Digital Commons Network^™

Calloway County Bicentennial Celebration: A Panel Discussion

Jackson Purchase Historical Society Journal Archive

Calloway County Bicentennial Celebration: A Panel Discussion

Bill Mulligan, Bobby Smith Bryant, Gina Winchester, Randy Patterson, and Pat Seiber

"Ideal Press Work": The Contributions Of Kentucky Suffrage Press Superintendents To Public Relations History

Jackson Purchase Historical Society Journal Archive

"Ideal Press Work": The Contributions of Kentucky Suffrage Press Superintendents to Public Relations History

Melony Shemberger

Development Of A Latex Agglutination Test Based On Vh Antibody Fragment For Detection Of Streptococcus Suis Serotype 2, Kiratika Chidkoksung

Chulalongkorn University Theses and Dissertations (Chula ETD)

Streptococcus suis serotype 2 (S. suis 2) is an important porcine pathogen that causes diseases in both swine and humans. For rapid S. suis 2 identification, a novel latex agglutination test (LAT) based on heavy-chain variable domain antibody (VH) was developed. Firstly, the soluble 47B3 VH antibody fragment from a phage display library, in which cysteine residues were engineered at the C-terminus, was expressed in Escherichia coli. The purified protein was then gently reduced to form monomeric soluble 47B3 VH subsequently used to coat with latex beads by means of site-specific conjugation. The detection sensitivity was found to be as …

Exploring Academic Libraries' Core Values [Presentation Slides], Julia K. Nims

Faculty Scholarship 2023

Many academic libraries create mission and vision statements to communicate their purpose, priorities, and aspirations to both internal and external stakeholders. Core values, which express a library’s guiding principles or behaviors, are often included as part of these foundational documents. However, while there is already a significant and growing body of literature examining academic library mission and vision statements, such as their inclusion of specific library roles or functions, far less attention has been paid to libraries’ core values. To better understand these public declarations of convictions, the core values from 24 academic libraries in Michigan and Ohio were collected …

Development Of Sensors For Bacteria And Wound Environment Monitoring, Pannawich Thirabowonkitphithan

Chulalongkorn University Theses and Dissertations (Chula ETD)

Diabetes mellitus (DM) is a leading cause of death and reduces life expectancy. Complications from diabetes have wreaked havoc not only on patients' quality of life, but also on the global burdens of public health and socioeconomic development. The most frequent complication among patients with diabetes mellitus is diabetic wounds, particularly diabetic foot ulcers because of the damage to sensory nerves and blood vessel issues brought on by elevated blood glucose levels. As a result, chronic wounds eventually develop and are prone to infection. In this study, several novel analytical devices for the detection of wound infections and the wound …

Innate Immune Cell Dysfunction And Systemic Inflammation In Children With Chronic Liver Diseases Undergoing Transplantation, Krupa R Mysore, Sunil Kannanganat, Jeremy M Schraw, Philip J Lupo, John A Goss, Kenneth D R Setchell, Farrah Kheradmand, Xian C Li, Benjamin L Shneider

Faculty, Staff and Students Publications

Advanced liver diseases (ALD) can affect immune function and compromise host defense against infections. In this study, we examined the phenotypic and functional alterations in circulating monocyte and dendritic cells (DCs) in children with ALD undergoing liver transplantation (LT). Children were stratified into 2 clusters, C1 (mild) and C2 (severe), on the basis of laboratory parameters of ALD and compared with healthy pediatric controls. Children in C2 had a significant reduction in frequencies of nonclassical monocytes and myeloid DCs. Children in C2 displayed monocyte and DC dysfunction, characterized by lower human leucocyte antigen DR expression and reduced interleukin 12 production, …

Coevolution Combined With Molecular Dynamics Simulations Provides Structural And Mechanistic Insights Into The Interactions Between The Integrator Complex Subunits, Bernard Fongang, Yannick N Wadop, Yingjie Zhu, Eric J Wagner, Andrzej Kudlicki, Maga Rowicka

