Digital Commons Network^™

Panel Stacking Is A Threat To Consensus Statement Validity, Kasper P Kepp, Preben Aavitsland, Marcel Ballin, Francois Balloux, Stefan Baral, Kevin Bardosh, Howard Bauchner, Eran Bendavid, Raj Bhopal, Daniel T Blumstein, Paolo Boffetta, Florence Bourgeois, Adam Brufsky, Peter J Collignon, Sally Cripps, Ioana A Cristea, Nigel Curtis, Benjamin Djulbegovic, Oliver Faude, Maria Elena Flacco, Gordon H Guyatt, George Hajishengallis, Lars G Hemkens, Tammy Hoffmann, Ari R Joffe, Terry P Klassen, Despina Koletsi, Dimitrios P Kontoyiannis, Ellen Kuhl, Carlo La Vecchia, Tea Lallukka, John Lambris, Michael Levitt, Spyros Makridakis, Helena C Maltezou, Lamberto Manzoli, Ana Marusic, Clio Mavragani, David Moher, Ben W Mol, Taulant Muka, Florian Naudet, Paul W Noble, Anna Nordström, Peter Nordström, Nikolaos Pandis, Stefania Papatheodorou, Chirag J Patel, Irene Petersen, Stefan Pilz, Nikolaus Plesnila, Anne-Louise Ponsonby, Manuel A Rivas, Andrea Saltelli, Manuel Schabus, Michaéla C Schippers, Holger Schünemann, Marco Solmi, Andreas Stang, Hendrik Streeck, Joachim P Sturmberg, Lehana Thabane, Brett D Thombs, Athanasios Tsakris, Simon N Wood, John P A Ioannidis

Faculty, Staff and Student Publications

Consensus statements can be very influential in medicine and public health. Some of these statements use systematic evidence synthesis but others fail on this front. Many consensus statements use panels of experts to deduce perceived consensus through Delphi processes. We argue that stacking of panel members toward one particular position or narrative is a major threat, especially in absence of systematic evidence review. Stacking may involve financial conflicts of interest, but nonfinancial conflicts of strong advocacy can also cause major bias. Given their emerging importance, we describe here how such consensus statements may be misleading, by analyzing in depth a …

Image-Guided Patient-Specific Optimization Of Catheter Placement For Convection-Enhanced Nanoparticle Delivery In Recurrent Glioblastoma, Chengyue Wu, David A Hormuth, Chase D Christenson, Ryan T Woodall, Michael R A Abdelmalik, William T Phillips, Thomas J R Hughes, Andrew J Brenner, Thomas E Yankeelov

Faculty, Staff and Student Publications

Background: Proper catheter placement for convection-enhanced delivery (CED) is required to maximize tumor coverage and minimize exposure to healthy tissue. We developed an image-based model to patient-specifically optimize the catheter placement for rhenium-186 (186Re)-nanoliposomes (RNL) delivery to treat recurrent glioblastoma (rGBM).

Methods: The model consists of the 1) fluid fields generated via catheter infusion, 2) dynamic transport of RNL, and 3) transforming RNL concentration to the SPECT signal. Patient-specific tissue geometries were assigned from pre-delivery MRIs. Model parameters were personalized with either 1) individual-based calibration with longitudinal SPECT images, or 2) population-based assignment via leave-one-out cross-validation. The concordance correlation coefficient …

Nanoscale Gold Nanoparticle (Gnp)-Laden Tumor Cell Model And Its Use For Estimation Of Intracellular Dose From Gnp-Induced Secondary Electrons, Sandun Jayarathna, Amrit Kaphle, Sunil Krishnan, Sang Hyun Cho

Faculty, Staff and Student Publications

Background: Gold nanoparticles (GNPs) accumulated within tumor cells have been shown to sensitize tumors to radiotherapy. From a physics point of view, the observed GNP-mediated radiosensitization is due to various downstream effects of the secondary electron (SE) production from internalized GNPs such as GNP-mediated dose enhancement. Over the years, numerous computational investigations on GNP-mediated dose enhancement/radiosensitization have been conducted. However, such investigations have relied mostly on simple cellular geometry models and/or artificial GNP distributions. Thus, it is at least desirable, if not necessary, to conduct further investigations using cellular geometry models that properly reflect realistic cell morphology as well as …

