila Suarez, Alireza Atashi

Engineering Management & Systems Engineering Faculty Publications

This research examines the efficacy of ensemble Machine Learning (ML) models, mainly focusing on Deep Neural Networks (DNNs), in predicting the need for cardiovascular surgery, a critical aspect of clinical decision-making. It addresses key challenges such as class imbalance, which is pivotal in healthcare settings. The research involved a comprehensive comparison and evaluation of the performance of previously published ML methods against a new Deep Learning (DL) model. This comparison utilized a dataset encompassing 50,000 patient records from a large hospital between 2015-2022. The study proposes enhancing the efficacy of these models through feature selection and hyperparameter optimization, employing techniques …

Metals And Metal Complexes In Diseases With A Focus On Covid-19: Facts And Opinions, Agnieszka Ścibior, Manuel Aureliano, Alvin A. Holder, Juan Llopis

Chemistry & Biochemistry Faculty Publications

In the present Special Issue on “Metals and Metal Complexes in Diseases with a Focus on COVID-19: Facts and Opinions”, an attempt has been made to include reports updating our knowledge of elements considered to be potential candidates for therapeutic applications and certain metal-containing species, which are extensively being examined towards their potential biomedical use due to their specific physicochemical properties. The Special Issue compiles data on the role of metals in COVID-19 and focuses on other illnesses and biological processes that affect metal metabolism. It consists of eight manuscripts, including five review articles and three original research papers (Figure …

An Efficient Lightweight Provably Secure Authentication Protocol For Patient Monitoring Using Wireless Medical Sensor Networks, Garima Thakur, Sunil Prajapat, Pankaj Kumar, Ashok Kumar Das, Sachin Shetty

VMASC Publications

The refurbishing of conventional medical network with the wireless medical sensor network has not only amplified the efficiency of the network but concurrently posed different security threats. Previously, Servati and Safkhani had suggested an Internet of Things (IoT) based authentication scheme for the healthcare environment promulgating a secure protocol in resistance to several attacks. However, the analysis demonstrates that the protocol could not withstand user, server, and gateway node impersonation attacks. Further, the protocol fails to resist offline password guessing, ephemeral secret leakage, and gateway-by-passing attacks. To address the security weaknesses, we furnish a lightweight three-factor authentication framework employing the …

Architectural Design Of A Blockchain-Enabled, Federated Learning Platform For Algorithmic Fairness In Predictive Health Care: Design Science Study, Xueping Liang, Juan Zhao, Yan Chen, Eranga Bandara, Sachin Shetty

VMASC Publications

Background: Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site.

Objective: This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead …

The Use Of Artificial Intelligence To Detect Students Sentiments And Emotions In Gross Anatomy Reflections, Krzysztof J. Rechowicz, Carrie A. Elzie

VMASC Publications

Students' reflective writings in gross anatomy provide a rich source of complex emotions experienced by learners. However, qualitative approaches to evaluating student writings are resource heavy and timely. To overcome this, natural language processing, a nascent field of artificial intelligence that uses computational techniques for the analysis and synthesis of text, was used to compare health professional students' reflections on the importance of various regions of the body to their own lives and those of the anatomical donor dissected. A total of 1365 anonymous writings (677 about a donor, 688 about self) were collected from 132 students. Binary and trinary …

The Shortfalls Of Vulnerability Indexes For Public Health Decision-Making In The Face Of Emergent Crises: The Case Of Covid-19 Vaccine Uptake In Virginia, Lydia Cleveland Sa, Erika Frydenlund

VMASC Publications

Equitable and effective vaccine uptake is a key issue in addressing COVID-19. To achieve this, we must comprehensively characterize the context-specific socio-behavioral and structural determinants of vaccine uptake. However, to quickly focus public health interventions, state agencies and planners often rely on already existing indexes of "vulnerability." Many such "vulnerability indexes" exist and become benchmarks for targeting interventions in wide ranging scenarios, but they vary considerably in the factors and themes that they cover. Some are even uncritical of the use of the word "vulnerable," which should take on different meanings in different contexts. The objective of this study is …

Multipatch Stochastic Epidemic Model For The Dynamics Of A Tick-Borne Disease, Milliward Maliyoni, Holly D. Gaff, Keshlan S. Govinder, Faraimunashe Chirove

