Digital Commons Network^™

Interpreting The Biological Effects Of Protons As A Function Of Physical Quantity: Linear Energy Transfer Or Microdosimetric Lineal Energy Spectrum?, Fada Guan, Lawrence Bronk, Matthew Kerr, Yuting Li, Leslie A Braby, Mary Sobieski, Xiaochun Wang, Xiaodong Zhang, Clifford Stephan, David R Grosshans, Radhe Mohan

Faculty, Staff and Student Publications

The choice of appropriate physical quantities to characterize the biological effects of ionizing radiation has evolved over time coupled with advances in scientific understanding. The basic hypothesis in radiation dosimetry is that the energy deposited by ionizing radiation initiates all the consequences of exposure in a biological sample (e.g., DNA damage, reproductive cell death). Physical quantities defined to characterize energy deposition have included dose, a measure of the mean energy imparted per unit mass of the target, and linear energy transfer (LET), a measure of the mean energy deposition per unit distance that charged particles traverse in a medium. The …

Professional Development: A Mixed Methods Study Of Masters Of Public Health Alumni, Orna Baron-Epel, Yana Douvdevany, Dana Ivancovsky-Wajcman, Paul Barach, Osnat Bashkin, Katarzyna Czabanowska, Keren Dopelt, Nadav Davidovitch, Szczepan Jakubowski, Fiona Macleod, Maureen Malowany, Leah Okenwa-Emegwa, Maya Peled-Raz, Shira Zelber-Sagi

College of Population Health Faculty Papers

INTRODUCTION: We examined the perceptions of the Master of Public Health (MPH) degree graduates regarding their personal competencies, job performance and professional development using a mixed method, explanatory sequential design.

METHODS: A cross-sectional, self-administered questionnaire of the Haifa School of Public Health alumni who graduated between 2005 and 2022 was disseminated to 849 graduates between March and June 2022, from which 127 responded (response rate: 14.90%). This was followed by 24 in-depth interviews with alumni from the same sample (conducted between November 2022 and March 2023).

RESULTS: The sample included 74.8% of females with a mean age of 40.7 years, …

Α4 Nicotinic Acetylcholine Receptors In Lipopolysaccharide-Related Lung Inflammation, J. Ritzenthaler, Walter H. Watson, Jesse Roman

Division of Pulmonary, Allergy, and Critical Care Medicine Faculty Papers

Sepsis remains an important healthcare challenge. The lungs are often affected in sepsis, resulting in acute lung injury characterized by inflammation. Mechanisms involving lipopolysaccharide (LPS) stimulation of toll-like receptor (TLR) signaling with induction of proinflammatory pathways have been implicated in this process. To date, however, studies targeting these pathways have failed to improve outcomes. We have found that LPS may also promote lung injury through the activation of α4 nicotinic acetylcholine receptors (α4 nAChRs) in immune cells. We observed increased expression of α4 nAChRs in human THP-1 monocytic cells exposed to LPS (100 ng/mL, 24 h). We also observed that …

Characterizing The Progression From Mild Cognitive Impairment To Dementia: A Network Analysis Of Longitudinal Clinical Visits, Muskan Garg, Sara Hejazi, Sunyang Fu, Maria Vassilaki, Ronald C Petersen, Jennifer St Sauver, Sunghwan Sohn

Faculty, Staff and Student Publications

Background: With the recent surge in the utilization of electronic health records for cognitive decline, the research community has turned its attention to conducting fine-grained analyses of dementia onset using advanced techniques. Previous works have mostly focused on machine learning-based prediction of dementia, lacking the analysis of dementia progression and its associations with risk factors over time. The black box nature of machine learning models has also raised concerns regarding their uncertainty and safety in decision making, particularly in sensitive domains like healthcare.