Large Language Models For Biomedicine: Foundations, Opportunities, Challenges, And Best Practices, Satya S Sahoo, Joseph M Plasek, Hua Xu, Özlem Uzuner, Trevor Cohen, Meliha Yetisgen, Hongfang Liu, Stéphane Meystre, Yanshan Wang

Faculty, Staff and Student Publications

Objectives: Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of …

Multi-Scale Variational Autoencoder For Imputation Of Missing Values In Untargeted Metabolomics Using Whole-Genome Sequencing Data, Chen Zhao, Kuan-Jui Su, Chong Wu, Xuewei Cao, Qiuying Sha, Wu Li, Zhe Luo, Tian Qing, Chuan Qiu, Lan Juan Zhao, Anqi Liu, Lindong Jiang, Xiao Zhang, Hui Shen, Weihua Zhou, Hong-Wen Deng

Faculty, Staff and Student Publications

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies.

Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics …

Tuberculosis Prevention: Current Strategies And Future Directions, Anca Vasiliu, Leonardo Martinez, Rishi K Gupta, Yohhei Hamada, Tara Ness, Alexander Kay, Maryline Bonnet, Martina Sester, Stefan H E Kaufmann, Christoph Lange, Anna M Mandalakas

Faculty, Staff and Students Publications

BACKGROUND: An estimated one fourth of the world's population is infected with Mycobacterium tuberculosis, and 5-10% of those infected develop tuberculosis in their lifetime. Preventing tuberculosis is one of the most underutilized but essential components of curtailing the tuberculosis epidemic. Moreover, current evidence illustrates that tuberculosis manifestations occur along a dynamic spectrum from infection to disease rather than a binary state as historically conceptualized. Elucidating determinants of transition between these states is crucial to decreasing the tuberculosis burden and reaching the END-TB Strategy goals as defined by the WHO. Vaccination, detection of infection, and provision of preventive treatment are key …

Diabetes Technology Meeting 2023, Tiffany Tian, Rachel E Aaron, Ashley Y Dunova, Johan H Jendle, David Kerr, Eda Cengiz, Andjela Drincic, John C Pickup, Kong Y Chen, Naomi Schwartz, Douglas B Muchmore, Halis K Akturk, Carol J Levy, Signe Schmidt, Riccardo Bellazzi, Alan H B Wu, Elias K Spanakis, Bijan Najafi, James Geoffrey Chase, Jane Jeffrie Seley, David C Klonoff

Center on Aging Staff Publications

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

Special Supplement Issue On Quality Assurance And Enrichment Of Biological And Biomedical Ontologies And Terminologies, Licong Cui, Ankur Agrawal

Faculty, Staff and Student Publications

Ontologies and terminologies serve as the backbone of knowledge representation in biomedical domains, facilitating data integration, interoperability, and semantic understanding across diverse applications. However, the quality assurance and enrichment of these resources remain an ongoing challenge due to the dynamic nature of biomedical knowledge. In this editorial, we provide an introductory summary of seven articles included in this special supplement issue for quality assurance and enrichment of biological and biomedical ontologies and terminologies. These articles span a spectrum of topics, such as development of automated quality assessment frameworks for Resource Description Framework (RDF) resources, identification of missing concepts in SNOMED …

Deployment-Related Cigarette Smoking Behaviors And Pulmonary Function Among Us Veterans, Jennifer R Maccarone, Olivia R Sterns, Andrew Timmons, Anna M Korpak, Nicholas L Smith, Karen S Nakayama, Coleen P Baird, Paul Ciminera, Farrah Kheradmand, Vincent S Fan, Jaime E Hart, Petros Koutrakis, Michael Jerrett, Ware G Kuschner, Octavian C Ioachimescu, Philippe R Montgrain, Susan P Proctor, Carrie A Redlich, Christine H Wendt, Paul D Blanc, Eric Garshick, Emily S Wan

Faculty, Staff and Students Publications

Introduction: The effects of smoking on lung function among post-9/11 Veterans deployed to environments with high levels of ambient particulate matter are incompletely understood.

