Biomedical Informatics Commons^™

Modeling Recurrence In Idiopathic Subglottic Stenosis With Mobile Peak Expiratory Flow, Kyle Kimura, Liping Du, Lynn D Berry, Li-Ching Huang, Sheau-Chiann Chen, David O Francis, Alexander Gelbard

Faculty, Staff and Student Publications

OBJECTIVES/HYPOTHESIS: We sought to establish normative peak expiratory flow (PEF) data for patients with idiopathic subglottic stenosis (iSGS), evaluate whether immediate changes in PEF after a procedure predict long-term treatment response, and test if a decline in longitudinal PEF is associated with disease recurrence.

STUDY DESIGN: International, prospective, 3-year multicenter cohort study of 810 patients with untreated, newly diagnosed, or previously treated iSGS.

METHODS: iSGS patients consented and enrolled in the North American Airway Collaborative (NoAAC) iSGS

RESULTS: Within the NoAAC iSGS1000 cohort, 810 patients participated in a 3-year, prospective study comparing surgical treatment efficacy and 385 had appropriate PEF …

Associations Of High-Sensitivity Troponin And Natriuretic Peptide Levels With Outcomes After Intensive Blood Pressure Lowering: Findings From The Sprint Randomized Clinical Trial, Jarett D Berry, Vijay Nambi, Walter T Ambrosius, Haiying Chen, Anthony A Killeen, Addison Taylor, Robert D Toto, Elsayed Z Soliman, John W Mcevoy, Ambarish Pandey, Parag H Joshi, Stefan Blankenberg, Dalane W Kitzman, Christie M Ballantyne, James A De Lemos

Faculty, Staff and Students Publications

IMPORTANCE: Elevated high-sensitivity cardiac troponin T (hscTnT) and N-terminal pro-B-type natriuretic peptide (NTproBNP) levels are associated with risk of heart failure (HF) and mortality among individuals in the general population. However, it is unknown if this risk is modifiable.

OBJECTIVE: To test the hypothesis that elevated hscTnT and NTproBNP levels would identify individuals with the greatest risk for mortality and HF and the largest benefit associated with intensive systolic blood pressure (SBP) lowering.

DESIGN, SETTING, AND PARTICIPANTS: This is a nonprespecified post hoc analysis of the multicenter, prospective, randomized clinical Systolic Blood Pressure Intervention Trial (SPRINT), conducted from October 20, …

Nrf1 Association With Auts2-Polycomb Mediates Specific Gene Activation In The Brain, Sanxiong Liu, Kimberly A Aldinger, Chi Vicky Cheng, Takae Kiyama, Mitali Dave, Hanna K Mcnamara, Wukui Zhao, James M Stafford, Nicolas Descostes, Pedro Lee, Stefano G Caraffi, Ivan Ivanovski, Edoardo Errichiello, Christiane Zweier, Orsetta Zuffardi, Michael Schneider, Antigone S Papavasiliou, M Scott Perry, Jennifer Humberson, Megan T Cho, Astrid Weber, Andrew Swale, Tudor C Badea, Chai-An Mao, Livia Garavelli, William B Dobyns, Danny Reinberg

Faculty, Staff and Student Publications

The heterogeneous family of complexes comprising Polycomb repressive complex 1 (PRC1) is instrumental for establishing facultative heterochromatin that is repressive to transcription. However, two PRC1 species, ncPRC1.3 and ncPRC1.5, are known to comprise novel components, AUTS2, P300, and CK2, that convert this repressive function to that of transcription activation. Here, we report that individuals harboring mutations in the HX repeat domain of AUTS2 exhibit defects in AUTS2 and P300 interaction as well as a developmental disorder reflective of Rubinstein-Taybi syndrome, which is mainly associated with a heterozygous pathogenic variant in CREBBP/EP300. Moreover, the absence of AUTS2 or mutation in its …

Artificial Intelligence Algorithms For Medical Imaging And Healthcare, Jonathan William Stubblefield