Faculty, Staff and Student Publications

Finding the 3D structure of large, multi-subunit complexes is difficult, despite recent advances in cryo-EM technology, due to remaining challenges to expressing and purifying subunits. Computational approaches that predict protein-protein interactions, including Direct Coupling Analysis (DCA), represent an attractive alternative for dissecting interactions within protein complexes. However, they are readily applicable only to small proteins due to high computational complexity and a high number of false positives. To solve this problem, we proposed a modified DCA approach, a powerful tool to predict the most likely interfaces of protein complexes. Since our modified approach cannot provide structural and mechanistic details of …

Leveraging Informative Missing Data To Learn About Acute Respiratory Distress Syndrome And Mortality In Long-Term Hospitalized Covid-19 Patients Throughout The Years Of The Pandemic, Emily Getzen, Amelia Lm Tan, Gabriel Brat, Gilbert S Omenn, Zachary Strasser, Qi Long, John H Holmes, Danielle Mowery

Faculty, Staff and Student Publications

Electronic health records (EHRs) contain a wealth of information that can be used to further precision health. One particular data element in EHRs that is not only under-utilized but oftentimes unaccounted for is missing data. However, missingness can provide valuable information about comorbidities and best practices for monitoring patients, which could save lives and reduce burden on the healthcare system. We characterize patterns of missing data in laboratory measurements collected at the University of Pennsylvania Hospital System from long-term COVID-19 patients and focus on the changes in these patterns between 2020 and 2021. We investigate how these patterns are associated …

Minimax Estimation For Personalized Federated Learning: An Alternative Between Fedavg And Local Training?, Shuxiao Chen, Qinqing Zheng, Qi Long, Weijie J Su

Faculty, Staff and Student Publications

A widely recognized difficulty in federated learning arises from the statistical heterogeneity among clients: local datasets often originate from distinct yet not entirely unrelated probability distributions, and personalization is, therefore, necessary to achieve optimal results from each individual's perspective. In this paper, we show how the excess risks of personalized federated learning using a smooth, strongly convex loss depend on data heterogeneity from a minimax point of view, with a focus on the FedAvg algorithm (McMahan et al., 2017) and pure local training (i.e., clients solve empirical risk minimization problems on their local datasets without any communication). Our main result …

A Query Engine For Self-Controlled Case Series, With An Application To Covid-19 Ehr Data, Xiaojin Li, Yan Huang, Licong Cui, Guo-Qiang Zhang

Faculty, Staff and Student Publications

Self-controlled case series (SCCS) is a statistical method in epidemiological study design that uses individuals as their own controls, with comparisons made within the same individuals at different time points of observation. SCCS has been applied in settings where it is difficult to identify comparison or control groups. To provide computational support for SCCS, we introduce a query engine called Self-Controlled Case Query (SCCQ) and use it to extract cohorts of self-controlled case series from a large-scale COVID-19 Electronic Health Records (EHR) dataset. Visual summary of the queried population through the R-Shiny visualization framework offers SCCQ's query result dashboard to …

Investigating Cellular Heterogeneity At The Single-Cell Level By The Flexible And Mobile Extrachromosomal Circular Dna, Jiajinlong Kang, Yulin Dai, Jinze Li, Huihui Fan, Zhongming Zhao

Faculty, Staff and Student Publications

Extrachromosomal circular DNA (eccDNA) is a special class of DNA derived from linear chromosomes. It coexists independently with linear chromosomes in the nucleus. eccDNA has been identified in multiple organisms, including Homo sapiens, and has been shown to play important roles relevant to tumor progression and drug resistance. To date, computational tools developed for eccDNA detection are only applicable to bulk tissue. Investigating eccDNA at the single-cell level using a computational approach will elucidate the heterogeneous and cell-type-specific landscape of eccDNA within cellular context. Here, we performed the first eccDNA analysis at the single-cell level using data generated by single-cell …

Contextual Variation Of Clinical Notes Induced By Ehr Migration, Kurt Miller, Sungrim Moon, Sunyang Fu, Hongfang Liu