Objective: We aimed to characterize the progression of health conditions, such as chronic diseases and neuropsychiatric symptoms, …

Immuneapp For Hla-I Epitope Prediction And Immunopeptidome Analysis, Haodong Xu, Ruifeng Hu, Xianjun Dong, Lan Kuang, Wenchao Zhang, Chao Tu, Zhihong Li, Zhongming Zhao

Faculty, Staff and Student Publications

Advances in mass spectrometry accelerates the characterization of HLA ligandome, necessitating the development of efficient methods for immunopeptidomics analysis and (neo)antigen prediction. We develop ImmuneApp, an interpretable deep learning framework trained on extensive HLA ligand datasets, which improves the prediction of HLA-I epitopes, prioritizes neoepitopes, and enhances immunopeptidomics deconvolution. ImmuneApp extracts informative embeddings and identifies key residues for pHLA binding. We also present a more accurate model-based deconvolution approach and systematically analyzed 216 multi-allelic immunopeptidomics samples, identifying 835,551 ligands restricted to over 100 HLA-I alleles. Our investigation reveals the effectiveness of the composite model, denoted as ImmuneApp-MA, which integrates mono- …

Multivariable Mendelian Randomization With Incomplete Measurements On The Exposure Variables In The Hispanic Community Health Study/Study Of Latinos, Yilun Li, Kin Yau Wong, Annie Green Howard, Penny Gordon-Larsen, Heather M Highland, Mariaelisa Graff, Kari E North, Carolina G Downie, Christy L Avery, Bing Yu, Kristin L Young, Victoria L Buchanan, Robert Kaplan, Lifang Hou, Brian Thomas Joyce, Qibin Qi, Tamar Sofer, Jee-Young Moon, Dan-Yu Lin

Faculty, Staff and Student Publications

Multivariable Mendelian randomization allows simultaneous estimation of direct causal effects of multiple exposure variables on an outcome. When the exposure variables of interest are quantitative omic features, obtaining complete data can be economically and technically challenging: the measurement cost is high, and the measurement devices may have inherent detection limits. In this paper, we propose a valid and efficient method to handle unmeasured and undetectable values of the exposure variables in a one-sample multivariable Mendelian randomization analysis with individual-level data. We estimate the direct causal effects with maximum likelihood estimation and develop an expectation-maximization algorithm to compute the estimators. We …

Gender And Sexuality Awareness In Medical Education And Practice: Mixed Methods Study, Rola Khamisy-Farah, Eden Biras, Rabie Shehadeh, Ruba Tuma, Hisham Atwan, Anna Siri, Manlio Converti, Francesco Chirico, Łukasz Szarpak, Carlo Biz, Raymond Farah, Nicola Bragazzi

Faculty, Staff and Students Publications

BACKGROUND: The integration of gender and sexuality awareness in health care is increasingly recognized as vital for patient outcomes. Despite this, there is a notable lack of comprehensive data on the current state of physicians' training and perceptions in these areas, leading to a gap in targeted educational interventions and optimal health care delivery.

OBJECTIVE: The study's aim was to explore the experiences and perceptions of attending and resident physicians regarding the inclusion of gender and sexuality content in medical school curricula and professional practice in Israel.

METHODS: This cross-sectional survey targeted a diverse group of physicians across various specializations …

A New Era Of Data-Driven Cancer Research And Care: Opportunities And Challenges, Felicia Gomez, Arpad M Danos, Joshua F Mcmichael, Malachi Griffith, Obi L Griffith, Et Al.

2020-Current year OA Pubs

People diagnosed with cancer and their formal and informal caregivers are increasingly faced with a deluge of complex information, thanks to rapid advancements in the type and volume of diagnostic, prognostic, and treatment data. This commentary discusses the opportunities and challenges that the society faces as we integrate large volumes of data into regular cancer care.