Student Theses and Dissertations

In this dissertation, we studied several applications of artificial intelligence applications to healthcare. In the first chapter, we examined a machine learning algorithm for classifying patients presenting to the emergency department with acute respiratory distress syndrome (ARDS). Patients presenting with this life-threatening condition require a quick and accurate assessment of whether the condition is infectious or cardiac in etiology as the treatments for these etiologies of ARDS differ significantly. We used a transfer learning approach to develop our model. The model used a combination of clinical data and a chest x-ray as its input and achieved an accuracy 0.675 on …

Enhance Representation Learning Of Clinical Narrative With Neural Networks For Clinical Predictive Modeling, Yuqi Si

Dissertations and Theses (Open Access)

Medicine is undergoing a technological revolution. Understanding human health from clinical data has major challenges from technical and practical perspectives, thus prompting methods that understand large, complex, and noisy data. These methods are particularly necessary for natural language data from clinical narratives/notes, which contain some of the richest information on a patient. Meanwhile, deep neural networks have achieved superior performance in a wide variety of natural language processing (NLP) tasks because of their capacity to encode meaningful but abstract representations and learn the entire task end-to-end. In this thesis, I investigate representation learning of clinical narratives with deep neural networks …

Secondary Use Of Structured Electronic Health Records Data: From Observational Studies To Deep Learning-Based Predictive Modeling, Laila Bekhet

Dissertations and Theses (Open Access)

With the wide adoption of electronic health records (EHRs), researchers, as well as large healthcare organizations, governmental institutions, insurance, and pharmaceutical companies have been interested in leveraging this rich clinical data source to extract clinical evidence and develop predictive algorithms. Large vendors have been able to compile structured EHR data from sites all over the United States, de-identify these data, and make them available to data science researchers in a more usable format. For this dissertation, we leveraged one of the earliest and largest secondary EHR data sources and conducted three studies of increasing scope. In the first study, which …

Perspectives From The Society For Pediatric Research: Advice On Sustaining Science And Mentoring During Covid-19, Catherine S Forster, Suong T Nguyen, Weston T Powell, Daniel J Moore, Jacqueline Ho, Melvin B Heyman, Tara L Wenger, Fernando Gonzalez, Margaret Hostetter, Andrew Nowalk, Caroline E Rassbach, Debra Boyer, Pnina Weiss, Rebecca L Blankenburg, Jordan S Orange, Kate G Ackerman, Audrea M Burns, National Pediatric Physician-Scientist Collaborative Workgroup

Faculty, Staff and Students Publications

The COVID-19 pandemic will leave an indelible mark on the careers of current medical trainees. Given the disruptions to medical education, economic impact on institutions, and the uncertainties around future job prospects, trainees are facing unprecedented challenges. This situation is especially concerning for futures of pediatric physician-scientist trainees, where concerns regarding maintaining the pipeline were well documented prior to the emergence of COVID-19. In this Perspectives article, we leverage the unique expertise of our workgroup to address concerns of physician-scientist trainees and to provide suggestions on how to navigate career trajectories in the post-COVID-19 era. We identified and addressed four …

Invariant Natural Killer T-Cell Subsets Have Diverse Graft-Versus-Host-Disease-Preventing And Antitumor Effects, Kristina Maas-Bauer, Juliane K Lohmeyer, Toshihito Hirai, Teresa Lopes Ramos, Furqan M Fazal, Ulrike M Litzenburger, Kathryn E Yost, Jessica V Ribado, Neeraja Kambham, Arielle S Wenokur, Po-Yu Lin, Maite Alvarez, Melissa Mavers, Jeanette Baker, Ami S Bhatt, Howard Y Chang, Federico Simonetta, Robert S Negrin

Faculty, Staff and Students Publications

Invariant natural killer T (iNKT) cells are a T-cell subset with potent immunomodulatory properties. Experimental evidence in mice and observational studies in humans indicate that iNKT cells have antitumor potential as well as the ability to suppress acute and chronic graft-versus-host-disease (GVHD). Murine iNKT cells differentiate during thymic development into iNKT1, iNKT2, and iNKT17 sublineages, which differ transcriptomically and epigenomically and have subset-specific developmental requirements. Whether distinct iNKT sublineages also differ in their antitumor effect and their ability to suppress GVHD is currently unknown. In this work, we generated highly purified murine iNKT sublineages, characterized their transcriptomic and epigenomic landscape, …