Faculty, Staff and Student Publications

The structure and semantics of clinical notes vary considerably across different Electronic Health Record (EHR) systems, sites, and institutions. Such heterogeneity hampers the portability of natural language processing (NLP) models in extracting information from the text for clinical research or practice. In this study, we evaluate the contextual variation of clinical notes by measuring the semantic and syntactic similarity of the notes of two sets of physicians comprising four medical specialties across EHR migrations at two Mayo Clinic sites. We find significant semantic and syntactic variation imposed by the context of the EHR system and between medical specialties whereas only …

Digital Solutions Observed In Clinical Trials: A Formative Feasibility Scoping Review, Taylor M Harrison, Sungrim Moon, Liwei Wang, Sunyang Fu, Hongfang Liu

Faculty, Staff and Student Publications

Growing digital access accelerates digital transformation of clinical trials where digital solutions (DSs) are increasingly and widely leveraged for improving trial efficiency, effectiveness, and accessibility. Many factors impact DS success including technology barriers, privacy concerns, or user engagement activities. It is unclear how those factors are considered or reported in the literature. Here, we perform a formative feasibility scoping review to identify gaps impacting DS quality and reproducibility in trials. Articles containing digital terms published in English from 2009 to 2022 were collected (n=4,167). 130 articles published between 2016 and 2022 were randomly selected for full-text review. Eligible articles (n=100) …

Dental Claires: Contrastive Language Image Retrieval Search For Dental Research, Tanjida Kabir, Luyao Chen, Muhammad F Walji, Luca Giancardo, Xiaoqian Jiang, Shayan Shams

Faculty, Staff and Student Publications

Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The proposed framework,

The Health-Promoting Effects And The Mechanism Of Intermittent Fasting, Simin Liu, Min Zeng, Weixi Wan, Ming Huang, Xiang Li, Zixian Xie, Shang Wang, Yu Cai

Faculty, Staff and Student Publications

Intermittent fasting (IF) is an eating pattern in which individuals go extended periods with little or no energy intake after consuming regular food in intervening periods. IF has several health-promoting effects. It can effectively reduce weight, fasting insulin levels, and blood glucose levels. It can also increase the antitumor activity of medicines and cause improvement in the case of neurological diseases, such as memory deficit, to achieve enhanced metabolic function and prolonged longevity. Additionally, IF activates several biological pathways to induce autophagy, encourages cell renewal, prevents cancer cells from multiplying and spreading, and delays senescence. However, IF has specific adverse …

Emulate Randomized Clinical Trials Using Heterogeneous Treatment Effect Estimation For Personalized Treatments: Methodology Review And Benchmark, Yaobin Ling, Pulakesh Upadhyaya, Luyao Chen, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medicine is about the causal effects. The average treatment effect has long been studied as a measure of causal effect, assuming that all populations have the same effect size. However, no "one-size-fits-all" treatment seems to work in some complex diseases. Treatment effects may vary by patient. Estimating heterogeneous treatment effects (HTE) may have a high impact on developing personalized treatment. Lots of advanced machine learning models for estimating HTE have emerged in recent years, but there has been …

Covidanno, Covid-19 Annotation In Human, Yuzhou Feng, Mengyuan Yang, Zhiwei Fan, Weiling Zhao, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 19 (COVID-19), has caused a global health crisis. Despite ongoing efforts to treat patients, there is no universal prevention or cure available. One of the feasible approaches will be identifying the key genes from SARS-CoV-2-infected cells. SARS-CoV-2-infected in vitro model, allows easy control of the experimental conditions, obtaining reproducible results, and monitoring of infection progression. Currently, accumulating RNA-seq data from SARS-CoV-2 in vitro models urgently needs systematic translation and interpretation. To fill this gap, we built COVIDanno, COVID-19 annotation in humans, available at http://biomedbdc.wchscu.cn/COVIDanno/. The aim …

Text Classification Of Cancer Clinical Trial Eligibility Criteria, Yumeng Yang, Soumya Jayaraj, Ethan Ludmir, Kirk Roberts

Faculty, Staff and Student Publications

Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility are stated in natural language. A potential solution to this problem is to employ text classification methods for common types of eligibility criteria. In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained …

Use Gpt-J Prompt Generation With Roberta For Ner Models On Diagnosis Extraction Of Periodontal Diagnosis From Electronic Dental Records, Yao-Shun Chuang, Xiaoqian Jiang, Chun-Teh Lee, Ryan Brandon, Duong Tran, Oluwabunmi Tokede, Muhammad F Walji

Faculty, Staff and Student Publications

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the F1 score, in all settings after training with the RoBERTa model. …

A Novel Nih Research Grant Recommender Using Bert, Jie Zhu, Braja Gopal Patra, Hulin Wu, Ashraf Yaseen

Faculty, Staff and Student Publications

Research grants are important for researchers to sustain a good position in academia. There are many grant opportunities available from different funding agencies. However, finding relevant grant announcements is challenging and time-consuming for researchers. To resolve the problem, we proposed a grant announcements recommendation system for the National Institute of Health (NIH) grants using researchers' publications. We formulated the recommendation as a classification problem and proposed a recommender using state-of-the-art deep learning techniques: i.e. Bidirectional Encoder Representations from Transformers (BERT), to capture intrinsic, non-linear relationship between researchers' publications and grants announcements. Internal and external evaluations were conducted to assess the …

Data Mining Pipeline For Covid-19 Vaccine Safety Analysis Using A Large Electronic Health Record, Yan Huang, Xiaojin Li, Deepa Dongarwar, Hulin Wu, Guo-Qiang Zhang

Faculty, Staff and Student Publications

We developed a novel data mining pipeline that automatically extracts potential COVID-19 vaccine-related adverse events from a large Electronic Health Record (EHR) dataset. We applied this pipeline to Optum® de-identified COVID-19 EHR dataset containing COVID-19 vaccine records between December 11, 2020 and January 20, 2022. We compared post-vaccination diagnoses between the COVID-19 vaccine group and the influenza vaccine group among 553,682 individuals without COVID-19 infection. We extracted 1,414 ICD-10 diagnosis categories (first three ICD10 digits) within 180 days after the first dose of the COVID-19 vaccine. We then ranked the diagnosis codes using the adverse event rates and adjusted odds …

Transcriptomic Analyses Of Ovarian Clear-Cell Carcinoma With Concurrent Endometriosis, Kaitlyn E Collins, Xiyin Wang, Yuliya Klymenko, Noah B Davis, Maria C Martinez, Chi Zhang, Kaman So, Aaron Buechlein, Douglas B Rusch, Chad J Creighton, Shannon M Hawkins

Faculty, Staff and Students Publications

INTRODUCTION: Endometriosis, a benign inflammatory disease whereby endometrial-like tissue grows outside the uterus, is a risk factor for endometriosis-associated ovarian cancers. In particular, ovarian endometriomas, cystic lesions of deeply invasive endometriosis, are considered the precursor lesion for ovarian clear-cell carcinoma (OCCC).

METHODS: To explore this transcriptomic landscape, OCCC from women with pathology-proven concurrent endometriosis (n = 4) were compared to benign endometriomas (n = 4) by bulk RNA and small-RNA sequencing.

RESULTS: Analysis of protein-coding genes identified 2449 upregulated and 3131 downregulated protein-coding genes (DESeq2, P< 0.05, log2 fold-change > |1|) in OCCC with concurrent endometriosis compared to endometriomas. Gene set enrichment …

Machine Learning-Based Prediction Of Acute Mortality In Emergency Department Patients Using Twelve-Lead Electrocardiogram, Po-Cheng Chang, Zhi-Yong Liu, Yu-Chang Huang, Yu-Chun Hsu, Jung-Sheng Chen, Ching-Heng Lin, Richard Tsai, Chung-Chuan Chou, Ming-Shien Wen, Hung-Ta Wo, Wen-Chen Lee, Hao-Tien Liu, Chun-Chieh Wang, Chang-Fu Kuo

Faculty, Staff and Student Publications

BACKGROUND: The risk of mortality is relatively high among patients who visit the emergency department (ED), and stratifying patients at high risk can help improve medical care. This study aimed to create a machine-learning model that utilizes the standard 12-lead ECG to forecast acute mortality risk in ED patients.