Calibrating Tumor Growth And Invasion Parameters With Spectral Spatial Analysis Of Cancer Biopsy Tissues, Stefano Pasetto, Michael Montejo, Mohammad U Zahid, Marilin Rosa, Robert Gatenby, Pirmin Schlicke, Roberto Diaz, Heiko Enderling

Faculty, Staff and Student Publications

The reaction-diffusion equation is widely used in mathematical models of cancer. The calibration of model parameters based on limited clinical data is critical to using reaction-diffusion equation simulations for reliable predictions on a per-patient basis. Here, we focus on cell-level data as routinely available from tissue biopsies used for clinical cancer diagnosis. We analyze the spatial architecture in biopsy tissues stained with multiplex immunofluorescence. We derive a two-point correlation function and the corresponding spatial power spectral distribution. We show that this data-deduced power spectral distribution can fit the power spectrum of the solution of reaction-diffusion equations that can then identify …

Communities That Heal Intervention And Mortality Including Polysubstance Overdose Deaths: A Randomized Clinical Trial, Bridget Freisthler, Rouba A. Chahine, Jennifer Villani, Redonna Chandler, Daniel J. Feaster, Svetla Slavova, Jolene Defiore-Hyrmer, Alexander Y. Walley, Sarah Kosakowski, Arnie Aldridge, Carolina Barbosa, Sabana Bhatta, Candace Brancato, Carly Bridden, Mia Christopher, Tom Clarke, James David, Lauren D'Costa, Irene Ewing, Soledad Fernandez, Erin Gibson, Louisa Gilbert, Megan E. Hall, Sarah Hargrove, Timothy Hunt, Elizabeth N. Kinnard, Lauren Larochelle, Aaron Macoubray, Shawn R. Nigam, Edward V. Nunes, Carrie B. Oser, Sharon Pagnano, Peter J. Rock, Pamela Salsberry, Aimee Shadwick, Thomas J. Stopka, Sylvia Tan, Jessica L. Taylor, Philip M. Westgate, Elwin Wu, Gary A. Zarkin, Sharon L. Walsh, Nabila El-Bassel, T. John Winhusen, Jeffrey H. Samet, Emmanuel A. Oga

Biostatistics Faculty Publications

IMPORTANCE: The HEALing Communities Study (HCS) evaluated the effectiveness of the Communities That HEAL (CTH) intervention in preventing fatal overdoses amidst the US opioid epidemic.

OBJECTIVE: To evaluate the impact of the CTH intervention on total drug overdose deaths and overdose deaths involving combinations of opioids with psychostimulants or benzodiazepines.

DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial was a parallel-arm, multisite, community-randomized, open, and waitlisted controlled comparison trial of communities in 4 US states between 2020 and 2023. Eligible communities were those reporting high opioid overdose fatality rates in Kentucky, Massachusetts, New York, and Ohio. Covariate constrained randomization stratified …

A Guideline For Open-Source Tools To Make Medical Imaging Data Ready For Artificial Intelligence Applications: A Society Of Imaging Informatics In Medicine (Siim) Survey, Sanaz Vahdati, Bardia Khosravi, Elham Mahmoudi, Kuan Zhang, Pouria Rouzrokh, Shahriar Faghani, Mana Moassefi, Aylin Tahmasebi, Katherine Andriole, Peter Chang, Keyvan Farahani, Mona Flores, Les Folio, Sina Houshmand, Maryellen Giger, Judy Gichoya, Bradley Erickson

Department of Radiology Faculty Papers

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora …

Challenges And Opportunities For Early Phase Clinical Trials Of Novel Drug-Radiotherapy Combinations: Recommendations From Nrg Oncology, The American Society For Radiation Oncology (Astro), The American College Of Radiology (Acr), The Sarah Cannon Research Institute, And The American College Of Radiation Oncology (Acro), Zachary S Zumsteg, Siddharth Sheth, Salma K Jabbour, Krishnan R Patel, Randall J Kimple, Terence M Williams, Meng Xu-Welliver, Pedro A Torres-Saavedra, Arta M Monjazeb, Jyoti Mayadev, Steven E Finkelstein, John M Buatti, Sandip P Patel, Steven H Lin

Faculty, Staff and Student Publications

NRG Oncology's Developmental Therapeutics and Radiation Therapy Subcommittee assembled an interdisciplinary group of investigators to address barriers to successful early phase clinical trials of novel combination therapies involving radiation. This Policy Review elucidates some of the many challenges associated with study design for early phase trials combining radiotherapy with novel systemic agents, which are distinct from drug-drug combination development and are often overlooked. We also advocate for potential solutions that could mitigate or eliminate some of these barriers, providing examples of specific clinical trial designs that could help facilitate efficient and effective evaluation of novel drug-radiotherapy combinations.