Comprehensive Characterization Of Covid-19 Patients With Repeatedly Positive Sars-Cov-2 Tests Using A Large Us Electronic Health Record Database, Xiao Dong, Yujia Zhou, Xiao-Ou Shu, Elmer V Bernstam, Rebecca Stern, David M Aronoff, Hua Xu, Loren Lipworth

Faculty, Staff and Student Publications

In the absence of genome sequencing, two positive molecular tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) separated by negative tests, prolonged time, and symptom resolution remain the best surrogate measure of possible reinfection. Using a large electronic health record database, we characterized clinical and testing data for 23 patients with repeatedly positive SARS-CoV-2 PCR test results ≥60 days apart, separated by ≥2 consecutive negative test results. The prevalence of chronic medical conditions, symptoms, and severe outcomes related to coronavirus disease 19 (COVID-19) illness were ascertained. The median age of patients was 64.5 years, 40% were Black, and 39% …

A Comparison Of Machine Learning Classifiers For Pediatric Epilepsy Using Resting-State Functional Mri Latency Data, Ryan D Nguyen, Matthew D Smyth, Liang Zhu, Ludovic P Pao, Shannon K Swisher, Emmett H Kennady, Anish Mitra, Rajan P Patel, Jeremy E Lankford, Gretchen Von Allmen, Michael W Watkins, Michael E Funke, Manish N Shah

Faculty, Staff and Student Publications

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from …

First-In-Human Segmental Esophageal Reconstruction Using A Bioengineered Mesenchymal Stromal Cell-Seeded Implant, Johnathon M Aho, Saverio La Francesca, Scott D Olson, Fabio Triolo, Jeff Bouchard, Laura Mondano, Sumati Sundaram, Christina Roffidal, Charles S Cox, Louis M Wong Kee Song, Sameh M Said, William Fodor, Dennis A Wigle

Faculty, Staff and Student Publications

INTRODUCTION: Resection and reconstruction of the esophagus remains fraught with morbidity and mortality. Recently, data from a porcine reconstruction model revealed that segmental esophageal reconstruction using an autologous mesenchymal stromal cell-seeded polyurethane graft (Cellspan esophageal implant [CEI]) can facilitate esophageal regrowth and regeneration. To this end, a patient requiring a full circumferential esophageal segmental reconstruction after a complex multiorgan tumor resection was approved for an investigational treatment under the Food and Drug Administration Expanded Access Use (Investigational New Drug 17402).

METHODS: Autologous adipose-derived mesenchymal stromal cells (Ad-MSCs) were isolated from the Emergency Investigational New Drug patient approximately 4 weeks before …

Standardizing New Diagnostic Tests To Facilitate Rapid Responses To The Covid-19 Pandemic, Xiao Dong

Dissertations and Theses (Open Access)

In order to enhance the data interoperability, an expeditious and accurate standardization solution is highly desirable for naming rapidly emerging novel lab tests, and thus diminishes confusion in early responses to pandemic outbreaks. This is a preliminary study to explore the roles and implementation of medical informatics technology, especially natural language processing and ontology methods, in standardizing information about emerging lab tests during a pandemic, thereby facilitating rapid responses to the pandemic. The ultimate goal of this study is to develop an informatics framework for rapid standardization of lab testing names during a pandemic to better prepare for future public …

Deep Learning-Based Mixed-Dimensional Gaussian Mixture Model For Characterizing Variability In Cryo-Em, Muyuan Chen, Steven J Ludtke

Faculty, Staff and Students Publications

Structural flexibility and/or dynamic interactions with other molecules is a critical aspect of protein function. Cryogenic electron microscopy (cryo-EM) provides direct visualization of individual macromolecules sampling different conformational and compositional states. While numerous methods are available for computational classification of discrete states, characterization of continuous conformational changes or large numbers of discrete state without human supervision remains challenging. Here we present e2gmm, a machine learning algorithm to determine a conformational landscape for proteins or complexes using a three-dimensional Gaussian mixture model mapped onto two-dimensional particle images in known orientations. Using a deep neural network architecture, e2gmm can automatically resolve the …