Materials and methods: We analyzed interim data (04/2018-03/2020) from the Veterans Affairs (VA) Cooperative Studies Program #595, "Service and Health Among Deployed Veterans". Veterans with ≥1 land-based deployments enrolled at 1 of 6 regional Veterans Affairs sites completed questionnaires and spirometry. Multivariable linear regression models assessed associations between cigarette smoking (cumulative, deployment-related and non-deployment-related) with pulmonary function.

Results: Among 1,836 participants (mean age 40.7 ± 9.6, 88.6% male), 44.8% (n = 822) were ever-smokers (mean age …

Perceptions Of Hiv-Related Comorbidities And Usability Of A Virtual Environment For Cardiovascular Disease Prevention Education In Sexual Minority Men With Hiv: Formative Phases Of A Pilot Randomized Controlled Trial, S Raquel Ramos, Harmony Reynolds, Constance Johnson, Gail Melkus, Trace Kershaw, Julian F Thayer, Allison Vorderstrasse

Faculty, Staff and Student Publications

Background: Sexual minority men with HIV are at an increased risk of cardiovascular disease (CVD) and have been underrepresented in behavioral research and clinical trials.

Objective: This study aims to explore perceptions of HIV-related comorbidities and assess the interest in and usability of a virtual environment for CVD prevention education in Black and Latinx sexual minority men with HIV.

Methods: This is a 3-phase pilot behavioral randomized controlled trial. We report on formative phases 1 and 2 that informed virtual environment content and features using qualitative interviews, usability testing, and beta testing with a total of 25 individuals. In phase …

Sexannodb, A Knowledgebase Of Sex-Specific Regulations From Multi-Omics Data Of Human Cancers, Mengyuan Yang, Yuzhou Feng, Jiajia Liu, Hong Wang, Sijia Wu, Weiling Zhao, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

Background

Sexual differences across molecular levels profoundly impact cancer biology and outcomes. Patient gender significantly influences drug responses, with divergent reactions between men and women to the same drugs. Despite databases on sex differences in human tissues, understanding regulations of sex disparities in cancer is limited. These resources lack detailed mechanistic studies on sex-biased molecules.

Methods

In this study, we conducted a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, delving into sex-biased effects. Our analyses encompassed sex-biased competitive endogenous RNA networks, regulatory networks involving sex-biased RNA binding protein-exon skipping events, sex-biased transcription factor-gene regulatory networks, …

An Online Nanoinformatics Platform Empowering Computational Modeling Of Nanomaterials By Nanostructure Annotations And Machine Learning Toolkits., Tong Wang, Daniel P Russo, Philip Demokritou, Xuelian Jia, Heng Huang, Xinyu Yang, Hao Zhu

College of Science & Mathematics Departmental Research

Modern nanotechnology has generated numerous datasets from in vitro and in vivo studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machine learning studies. Here, we report a nanoinformatics platform that accurately annotates nanostructures into machine-readable data files and provides modeling toolkits. This platform, accessible to the public at https://vinas-toolbox.com/, has annotated nanostructures of 14 material types. The associated nanodescriptor data and assay test results are appropriate for modeling purposes. The modeling toolkits enable data standardization, data visualization, and machine learning model development to predict properties and bioactivities …

Covid-19 And Mental Illnesses In Vaccinated And Unvaccinated People, Venexia M. Walker, Praveetha Patalay, Jose Ignacio Cuitun Coronado, Rachel Denholm, Harriet Forbes, Jean Stafford, Bettina Moltrecht, Alex Walker, Tom Palmer, Ellen J. Thompson, Kurt Taylor, Genevieve Cezard, Elsie M. F. Horne, Yinghui Wei, Marwa Al Arab, Rochelle Knight, Jon Massey, Louis Fisher, Simon Davy, Amir Mehrkar, Seb Bacon, Ben Goldacre, Angela Wood, Nishi Chaturvedi, John Macleod, Ann John, Jonathan A. C. Sterne, Longitudinal Health And Wellbeing Covid-19 National Core Study