METHODS: The database included patients who visited the EDs and underwent standard 12-lead ECG between October 2007 and December 2017. A convolutional neural network (CNN) ECG model was developed to classify survival and mortality using 12-lead ECG tracings acquired from 345,593 ED patients. For machine learning model development, the patients were …

Artificial Intelligence-Enabled Electrocardiographic Screening For Left Ventricular Systolic Dysfunction And Mortality Risk Prediction, Yu-Chang Huang, Yu-Chun Hsu, Zhi-Yong Liu, Ching-Heng Lin, Richard Tsai, Jung-Sheng Chen, Po-Cheng Chang, Hao-Tien Liu, Wen-Chen Lee, Hung-Ta Wo, Chung-Chuan Chou, Chun-Chieh Wang, Ming-Shien Wen, Chang-Fu Kuo

Faculty, Staff and Student Publications

BACKGROUND: Left ventricular systolic dysfunction (LVSD) characterized by a reduced left ventricular ejection fraction (LVEF) is associated with adverse patient outcomes. We aimed to build a deep neural network (DNN)-based model using standard 12-lead electrocardiogram (ECG) to screen for LVSD and stratify patient prognosis.

METHODS: This retrospective chart review study was conducted using data from consecutive adults who underwent ECG examinations at Chang Gung Memorial Hospital in Taiwan between October 2007 and December 2019. DNN models were developed to recognize LVSD, defined as LVEF

RESULTS: The mean age of patients in the testing dataset was 63.7 ± 16.3 years (46.3% …

Efficient Federated Kinship Relationship Identification, Xinyue Wang, Leonard Dervishi, Wentao Li, Xiaoqian Jiang, Erman Ayday, Jaideep Vaidya

Faculty, Staff and Student Publications

Kinship relationship estimation plays a significant role in today's genome studies. Since genetic data are mostly stored and protected in different silos, retrieving the desirable kinship relationships across federated data warehouses is a non-trivial problem. The ability to identify and connect related individuals is important for both research and clinical applications. In this work, we propose a new privacy-preserving kinship relationship estimation framework: Incremental Update Kinship Identification (INK). The proposed framework includes three key components that allow us to control the balance between privacy and accuracy (of kinship estimation): an incremental process coupled with the use of auxiliary information and …

Towards Fair Patient-Trial Matching Via Patient-Criterion Level Fairness Constraint, Chia-Yuan Chang, Jiayi Yuan, Sirui Ding, Qiaoyu Tan, Kai Zhang, Xiaoqian Jiang, Xia Hu, Na Zou

Faculty, Staff and Student Publications

Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To …

Large Language Models For Healthcare Data Augmentation: An Example On Patient-Trial Matching, Jiayi Yuan, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of large language models (LLMs) to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security …

Split Learning For Distributed Collaborative Training Of Deep Learning Models In Health Informatics, Zhuohang Li, Chao Yan, Xinmeng Zhang, Gharib Gharibi, Zhijun Yin, Xiaoqian Jiang, Bradley A Malin

Faculty, Staff and Student Publications

Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We …

Integrating Comorbidity Knowledge For Alzheimer's Disease Drug Repurposing Using Multi-Task Graph Neural Network, Ko-Hong Lin, Kang-Lin Hsieh, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

Alzheimer's Disease (AD) is a multifactorial disease that shares common etiologies with its multiple comorbidities, especially vascular diseases. To predict repurposable drugs for AD utilizing the relatively well-investigated comorbidities' knowledge, we proposed a multi-task graph neural network (GNN)-based pipeline that incorporates the corresponding biomedical interactome of these diseases with their genetic markers and effective therapeutics. Our pipeline can accurately capture the interactions and disease classification in the network. Next, we predicted drugs that might interact with the AD module by the node embedding similarity. Our candidates are mostly BBB permeable, and literature evidence showed their potential for treating AD pathologies, …

Local Contrastive Learning For Medical Image Recognition, Syed A Rizvi, Ruixiang Tang, Xiaoqian Jiang, Xiaotian Ma, Xia Hu

Faculty, Staff and Student Publications

The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Region Contrastive Learning (LRCLR), a flexible fine-tuning framework that adds layers for significant image region selection as well as cross-modality …