Left Ventricular Recovery With Explantation Of Continuous-Flow Left Ventricular Assist Device After 5 Years Of Support, Andre Critsinelis, Chitaru Kurihara, Masashi Kawabori, Tadahisa Sugiura, Andrew B Civitello, Jeffrey A Morgan

Faculty, Staff and Students Publications

Mechanical circulatory support may result in sufficient myocardial recovery to allow for explantation of the left ventricular assist device (LVAD). The duration of support associated with left ventricular recovery has generally been 6-12 months. In this report, we present a patient in whom the left ventricle recovered after 5 years of support with a LVAD. Our report demonstrates that long-term monitoring for left ventricular recovery is prudent and may allow for late device explantation.

The Synergy Of Bet Inhibitors With Aurora A Kinase Inhibitors In Mycn-Amplified Neuroblastoma Is Heightened With Functional Tp53, Joanna S Yi, Oscar Sias-Garcia, Nicole Nasholm, Xiaoyu Hu, Amanda Balboni Iniguez, Matthew D Hall, Mindy Davis, Rajarshi Guha, Myrthala Moreno-Smith, Eveline Barbieri, Kevin Duong, Jessica Koach, Jun Qi, James E Bradner, Kimberly Stegmaier, William A Weiss, W Clay Gustafson

Faculty, Staff and Students Publications

Amplification of MYCN is a poor prognostic feature in neuroblastoma (NBL) indicating aggressive disease. We and others have shown BET bromodomain inhibitors (BETi) target MYCN indirectly by downregulating its transcription. Here we sought to identify agents that synergize with BETi and to identify biomarkers of resistance. We previously performed a viability screen of ∼1,900 oncology-focused compounds combined with BET bromodomain inhibitors against MYCN-amplified NBL cell lines. Reanalysis of our screening results prominently identified inhibitors of aurora kinase A (AURKAi) to be highly synergistic with BETi. We confirmed the anti-proliferative effects of several BETi+AURKAi combinations in MYCN-amplified NBL cell lines. Compared …

Digital Technology Needs In Maternal Mental Health: A Qualitative Inquiry, Alexandra Zingg, Laura Carter, Deevakar Rogith, Amy Franklin, Sudhakar Selvaraj, Jerrie Refuerzo, Sahiti Myneni

Faculty, Staff and Student Publications

Digital technologies offer many opportunities to improve mental healthcare management for women seeking pre- and-postnatal care. They provide a discrete, practical medium that is well-suited for the sensitive nature of mental health. Women who are more prone to experiencing peripartum depression (PPD), such as those of low-socioeconomic background or in high-risk pregnancies, can benefit the most from such technologies. However, current digital interventions directed towards this population provide suboptimal support, and their responsiveness to end user needs is quite limited. Our objective is to understand the digital terrain of information needs for low-socioeconomic status women with high-risk pregnancies, specifically within …

Deep Learning Predicts Chromosomal Instability From Histopathology Images, Zhuoran Xu, Akanksha Verma, Uska Naveed, Samuel F. Bakhoum, Pegah Khosravi, Olivier Elemento

Publications and Research

Chromosomal instability (CIN) is a hallmark of human cancer yet not readily testable for patients with cancer in routine clinical setting. In this study, we sought to explore whether CIN status can be predicted using ubiquitously available hematoxylin and eosin histology through a deep learning-based model. When applied to a cohort of 1,010 patients with breast cancer (Training set: n = 858, Test set: n = 152) from The Cancer Genome Atlas where 485 patients have high CIN status, our model accurately classified CIN status, achieving an area under the curve of 0.822 with 81.2% sensitivity and 68.7% specificity in …

Med-Bert: Pretrained Contextualized Embeddings On Large-Scale Structured Electronic Health Records For Disease Prediction, Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, Degui Zhi

Faculty, Staff and Student Publications

Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required by these models to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pretraining of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. Inspired by BERT, we propose Med-BERT, which adapts the BERT framework originally developed …