School of Engineering, Computing and Mathematics

Importance Associations have been found between COVID-19 and subsequent mental illness in both hospital- and population-based studies. However, evidence regarding which mental illnesses are associated with COVID-19 by vaccination status in these populations is limited.Objective To determine which mental illnesses are associated with diagnosed COVID-19 by vaccination status in both hospitalized patients and the general population.Design, Setting, and Participants This study was conducted in 3 cohorts, 1 before vaccine availability followed during the wild-type/Alpha variant eras (January 2020-June 2021) and 2 (vaccinated and unvaccinated) during the Delta variant era (June-December 2021). With National Health Service England approval, OpenSAFELY-TPP was used …

Clinical And Genomic-Based Decision Support System To Define The Optimal Timing Of Allogeneic Hematopoietic Stem-Cell Transplantation In Patients With Myelodysplastic Syndromes, Cristina Astrid Tentori, Caterina Gregorio, Marie Robin, Nico Gagelmann, Carmelo Gurnari, Somedeb Ball, Juan Carlos Caballero Berrocal, Luca Lanino, Saverio D'Amico, Marta Spreafico, Giulia Maggioni, Erica Travaglino, Elisabetta Sauta, Manja Meggendorfer, Lin-Pierre Zhao, Alessia Campagna, Victor Savevski, Armando Santoro, Najla Al Ali, David Sallman, Francesc Sole, Guillermo Garcia-Manero, Ulrich Germing, Nicolaus Kroger, Shahram Kordasti, Valeria Santini, Guillermo Sanz, Wolfgang Kern, Uwe Platzbecker, Maria Diez-Campelo, Jaroslaw P Maciejewski, Lionel Ades, Pierre Fenaux, Torsten Haferlach, Amer M Zeidan, Gastone Castellani, Rami Komrokji, Francesca Ieva, Matteo Giovanni Della Porta, Genomed4all And Synthema Consortiums

Faculty, Staff and Student Publications

Purpose: Allogeneic hematopoietic stem-cell transplantation (HSCT) is the only potentially curative treatment for patients with myelodysplastic syndromes (MDS). Several issues must be considered when evaluating the benefits and risks of HSCT for patients with MDS, with the timing of transplantation being a crucial question. Here, we aimed to develop and validate a decision support system to define the optimal timing of HSCT for patients with MDS on the basis of clinical and genomic information as provided by the Molecular International Prognostic Scoring System (IPSS-M).

Patients and methods: We studied a retrospective population of 7,118 patients, stratified into training and validation …

Hybridizing Mechanistic Modeling And Deep Learning For Personalized Survival Prediction After Immune Checkpoint Inhibitor Immunotherapy, Joseph D Butner, Prashant Dogra, Caroline Chung, Eugene J Koay, James W Welsh, David S Hong, Vittorio Cristini, Zhihui Wang

Faculty, Staff and Student Publications

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, …

Development Of Vla4 And Cxcr4 Antagonists For The Mobilization Of Hematopoietic Stem And Progenitor Cells, Peter G Ruminski, Michael P Rettig, John F Dipersio

2020-Current year OA Pubs

The treatment of patients diagnosed with hematologic malignancies typically includes hematopoietic stem cell transplantation (HSCT) as part of a therapeutic standard of care. The primary graft source of hematopoietic stem and progenitor cells (HSPCs) for HSCT is mobilized from the bone marrow into the peripheral blood of allogeneic donors or patients. More recently, these mobilized HSPCs have also been the source for gene editing strategies to treat diseases such as sickle-cell anemia. For a HSCT to be successful, it requires the infusion of a sufficient number of HSPCs that are capable of adequate homing to the bone marrow niche and …