Vessel-Targeted Compensation Of Deformable Motion In Interventional Cone-Beam Ct, Alexander Lu, Heyuan Huang, Yicheng Hu, Wojciech Zbijewski, Mathias Unberath, Jeffrey H Siewerdsen, Clifford R Weiss, Alejandro Sisniega

Faculty, Staff and Student Publications

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior …

Human Embryonic Genetic Mosaicism And Its Effects On Development And Disease, Sarah M Waldvogel, Jennifer E Posey, Margaret A Goodell

Center on Aging Staff Publications

Nearly every mammalian cell division is accompanied by a mutational event that becomes fixed in a daughter cell. When carried forward to additional cell progeny, a clone of variant cells can emerge. As a result, mammals are complex mosaics of clones that are genetically distinct from one another. Recent high-throughput sequencing studies have revealed that mosaicism is common, clone sizes often increase with age and specific variants can affect tissue function and disease development. Variants that are acquired during early embryogenesis are shared by multiple cell types and can affect numerous tissues. Within tissues, variant clones compete, which can result …

Stareg: Statistical Replicability Analysis Of High Throughput Experiments With Applications To Spatial Transcriptomic Studies, Yan Li, Xiang Zhou, Rui Chen, Xianyang Zhang, Hongyuan Cao

Faculty, Staff and Students Publications

Replicable signals from different yet conceptually related studies provide stronger scientific evidence and more powerful inference. We introduce STAREG, a statistical method for replicability analysis of high throughput experiments, and apply it to analyze spatial transcriptomic studies. STAREG uses summary statistics from multiple studies of high throughput experiments and models the the joint distribution of p-values accounting for the heterogeneity of different studies. It effectively controls the false discovery rate (FDR) and has higher power by information borrowing. Moreover, it provides different rankings of important genes. With the EM algorithm in combination with pool-adjacent-violator-algorithm (PAVA), STAREG is scalable to datasets …

Polycaprolactone-Based Shape Memory Foams As Self-Fitting Vaginal Stents, Ashley J Hicks, Courteney Roberts, Andrew Robinson, Kailey Wilson, Varsha Kotamreddy, Trace Larue, Arian Veyssi, Felipe Beltran, Julie Hakim, Manuel K Rausch, Melissa Grunlan, Elizabeth Cosgriff-Hernandez

Faculty, Staff and Students Publications

There is an urgent critical need for a patient-forward vaginal stent that can prevent debilitating vaginal stenosis that occurs after pelvic radiation treatments and vaginal reconstruction. To this end, we developed a self-fitting vaginal stent based on a shape-memory polymer (SMP) foam that can assume a secondary, compressed shape for ease of deployment. Upon insertion, the change in temperature and hydration initiates foam expansion to shape fit to the individual patient and restore the lumen of the stent to allow egress of vaginal secretions. To achieve rapid actuation at physiological temperature, we investigated the effect of architecture of two photocurable, …

Body Mass Index And Postacute Sequelae Of Sars-Cov-2 Infection In Children And Young Adults, Ting Zhou, Chongliang Luo, Et Al.

2020-Current year OA Pubs

IMPORTANCE: Obesity is associated with increased severity of COVID-19. Whether obesity is associated with an increased risk of post-acute sequelae of SARS-CoV-2 infection (PASC) among pediatric populations, independent of its association with acute infection severity, is unclear.