Soluble Angiotensin-Converting Enzyme 2, Cardiac Biomarkers, Structure, And Function, And Cardiovascular Events (From The Atherosclerosis Risk In Communities Study), Aliza Hussain, Olive Tang, Caroline Sun, Xiaoming Jia, Elizabeth Selvin, Vijay Nambi, Aaron Folsom, Gerardo Heiss, Faiez Zannad, Thomas Mosley, Salim S Virani, Josef Coresh, Eric Boerwinkle, Bing Yu, Jonathan W Cunningham, Amil M Shah, Scott D Solomon, James A De Lemos, Ron C Hoogeveen, Christie M Ballantyne

Faculty, Staff and Students Publications

Membrane-bound angiotensin-converting enzyme 2 is important in regulation of the renin-angiotensin-aldosterone system, but the association of cleaved soluble ACE2 (sACE2) with cardiovascular disease (CVD) is unclear. We evaluated the association of sACE2 with cardiac biomarkers, structure, and function and cardiovascular events in the Atherosclerosis Risk in Communities Study. sACE2 was measured in a subset of 497 participants (mean age 78±5.4 years, 53% men, 27% black); Cox regression analyses assessed prospective associations of sACE2 with time to first CVD event at median 6.1-year follow-up. sACE2 was higher in men, blacks, and participants with prevalent CVD, diabetes, or hypertension. Higher sACE2 levels …

Enabling Precision Medicine In Cancer Care Through A Molecular Data Warehouse: The Moffitt Experience, Steven A Eschrich, Jamie K Teer, Phillip Reisman, Erin Siegel, Chandan Challa, Patricia Lewis, Katherine Fellows, Everin Malpica, Rodrigo Carvajal, Guillermo Gonzalez, Scott Cukras, Miguel Betin-Montes, Garrick Aden-Buie, Melissa Avedon, Daniel Manning, Aik Choon Tan, Brooke L Fridley, Travis Gerke, Mattias Van Looveren, Amilcar Blake, Jennifer Greenman, Dana E Rollison

Faculty, Staff and Students Publications

PURPOSE: The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research.

METHODS: Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse …

Proteogenomic And Metabolomic Characterization Of Human Glioblastoma, Liang-Bo Wang, Alla Karpova, Marina A Gritsenko, Jennifer E Kyle, Song Cao, Yize Li, Dmitry Rykunov, Antonio Colaprico, Joseph H Rothstein, Runyu Hong, Vasileios Stathias, Macintosh Cornwell, Francesca Petralia, Yige Wu, Boris Reva, Karsten Krug, Pietro Pugliese, Emily Kawaler, Lindsey K Olsen, Wen-Wei Liang, Xiaoyu Song, Yongchao Dou, Michael C Wendl, Wagma Caravan, Wenke Liu, Daniel Cui Zhou, Jiayi Ji, Chia-Feng Tsai, Vladislav A Petyuk, Jamie Moon, Weiping Ma, Rosalie K Chu, Karl K Weitz, Ronald J Moore, Matthew E Monroe, Rui Zhao, Xiaolu Yang, Seungyeul Yoo, Azra Krek, Alexis Demopoulos, Houxiang Zhu, Matthew A Wyczalkowski, Joshua F Mcmichael, Brittany L Henderson, Caleb M Lindgren, Hannah Boekweg, Shuangjia Lu, Jessika Baral, Lijun Yao, Kelly G Stratton, Lisa M Bramer, Erika Zink, Sneha P Couvillion, Kent J Bloodsworth, Shankha Satpathy, Weiva Sieh, Simina M Boca, Stephan Schürer, Feng Chen, Maciej Wiznerowicz, Karen A Ketchum, Emily S Boja, Christopher R Kinsinger, Ana I Robles, Tara Hiltke, Mathangi Thiagarajan, Alexey I Nesvizhskii, Bing Zhang, D R Mani, Michele Ceccarelli, Xi S Chen, Sandra L Cottingham, Qing Kay Li, Albert H Kim, David Fenyö, Kelly V Ruggles, Henry Rodriguez, Mehdi Mesri, Samuel H Payne, Adam C Resnick, Pei Wang, Richard D Smith, Antonio Iavarone, Milan G Chheda, Jill S Barnholtz-Sloan, Karin D Rodland, Tao Liu, Li Ding, Clinical Proteomic Tumor Analysis Consortium