3d Chromatin Architecture, Brd4, And Mediator Have Distinct Roles In Regulating Genome-Wide Transcriptional Bursting And Gene Network, Pawel Trzaskoma, Seolkyoung Jung, Aleksandra Pękowska, Christopher H Bohrer, Xiang Wang, Faiza Naz, Stefania Dell'orso, Wendy D Dubois, Ana Olivera, Supriya V Vartak, Yongbing Zhao, Subhashree Nayak, Andrew Overmiller, Maria I Morasso, Vittorio Sartorelli, Daniel R Larson, Carson C Chow, Rafael Casellas, John J O'Shea

Faculty, Staff and Student Publications

Discontinuous transcription is evolutionarily conserved and a fundamental feature of gene regulation; yet, the exact mechanisms underlying transcriptional bursting are unresolved. Analyses of bursting transcriptome-wide have focused on the role of cis-regulatory elements, but other factors that regulate this process remain elusive. We applied mathematical modeling to single-cell RNA sequencing data to infer bursting dynamics transcriptome-wide under multiple conditions to identify possible molecular mechanisms. We found that Mediator complex subunit 26 (MED26) primarily regulates frequency, MYC regulates burst size, while cohesin and Bromodomain-containing protein 4 (BRD4) can modulate both. Despite comparable effects on RNA levels among these perturbations, acute depletion …

Whole Genome Sequencing Based Analysis Of Inflammation Biomarkers In The Trans-Omics For Precision Medicine (Topmed) Consortium, Min-Zhi Jiang, Sheila M Gaynor, Xihao Li, Eric Van Buren, Adrienne Stilp, Erin Buth, Fei Fei Wang, Regina Manansala, Stephanie M Gogarten, Zilin Li, Linda M Polfus, Shabnam Salimi, Joshua C Bis, Nathan Pankratz, Lisa R Yanek, Peter Durda, Russell P Tracy, Stephen S Rich, Jerome I Rotter, Braxton D Mitchell, Joshua P Lewis, Bruce M Psaty, Katherine A Pratte, Edwin K Silverman, Robert C Kaplan, Christy Avery, Kari E North, Rasika A Mathias, Nauder Faraday, Honghuang Lin, Biqi Wang, April P Carson, Arnita F Norwood, Richard A Gibbs, Charles Kooperberg, Jessica Lundin, Ulrike Peters, Josée Dupuis, Lifang Hou, Myriam Fornage, Emelia J Benjamin, Alexander P Reiner, Russell P Bowler, Xihong Lin, Paul L Auer, Laura M Raffield, Nhlbi Trans-Omics For Precision Medicine (Topmed) Consortium, Topmed Inflammation Working Group

Faculty, Staff and Student Publications

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant …

Mucosal Immunization With Chad-Sars-Cov-2-S Prevents Sequential Transmission Of Sars-Cov-2 To Unvaccinated Hamsters, Tamarand L Darling, Houda H Harastani, Astha Joshi, Traci L Bricker, Nadia Soudani, Kuljeet Seehra, Ahmed O Hassan, Michael S Diamond, Adrianus C M Boon

2020-Current year OA Pubs

COVID-19 vaccines have successfully reduced severe disease and death after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Nonetheless, COVID-19 vaccines are variably effective in preventing transmission and symptomatic SARS-CoV-2 infection. Here, we evaluated the impact of mucosal or intramuscular vaccine immunization on airborne infection and transmission of SARS-CoV-2 in Syrian hamsters. Immunization of the primary contact hamsters with a mucosal chimpanzee adenoviral-vectored vaccine (ChAd-CoV-2-S), but not intramuscular messenger RNA (mRNA) vaccine, reduced infectious virus titers ~100-fold and 100,000-fold in the upper and lower respiratory tract of the primary contact hamster following SARS-CoV-2 exposure. This reduction in virus titer in …

Renal Mass Imaging Modalities: Does Body Mass Index (Bmi) Matter?, Young Son, Mark E. Quiring, Raeann M. Dalton, Brian Thomas, Noah Davidson, Dayna Devincentz, Collin Payne, Sahil H. Parikh, Benjamin A. Fink, Thomas Mueller, Gordon Brown

Jefferson Health - New Jersey Papers

PURPOSE: Accurate measurement of renal mass size is crucial in the management of renal cancer. With the burdensome cost of imaging yet its need for management, a better understanding of the variability among patients when determining mass size remains of urgent importance. Current guidelines on optimal imaging are limited, especially with respect to body mass index (BMI). The aim of this study is to discern which modalities accurately measure renal mass size and whether BMI influences such accuracy.