Biological Sciences Faculty Publications

Spatial heterogeneity and migration of hosts and ticks have an impact on the spread, extinction and persistence of tick-borne diseases. In this paper, we investigate the impact of between-patch migration of white-tailed deer and lone star ticks on the dynamics of a tick-borne disease with regard to disease extinction and persistence using a system of Itô stochastic differential equations model. It is shown that the disease-free equilibrium exists and is unique. The general formula for computing the basic reproduction number for all patches is derived. We show that for patches in isolation, the basic reproduction number is equal to the …

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 …

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 …

Readiness For Transfer: A Mixed-Methods Study On Icu Transfers Of Care, Soo-Hoon Lee, Clarice Wee, Phillip Phan, Yanika Kowitlawakul, Chee-Kiat Tan, Amartya Mukhopadhyay

Management Faculty Publications

Objective Past studies on intensive care unit (ICU) patient transfers compare the efficacy of using standardised checklists against unstructured communications. Less studied are the experiences of clinicians in enacting bidirectional (send/receive) transfers. This study reports on the differences in protocols and data elements between receiving and sending transfers in the ICU, and the elements constituting readiness for transfer.

Methods Mixed-methods study of a 574-bed general hospital in Singapore with a 74-bed ICU for surgical and medical patients. Six focus group discussions (FGDs) with 34 clinicians comprising 15 residents and 19 nurses, followed by a structured questionnaire survey of 140 clinicians …

Health Care Equity Through Intelligent Edge Computing And Augmented Reality/Virtual Reality: A Systematic Review, Vishal Lakshminarayanan, Aswathy Ravikumar, Harini Sriraman, Sujatha Alla, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this …

Ultrasensitive Tapered Optical Fiber Refractive Index, Erem Ujah, Meimei Lai, Gymama Slaughter

Electrical & Computer Engineering Faculty Publications

Refractive index (RI) sensors are of great interest for label-free optical biosensing. A tapered optical fiber (TOF) RI sensor with micron-sized waist diameters can dramatically enhance sensor sensitivity by reducing the mode volume over a long distance. Here, a simple and fast method is used to fabricate highly sensitive refractive index sensors based on localized surface plasmon resonance (LSPR). Two TOFs (l = 5 mm) with waist diameters of 5 µm and 12 µm demonstrated sensitivity enhancement at λ = 1559 nm for glucose sensing (5-45 wt%) at room temperature. The optical power transmission decreased with increasing glucose concentration due …

Adaptive Critic Network For Person Tracking Using 3d Skeleton Data, Joseph G. Zalameda, Alex Glandon, Khan M. Iftekharuddin, Mohammad S. Alam (Ed.), Vijayan K. Asari (Ed.)

Electrical & Computer Engineering Faculty Publications

Analysis of human gait using 3-dimensional co-occurrence skeleton joints extracted from Lidar sensor data has been shown a viable method for predicting person identity. The co-occurrence based networks rely on the spatial changes between frames of each joint in the skeleton data sequence. Normally, this data is obtained using a Lidar skeleton extraction method to estimate these co-occurrence features from raw Lidar frames, which can be prone to incorrect joint estimations when part of the body is occluded. These datasets can also be time consuming and expensive to collect and typically offer a small number of samples for training and …

Comparison Of Machine Learning Methods For Classification Of Alexithymia In Individuals With And Without Autism From Eye-Tracking Data, Furkan Iigin, Megan A. Witherow, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Alexithymia describes a psychological state where individuals struggle with feeling and expressing their emotions. Individuals with alexithymia may also have a more difficult time understanding the emotions of others and may express atypical attention to the eyes when recognizing emotions. This is known to affect individuals with Autism Spectrum Disorder (ASD) differently than neurotypical (NT) individuals. Using a public data set of eye-tracking data from seventy individuals with and without autism who have been assessed for alexithymia, we train multiple traditional machine learning models for alexithymia classification including support vector machines, logistic regression, decision trees, random forest, and multilayer perceptron. …

An Explainable Artificial Intelligence Framework For The Predictive Analysis Of Hypo And Hyper Thyroidism Using Machine Learning Algorithms, Md. Bipul Hossain, Anika Shama, Apurba Adhikary, Avi Deb Raha, K. M. Aslam Uddin, Mohammad Amzad Hossain, Imtia Islam, Saydul Akbar Murad, Md. Shirajum Munir, Anupam Kumur Bairagi