OBJECTIVE: To quantify the association of body mass index (BMI) status before SARS-CoV-2 infection with pediatric PASC risk, controlling for acute infection severity.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study occurred at 26 US children's hospitals from March 2020 to May 2023 with a minimum follow-up of 179 days. Eligible participants included children and young adults aged 5 to 20 years with …

Nongenetic And Genetic Factors Associated With White Matter Brain Aging: Exposome-Wide And Genome-Wide Association Study, Li Feng, Halley S Milleson, Zhenyao Ye, Travis Canida, Hongjie Ke, Menglu Liang, Si Gao, Shuo Chen, L Elliot Hong, Peter Kochunov, David K Y Lei, Tianzhou Ma

Faculty, Staff and Student Publications

BACKGROUND/OBJECTIVES: Human brain aging is a complex process that affects various aspects of brain function and structure, increasing susceptibility to neurological and psychiatric disorders. A number of nongenetic (e.g., environmental and lifestyle) and genetic risk factors are found to contribute to the varying rates at which the brain ages among individuals.

METHODS: In this paper, we conducted both an exposome-wide association study (XWAS) and a genome-wide association study (GWAS) on white matter brain aging in the UK Biobank, revealing the multifactorial nature of brain aging. We applied a machine learning algorithm and leveraged fractional anisotropy tract measurements from diffusion tensor …

Paediatric Mass Casualty Response Through The Lens Of Functional Resonance Analytical Methodology- Lessons Learned., R J Mackinnon, D Slater, R Jenner, T Stenfors, Christopher S. Kennedy, K P Härenstam

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: Mass Casualty Incidents are rare but can significantly stress healthcare systems. Functional Resonance Analytical Methodology (FRAM) is a systematic approach to model and explore how complex systems adapt to variations and to understand resilient properties in the face of perturbations. The aim of this study was to use FRAM to create a model of a paediatric trauma system during the initial response to the Manchester Arena Attack to provide resilience-based insights for the management of future Mass Casualty Incidents (MCI).

METHODS: Qualitative interviews in the immediate aftermath of a terrorist bombing, were followed up with further in-depth probing of …

A Bayesian Semi-Parametric Scalar-On-Function Regression With Measurement Error Using Instrumental Variables, Roger S Zoh, Yuanyuan Luan, Lan Xue, David B Allison, Carmen D Tekwe

Children’s Nutrition Research Center Staff Publications

Wearable devices such as the ActiGraph are now commonly used in research to monitor or track physical activity. This trend corresponds with the growing need to assess the relationships between physical activity and health outcomes, such as obesity, accurately. Device-based physical activity measures are best treated as functions when assessing their associations with scalar-valued outcomes such as body mass index. Scalar-on-function regression (SoFR) is a suitable regression model in this setting. Most estimation approaches in SoFR assume that the measurement error in functional covariates is white noise. Violating this assumption can lead to underestimating model parameters. There are limited approaches …

Sequence-To-Expression Approach To Identify Etiological Non-Coding Dna Variations In P53 And Cmyc-Driven Diseases, Katherine Kin, Shounak Bhogale, Lisha Zhu, Derrick Thomas, Jessica Bertol, W Jim Zheng, Saurabh Sinha, Walid D Fakhouri

Faculty, Staff and Student Publications

Disease risk prediction based on genomic sequence and transcriptional profile can improve disease screening and prevention. Despite identifying many disease-associated DNA variants, distinguishing deleterious non-coding DNA variations remains poor for most common diseases. In this study, we designed in vitro experiments to uncover the significance of occupancy and competitive binding between P53 and cMYC on common target genes. Analyzing publicly available ChIP-seq data for P53 and cMYC in embryonic stem cells showed that ~344-366 regions are co-occupied, and on average, two cis-overlapping motifs (CisOMs) per region were identified, suggesting that co-occupancy is evolutionarily conserved. Using U2OS and Raji cells untreated …

Impact Of An Online Decision Support Tool For Ductal Carcinoma In Situ (Dcis) Using A Pre-Post Design (Aft-25), Elissa Ozanne, Kellyn Maves, Angela Tramontano, Thomas Lynch, Alastair Thompson, Ann Partridge, Elizabeth Frank, Deborah Collyar, Desiree Basila, Donna Pinto, Terry Hyslop, Marc Ryser, Shoshana Rosenberg, E. Shelley Hwang, Rinaa Punglia