Faculty, Staff and Students Publications

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM …

Zno Nucleation Into Trititanate Nanotubes By Ald Equipment Techniques, A New Way To Functionalize Layered Metal Oxides, Mabel Moreno, Miryam Arredondo, Quentin M Ramasse, Matthew Mclaren, Philine Stötzner, Stefan Förster, Eglantina Benavente, Caterina Salgado, Sindy Devis, Paula Solar, Luis Velasquez, Guillermo González

Faculty, Staff and Students Publications

In this contribution, we explore the potential of atomic layer deposition (ALD) techniques for developing new semiconductor metal oxide composites. Specifically, we investigate the functionalization of multi-wall trititanate nanotubes, H2Ti3O7 NTs (sample T1) with zinc oxide employing two different ALD approaches: vapor phase metalation (VPM) using diethylzinc (Zn(C2H5)2, DEZ) as a unique ALD precursor, and multiple pulsed vapor phase infiltration (MPI) using DEZ and water as precursors. We obtained two different types of tubular H2Ti3O7 species containing ZnO in their structures. Multi-wall trititanate nanotubes with ZnO intercalated inside the tube wall sheets were the main products from the VPM infiltration …

Generalized And Transferable Patient Language Representation For Phenotyping With Limited Data, Yuqi Si, Elmer V Bernstam, Kirk Roberts

Faculty, Staff and Student Publications

The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. The model is first pre-trained with different but related high-prevalence phenotypes and further fine-tuned on downstream target tasks. Our main contribution focuses on the impact this technique can have on low-prevalence phenotypes, a challenging task due to the dearth of data. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes including 38 circulatory diseases, 23 …

A Principal Components Analysis Of Factors Associated With Successful Implementation Of An Lvad Decision Support Tool, Kristin M Kostick, Meredith Trejo, Arvind Bhimaraj, Andrew Civitello, Jonathan Grinstein, Douglas Horstmanshof, Ulrich P Jorde, Matthias Loebe, Mandeep R Mehra, Nasir Z Sulemanjee, Vinay Thohan, Barry H Trachtenberg, Nir Uriel, Robert J Volk, Jerry D Estep, J S Blumenthal-Barby

Faculty, Staff and Students Publications

BACKGROUND: A central goal among researchers and policy makers seeking to implement clinical interventions is to identify key facilitators and barriers that contribute to implementation success. Despite calls from a number of scholars, empirical insights into the complex structural and cultural predictors of why decision aids (DAs) become routinely embedded in health care settings remains limited and highly variable across implementation contexts.

METHODS: We examined associations between "reach", a widely used indicator (from the RE-AIM model) of implementation success, and multi-level site characteristics of nine LVAD clinics engaged over 18 months in implementation and dissemination of a decision aid for …

A Deep Learning Approach To Diagnostic Classification Of Prostate Cancer Using Pathology–Radiology Fusion, Pegah Khosravi, Maria Lysandrou, Mahmoud Eljalby, Qianzi Li, Ehsan Kazemi, Pantelis Zisimopoulos, Alexandros Sigaras, Matthew Brendel, Josue Barnes, Camir Ricketts, Dmitry Meleshko, Andy Yat, Timothy D. Mcclure, Brian D. Robinson, Andrea Sboner, Olivier Elemento, Bilal Chughtai, Iman Hajirasouliha

Publications and Research

Background

A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications.

Purpose

To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information.

Study Type

Retrospective.

Population

Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases).

Field Strength/Sequence

1.5 to 3.0 Tesla, T2-weighted image pulse sequences.

Assessment

MR images reviewed and selected …

Discovery And Characterization Of Bromodomain 2-Specific Inhibitors Of Brdt, Zhifeng Yu, Angela F Ku, Justin L Anglin, Rajesh Sharma, Melek Nihan Ucisik, John C Faver, Feng Li, Pranavanand Nyshadham, Nicholas Simmons, Kiran L Sharma, Sureshbabu Nagarajan, Kevin Riehle, Gundeep Kaur, Banumathi Sankaran, Marta Storl-Desmond, Stephen S Palmer, Damian W Young, Choel Kim, Martin M Matzuk