Electrical & Computer Engineering Faculty Publications

The thyroid gland is the crucial organ in the human body, secreting two hormones that help to regulate the human body's metabolism. Thyroid disease is a severe medical complaint that could be developed by high Thyroid Stimulating Hormone (TSH) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two critical conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Machine learning models can be used to precisely process the data generated from different medical sectors and to build a model to predict several diseases. In this paper, we use different machine-learning algorithms to …

Modeling The Spread Of Covid-19 In Spatio-Temporal Context, S.H. Sathish Indika, Norou Diawara, Hueiwang Anna Jeng, Bridget D. Giles, Dilini S.K. Gamage

Mathematics & Statistics Faculty Publications

This study aims to use data provided by the Virginia Department of Public Health to illustrate the changes in trends of the total cases in COVID-19 since they were first recorded in the state. Each of the 93 counties in the state has its COVID-19 dashboard to help inform decision makers and the public of spatial and temporal counts of total cases. Our analysis shows the differences in the relative spread between the counties and compares the evolution in time using Bayesian conditional autoregressive framework. The models are built under the Markov Chain Monte Carlo method and Moran spatial correlations. …

Biophysical Interactions Control The Progression Of Harmful Algal Blooms In Chesapeake Bay: A Novel Lagrangian Particle Tracking Model With Mixotrophic Growth And Vertical Migration, Jilian Xiong, Jian Shen, Qubin Qin, Michelle C. Tomlinson, Yinglong J. Zhang, Xun Cai, Fei Yi, Linlin Cui, Margaret R. Mulholland

OES Faculty Publications

Climate change and nutrient pollution contribute to the expanding global footprint of harmful algal blooms. To better predict their spatial distributions and disentangle biophysical controls, a novel Lagrangian particle tracking and biological (LPT-Bio) model was developed with a high-resolution numerical model and remote sensing. The LPT-Bio model integrates the advantages of Lagrangian and Eulerian approaches by explicitly simulating algal bloom dynamics, algal biomass change, and diel vertical migrations along predicted trajectories. The model successfully captured the intensity and extent of the 2020 Margalefidinium polykrikoides bloom in the lower Chesapeake Bay and resolved fine-scale structures of bloom patchiness, demonstrating a reliable …

Halogen Bonding Interactions Of Haloaromatic Endocrine Disruptors And The Potential For Inhibition Of Iodothyronine Deiodinases, Craig A. Bayse

Chemistry & Biochemistry Faculty Publications

Halogen bonding (XB) is a potential mechanism for the inhibition of the thyroid-activating/deactivating iodothyronine deiodinase family of selenoproteins through interactions with halogenated endocrine disrupting compounds (EDCs). Trends in XB interactions were examined using density functional theory for a series of polyhalogenated dibenzo-1,4-dioxins, biphenyls, and other EDCs with methylselenolate, a simple model of the Dio active site selenocysteine. The strengths of the interactions depend upon the halogen (Br>Cl), the degree of substitution, and the position of the acceptor. In terms of donor-acceptor energies, interactions at the meta position are often the strongest, suggesting a link to the topology of THs, …

Para-Methoxybenzylidene Acetal-Protected D-Glucosamine Derivatives As Ph-Responsive Gelators And Their Applications For Drug Delivery, Jonathan Bietsch, Logan Baker, Anna Duffney, Alice Mao, Mary Foutz, Cheandri Ackermann, Guijun Wang

Chemistry & Biochemistry Faculty Publications

Carbohydrate-based low molecular weight gelators (LMWGs) are compounds with the capability to self-assemble into complex molecular networks within a solvent, leading to solvent immobilization. This process of gel formation depends on noncovalent interactions, including Van der Waals, hydrogen bonding, and π–π stacking. Due to their potential applications in environmental remediation, drug delivery, and tissue engineering, these molecules have emerged as an important area of research. In particular, various 4,6-O-benzylidene acetal-protected D-glucosamine derivatives have shown promising gelation abilities. In this study, a series of C-2-carbamate derivatives containing a para-methoxy benzylidene acetal functional group were synthesized and characterized. These compounds exhibited good …

Synthesis Of A Series Of Trimeric Branched Glycoconjugates And Their Applications For Supramolecular Gels And Catalysis, Jonathan Bietsch, Anji Chen, Dan Wang, Guijun Wang