Department of Pharmacology and Experimental Therapeutics Faculty Papers

BACKGROUND: The heterogeneous biology of ductal carcinoma in situ (DCIS), as well as the variable outcomes, in the setting of numerous treatment options have led to prognostic uncertainty. Consequently, making treatment decisions is challenging and necessitates involved communication between patient and provider about the risks and benefits. We developed and investigated an interactive decision support tool (DST) designed to improve communication of treatment options and related long-term risks for individuals diagnosed with DCIS.

FINDINGS: The DST was developed for use by individuals aged > 40 years with DCIS and is based on a disease simulation model that integrates empirical data and …

Spatially Fractionated Grid Radiation Potentiates Immune-Mediated Tumor Control, Rebecca A Bekker, Nina Obertopp, Gage Redler, José Penagaricano, Jimmy J Caudell, Kosj Yamoah, Shari Pilon-Thomas, Eduardo G Moros, Heiko Enderling

Faculty, Staff and Student Publications

Background: Tumor-immune interactions shape a developing tumor and its tumor immune microenvironment (TIME) resulting in either well-infiltrated, immunologically inflamed tumor beds, or immune deserts with low levels of infiltration. The pre-treatment immune make-up of the TIME is associated with treatment outcome; immunologically inflamed tumors generally exhibit better responses to radio- and immunotherapy than non-inflamed tumors. However, radiotherapy is known to induce opposing immunological consequences, resulting in both immunostimulatory and inhibitory responses. In fact, it is thought that the radiation-induced tumoricidal immune response is curtailed by subsequent applications of radiation. It is thus conceivable that spatially fractionated radiotherapy (SFRT), administered through …

Addressing Dispersion In Mis-Measured Multivariate Binomial Outcomes: A Novel Statistical Approach For Detecting Differentially Methylated Regions In Bisulfite Sequencing Data., Kaiqiong Zhao, Karim Oualkacha, Yixiao Zeng, Cathy Shen, Kathleen Klein, Lajmi Lakhal-Chaieb, Aurélie Labbe, Tomi Pastinen, Marie Hudson, Inés Colmegna, Sasha Bernatsky, Celia M T Greenwood

Manuscripts, Articles, Book Chapters and Other Papers

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra-parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non-constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi-binomial varying coefficient mixed model, which …

A Multivariate To Multivariate Approach For Voxel-Wise Genome-Wide Association Analysis, Qiong Wu, Yuan Zhang, Xiaoqi Huang, Tianzhou Ma, L Elliot Hong, Peter Kochunov, Shuo Chen

Faculty, Staff and Student Publications

The joint analysis of imaging-genetics data facilitates the systematic investigation of genetic effects on brain structures and functions with spatial specificity. We focus on voxel-wise genome-wide association analysis, which may involve trillions of single nucleotide polymorphism (SNP)-voxel pairs. We attempt to identify underlying organized association patterns of SNP-voxel pairs and understand the polygenic and pleiotropic networks on brain imaging traits. We propose a bi-clique graph structure (ie, a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern. Next, we develop computational strategies to detect latent SNP-voxel bi-cliques and an inference model for statistical testing. …

Design And Implementation Of An Opioid Scorecard For Hospital System-Wide Peer Comparison Of Opioid Prescribing Habits: Observational Study, Benjamin Slovis, Soonyip Huang, Melanie Mcarthur, Cara Martino, Tasia Beers, Meghan Labella, Jeffrey Riggio, Edmund Pribitkin

Jefferson Hospital Staff Papers and Presentations

BACKGROUND: Reductions in opioid prescribing by health care providers can lead to a decreased risk of opioid dependence in patients. Peer comparison has been demonstrated to impact providers' prescribing habits, though its effect on opioid prescribing has predominantly been studied in the emergency department setting.