Faculty, Staff and Students Publications

Bromodomain testis (BRDT), a member of the bromodomain and extraterminal (BET) subfamily that includes the cancer targets BRD2, BRD3, and BRD4, is a validated contraceptive target. All BET subfamily members have two tandem bromodomains (BD1 and BD2). Knockout mice lacking BRDT-BD1 or both bromodomains are infertile. Treatment of mice with JQ1, a BET BD1/BD2 nonselective inhibitor with the highest affinity for BRD4, disrupts spermatogenesis and reduces sperm number and motility. To assess the contribution of each BRDT bromodomain, we screened our collection of DNA-encoded chemical libraries for BRDT-BD1 and BRDT-BD2 binders. High-enrichment hits were identified and resynthesized off-DNA and examined …

Tools For Visualizing And Analyzing Fourier Space Sampling In Cryo-Em, Philip R Baldwin, Dmitry Lyumkis

Faculty, Staff and Students Publications

A complete understanding of how an orientation distribution contributes to a cryo-EM reconstruction remains lacking. It is necessary to begin critically assessing the set of views to gain an understanding of its effect on experimental reconstructions. Toward that end, we recently suggested that the type of orientation distribution may alter resolution measures in a systematic manner. We introduced the sampling compensation factor (SCF), which incorporates how the collection geometry might change the spectral signal-to-noise ratio (SSNR), irrespective of the other experimental aspects. We show here that knowledge of the sampling restricted to spherical surfaces of sufficiently large radii in Fourier …

Evaluating The Revised American Society For Gastrointestinal Endoscopy Guidelines For Common Bile Duct Stone Diagnosis, Jake S Jacob, Michelle E Lee, Erin Y Chew, Aaron P Thrift, Robert J Sealock

Faculty, Staff and Students Publications

BACKGROUND/AIMS: The American Society for Gastrointestinal Endoscopy (ASGE) revised its guidelines for risk stratification of patients with suspected choledocholithiasis. This study aimed to assess the diagnostic performance of the revision and to compare it to the previous guidelines.

METHODS: We conducted a retrospective cohort study of 267 patients with suspected choledocholithiasis. We identified high-risk patients according to the original and revised guidelines and examined the diagnostic accuracy of both guidelines. We measured the association between individual criteria and choledocholithiasis.

RESULTS: Under the original guidelines, 165 (62%) patients met the criteria for high risk, of whom 79% had confirmed choledocholithiasis. The …

Continuous-Flow Left Ventricular Assist Device Therapy In Adults With Transposition Of The Great Vessels, Tadahisa Sugiura, Chitaru Kurihara, Masashi Kawabori, Andre C Critsinelis, Andrew B Civitello, Jeffrey A Morgan, O H Frazier

Faculty, Staff and Students Publications

An increasing number of children with congenital heart disease are surviving into adulthood and subsequently developing end-stage heart failure. Two example populations are adults who have been previously operated on for congenitally corrected transposition of the great arteries (CCTGA) and transposition of the great arteries (TGA). Implantation of a continuous flow left ventricular assist device (CF-LVAD) in these patients can present unusual anatomical and physiologic challenges. In this report, we describe outcomes of CF-LVAD implantation in three such patients. These cases demonstrate the feasibility of implanting a CF-LVAD in patients who have undergone surgery for CCTGA and/or TGA.

The Mitochondrial Protease Lonp1 Promotes Proteasome Inhibitor Resistance In Multiple Myeloma, Laure Maneix, Melanie A Sweeney, Sukyeong Lee, Polina Iakova, Shannon E Moree, Ergun Sahin, Premal Lulla, Sarvari V Yellapragada, Francis T F Tsai, Andre Catic

Faculty, Staff and Students Publications

Multiple myeloma and its precursor plasma cell dyscrasias affect 3% of the elderly population in the US. Proteasome inhibitors are an essential part of several standard drug combinations used to treat this incurable cancer. These drugs interfere with the main pathway of protein degradation and lead to the accumulation of damaged proteins inside cells. Despite promising initial responses, multiple myeloma cells eventually become drug resistant in most patients. The biology behind relapsed/refractory multiple myeloma is complex and poorly understood. Several studies provide evidence that in addition to the proteasome, mitochondrial proteases can also contribute to protein quality control outside of …