Chemistry & Biochemistry Faculty Publications

Carbohydrate-derived molecular gelators have found many practical applications as soft materials. To better understand the structure and molecular gelation relationship and further explore the applications of sugar-based gelators, we designed and synthesized eight trimeric branched sugar triazole derivatives and studied their self-assembling properties. These included glucose, glucosamine, galactose, and maltose derivatives. Interestingly, the gelation properties of these compounds exhibited correlations with the peripheral sugar structures. The maltose derivative did not form gels in the tested solvents, but all other compounds exhibited gelation properties in at least one of the solvents. Glucose derivatives showed superior performance, followed by glucosamine derivatives. They …

Synthesis And Self-Assembling Properties Of Carbohydrate- And Diarylethene-Based Photoswitchable Molecular Gelators, Pramod Aryal, Joedian Morris, Surya B. Adhikari, Jonathan Bietsch, Guijun Wang

Chemistry & Biochemistry Faculty Publications

Carbohydrate-based low-molecular-weight gelators are interesting new materials with many potential applications. These compounds can be designed to include multiple stimuli-responsive functional groups. In this study, we designed and synthesized several chemically responsive bola-glycolipids and dimeric carbohydrate- and diarylethene-based photoswitchable derivatives. The dimeric glycolipids formed stable gels in a variety of solvent systems. The best performing gelators in this series contained decanedioic and dithienylethene (DTE) spacers, which formed gels in eight and nine of the tested solvents, respectively. The two new DTE-containing esters possessed interesting photoswitching properties and DTE derivative 7 was found to have versatile gelation properties in many solvents, …

Extracting High-Molecular Weight Dna From Cyanobacteria Using Promega's Wizard® Hmw Dna Extraction Kit With A Modified Protocol, Metis, Megan A. Hept, Lesley H. Greene

Chemistry & Biochemistry Faculty Publications

Extraction of high molecular weight (HMW) DNA for long read sequencing with little to no fragmentation and high purity is difficult to acquire from cyanobacterial species. Here we describe a modified method of extraction using Promega's Wizard® HMW DNA Extraction Kit to acquire high molecular weight DNA from cyanobacterial species. The protocol used in the kit is the “3.D. Isolating HMW DNA from Gram-Positive and Gram-Negative Bacteria” protocol. During a key step in the protocol, the lingering remnants of the mucilage layer of the cyanobacterial species is removed, preventing it from sticking to the DNA pellet produced. This customized modification …

Sound The (Smaller) Alarm: The Triphosphate Magic Spot Nucleotide Pgpp, Areej Malik, Megan A. Hept, Erin B. Purcell

Chemistry & Biochemistry Faculty Publications

It has recently become evident that the bacterial stringent response is regulated by a triphosphate alarmone (pGpp) as well as the canonical tetra- and pentaphosphate alarmones ppGpp and pppGpp [together, (p)ppGpp]. Often dismissed in the past as an artifact or degradation product, pGpp has been confirmed as a deliberate endpoint of multiple synthetic pathways utilizing GMP, (p)ppGpp, or GDP/GTP as precursors. Some early studies concluded that pGpp functionally mimics (p)ppGpp and that its biological role is to make alarmone metabolism less dependent on the guanine energy charge of the cell by allowing GMP-dependent synthesis to continue when GDP/GTP has been …

Enhancing The Conformational Stability Of The Cl-Par-4 Tumor Suppressor Via Site-Directed Mutagenesis, Samjhana Pandey, Krishna K. Raut, Andrea M. Clark, Antoine Baudin, Lamya Djemri, David S. Libich, Komala Ponniah, Steven M. Pascal

Chemistry & Biochemistry Faculty Publications

Intrinsically disordered proteins play important roles in cell signaling, and dysregulation of these proteins is associated with several diseases. Prostate apoptosis response-4 (Par-4), an approximately 40 kilodalton proapoptotic tumor suppressor, is a predominantly intrinsically disordered protein whose downregulation has been observed in various cancers. The caspase-cleaved fragment of Par-4 (cl-Par-4) is active and plays a role in tumor suppression by inhibiting cell survival pathways. Here, we employed site-directed mutagenesis to create a cl-Par-4 point mutant (D313K). The expressed and purified D313K protein was characterized using biophysical techniques, and the results were compared to that of the wild-type (WT). We have …