OBJECTIVE: The purpose of this study is to describe the development of an enterprise-wide opioid scorecard, the architecture of its implementation, and plans for future research on its effects.

METHODS: Using data generated by the author's enterprise vendor-based electronic health record, the enterprise analytics software, and expertise from a dedicated group of informaticists, physicians, and …

Training Robust T1-Weighted Magnetic Resonance Imaging Liver Segmentation Models Using Ensembles Of Datasets With Different Contrast Protocols And Liver Disease Etiologies, Nihil Patel, Adrian Celaya, Mohamed Eltaher, Rachel Glenn, Kari Brewer Savannah, Kristy K Brock, Jessica I Sanchez, Tiffany L Calderone, Darrel Cleere, Ahmed Elsaiey, Matthew Cagley, Nakul Gupta, David Victor, Laura Beretta, Eugene J Koay, Tucker J Netherton, David T Fuentes

Faculty, Staff and Student Publications

Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. This manuscript develops a generalizable deep learning model to segment the liver on T1-weighted MR images. In particular, three distinct deep learning architectures (nnUNet, PocketNet, Swin UNETR) were considered using data gathered from six geographically different institutions. A total of 819 T1-weighted MR images were gathered from both public and internal sources. Our experiments compared each architecture's testing performance when trained both …

Myc Induces Oncogenic Stress Through Rna Decay And Ribonucleotide Catabolism In Breast Cancer, Jitendra K Meena, Jarey H Wang, Nicholas J Neill, Dianne Keough, Nagireddy Putluri, Panagiotis Katsonis, Amanda M Koire, Hyemin Lee, Elizabeth A Bowling, Siddhartha Tyagi, Mayra Orellana, Rocio Dominguez-Vidaña, Heyuan Li, Kenneth Eagle, Charles Danan, Hsiang-Ching Chung, Andrew D Yang, William Wu, Sarah J Kurley, Brian M Ho, Joseph R Zoeller, Calla M Olson, Kristen L Meerbrey, Olivier Lichtarge, Arun Sreekumar, Clifford C Dacso, Luke W Guddat, Dominik Rejman, Dana Hocková, Zlatko Janeba, Lukas M Simon, Charles Y Lin, Monica C Pillon, Thomas F Westbrook

Faculty, Staff and Students Publications

Upregulation of MYC is a hallmark of cancer, wherein MYC drives oncogenic gene expression and elevates total RNA synthesis across cancer cell transcriptomes. Although this transcriptional anabolism fuels cancer growth and survival, the consequences and metabolic stresses induced by excess cellular RNA are poorly understood. Herein, we discover that RNA degradation and downstream ribonucleotide catabolism is a novel mechanism of MYC-induced cancer cell death. Combining genetics and metabolomics, we find that MYC increases RNA decay through the cytoplasmic exosome, resulting in the accumulation of cytotoxic RNA catabolites and reactive oxygen species. Notably, tumor-derived exosome mutations abrogate MYC-induced cell death, suggesting …

Ensemble Pretrained Language Models To Extract Biomedical Knowledge From Literature, Zhao Li, Qiang Wei, Liang-Chin Huang, Jianfu Li, Yan Hu, Yao-Shun Chuang, Jianping He, Avisha Das, Vipina Kuttichi Keloth, Yuntao Yang, Chiamaka S Diala, Kirk E Roberts, Cui Tao, Xiaoqian Jiang, W Jim Zheng, Hua Xu

Faculty, Staff and Student Publications

OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking.

MATERIALS AND METHODS: For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and …

Improving Large Language Models For Clinical Named Entity Recognition Via Prompt Engineering, Yan Hu, Qingyu Chen, Jingcheng Du, Xueqing Peng, Vipina Kuttichi Keloth, Xu Zuo, Yujia Zhou, Zehan Li, Xiaoqian Jiang, Zhiyong Lu, Kirk Roberts, Hua Xu

Faculty, Staff and Student Publications

IMPORTANCE: The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets.

OBJECTIVES: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance.

MATERIALS AND METHODS: We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, …