METHODS: A multi-institutional chart review was performed for adult patients undergoing partial or radical nephrectomy between 2018 and 2021, with 236 patients …

Transformer-Based Deep Learning Prediction Of 10-Degree Humphrey Visual Field Tests From 24-Degree Data, Min Shi, Anagha Lokhande, Yu Tian, Yan Luo, Mohammad Eslami, Saber Kazeminasab, Tobias Elze, Lucy Shen, Louis Pasquale, Sarah Wellik, Carlos Gustavo De Moraes, Jonathan Myers, Nazlee Zebardast, David Friedman, Michael Boland, Mengyu Wang

Wills Eye Hospital Papers

PURPOSE: To predict 10-2 Humphrey visual fields (VFs) from 24-2 VFs and associated non-total deviation features using deep learning.

METHODS: We included 5189 reliable 24-2 and 10-2 VF pairs from 2236 patients, and 28,409 reliable pairs of macular OCT scans and 24-2 VF from 19,527 eyes of 11,560 patients. We developed a transformer-based deep learning model using 52 total deviation values and nine VF test features to predict 68 10-2 total deviation values. The mean absolute error, root mean square error, and the R2 were evaluation metrics. We further evaluated whether the predicted 10-2 VFs can improve the structure-function relationship …

Patient-Centered Clinical Decision Support Challenges And Opportunities Identified From Workflow Execution Models, Dean F Sittig, Aziz Boxwala, Adam Wright, Courtney Zott, Nicole A Gauthreaux, James Swiger, Edwin A Lomotan, Prashila Dullabh

Faculty, Staff and Student Publications

OBJECTIVE: To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows.

METHODS: To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee.

RESULTS AND DISCUSSION: We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new …

Automatic Uncovering Of Patient Primary Concerns In Portal Messages Using A Fusion Framework Of Pretrained Language Modelsautomatic Uncovering Of Patient Primary Concerns In Portal Messages Using A Fusion Framework Of Pretrained Language Models, Yang Ren, Yuqi Wu, Jungwei W Fan, Aditya Khurana, Sunyang Fu, Dezhi Wu, Hongfang Liu, Ming Huang

Faculty, Staff and Student Publications

OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs-a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care.

MATERIALS AND METHODS: We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural …

Artificial Intelligence In Fusion Protein Three-Dimensional Structure Prediction: Review And Perspective, Himansu Kumar, Pora Kim

Faculty, Staff and Student Publications

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action …

High Prevalence Of Artifacts In Optical Coherence Tomography With Adequate Signal Strength, Wei-Chun Lin, Aaron Coyner, Charles Amankwa, Abigail Lucero, Gadi Wollstein, Joel Schuman, Hiroshi Ishikawa

Wills Eye Hospital Papers

PURPOSE: This study aims to investigate the prevalence of artifacts in optical coherence tomography (OCT) images with acceptable signal strength and evaluate the performance of supervised deep learning models in improving OCT image quality assessment.

METHODS: We conducted a retrospective study on 4555 OCT images from 546 patients, with each image having an acceptable signal strength (≥6). A comprehensive analysis of prevalent OCT artifacts was performed, and five pretrained convolutional neural network models were trained and tested to infer images based on quality.

RESULTS: Our results showed a high prevalence of artifacts in OCT images with acceptable signal strength. Approximately …

Checklist For Reproducibility Of Deep Learning In Medical Imaging, Mana Moassefi, Yashbir Singh, Gian Marco Conte, Bardia Khosravi, Pouria Rouzrokh, Sanaz Vahdati, Nabile Safdar, Linda Moy, Felipe Kitamura, Amilcare Gentili, Paras Lakhani, Nina Kottler, Safwan Halabi, Joseph Yacoub, Yuankai Hou, Khaled Younis, Bradley Erickson, Elizabeth Krupinski, Shahriar Faghani

Department of Radiology Faculty Papers

The application of deep learning (DL) in medicine introduces transformative tools with the potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring transparent documentation is essential for researchers to enhance reproducibility and refine techniques. Our study addresses the unique challenges presented by DL in medical imaging by developing a comprehensive checklist using the Delphi method to enhance reproducibility and reliability in this dynamic field. We compiled a preliminary checklist based on a comprehensive review of existing checklists and relevant literature. A panel of 11 experts in medical imaging and DL assessed these items using Likert scales, with two survey …

Markov Models For Clinical Decision-Making In Radiation Oncology: A Systematic Review, Lucas B Mccullum, Aysenur Karagoz, Cem Dede, Raul Garcia, Fatemeh Nosrat, Mehdi Hemmati, Seyedmohammadhossein Hosseinian, Andrew J Schaefer, Clifton D Fuller

Faculty, Staff and Student Publications

The intrinsic stochasticity of patients' response to treatment is a major consideration for clinical decision-making in radiation therapy. Markov models are powerful tools to capture this stochasticity and render effective treatment decisions. This paper provides an overview of the Markov models for clinical decision analysis in radiation oncology. A comprehensive literature search was conducted within MEDLINE using PubMed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies published from 2000 to 2023 were considered. Selected publications were summarized in two categories: (i) studies that compare two (or more) fixed treatment policies using Monte Carlo simulation …

A Cell Cycle-Aware Network For Data Integration And Label Transferring Of Single-Cell Rna-Seq And Atac-Seq, Jiajia Liu, Jian Ma, Jianguo Wen, Xiaobo Zhou

Faculty, Staff and Student Publications

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell …

Mapping The Single-Cell Differentiation Landscape Of Osteosarcoma, Danh D Truong, Corey Weistuch, Kevin A Murgas, Prasad Admane, Bridgette L King, Jes Chauviere Lee, Salah-E Lamhamedi-Cherradi, Jyothishmathi Swaminathan, Najat C Daw, Nancy Gordon, Vidya Gopalakrishnan, Richard G Gorlick, Neeta Somaiah, Joseph O Deasy, Antonios G Mikos, Allen Tannenbaum, Joseph Ludwig

Faculty, Staff and Student Publications

Purpose: The genetic intratumoral heterogeneity observed in human osteosarcomas poses challenges for drug development and the study of cell fate, plasticity, and differentiation, which are processes linked to tumor grade, cell metastasis, and survival.

Experimental design: To pinpoint errors in osteosarcoma differentiation, we transcriptionally profiled 31,527 cells from a tissue-engineered model that directs mesenchymal stem cells toward adipogenic and osteoblastic fates. Incorporating preexisting chondrocyte data, we applied trajectory analysis and non-negative matrix factorization to generate the first human mesenchymal differentiation atlas.

Results: This "roadmap" served as a reference to delineate the cellular composition of morphologically complex osteosarcoma tumors and quantify …

Cluster-Based Toxicity Estimation Of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability By Using Whole Dose-Volume Histograms For Cohort Risk Stratification, Seyedmohammadhossein Hosseinian, Mehdi Hemmati, Cem Dede, Travis C Salzillo, Lisanne V Van Dijk, Abdallah S R Mohamed, Stephen Y Lai, Andrew J Schaefer, Clifton D Fuller

Faculty, Staff and Student Publications

PURPOSE: Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of data and provide a clinically reliable model for ORN risk evaluation through an unsupervised-learning analysis that incorporates the whole radiation dose distribution on the mandible.

METHODS AND MATERIALS: The analysis was conducted on retrospective data of 1259 patients with head and neck cancer treated at The University of Texas MD Anderson Cancer Center between 2005 and 2015. During a minimum 12-month posttherapy follow-up period, 173 patients …