Digital Commons Network^™

Π-Hub: The Proteomic Navigator Of The Human Body, Fuchu He, Ruedi Aebersold, Mark S Baker, Xiuwu Bian, Xiaochen Bo, Daniel W Chan, Cheng Chang, Luonan Chen, Xiangmei Chen, Yu-Ju Chen, Heping Cheng, Ben C Collins, Fernando Corrales, Jürgen Cox, Weinan E, Jennifer E Van Eyk, Jia Fan, Pouya Faridi, Daniel Figeys, George Fu Gao, Wen Gao, Zu-Hua Gao, Keisuke Goda, Wilson Wen Bin Goh, Dongfeng Gu, Changjiang Guo, Tiannan Guo, Yuezhong He, Albert J R Heck, Henning Hermjakob, Tony Hunter, Narayanan Gopalakrishna Iyer, Ying Jiang, Connie R Jimenez, Lokesh Joshi, Neil L Kelleher, Ming Li, Yang Li, Qingsong Lin, Cui Hua Liu, Fan Liu, Guang-Hui Liu, Yansheng Liu, Zhihua Liu, Teck Yew Low, Ben Lu, Matthias Mann, Anming Meng, Robert L Moritz, Edouard Nice, Guang Ning, Gilbert S Omenn, Christopher M Overall, Giuseppe Palmisano, Yaojin Peng, Charles Pineau, Terence Chuen Wai Poon, Anthony W Purcell, Jie Qiao, Roger R Reddel, Phillip J Robinson, Paola Roncada, Chris Sander, Jiahao Sha, Erwei Song, Sanjeeva Srivastava, Aihua Sun, Siu Kwan Sze, Chao Tang, Liujun Tang, Ruijun Tian, Juan Antonio Vizcaíno, Chanjuan Wang, Chen Wang, Xiaowen Wang, Xinxing Wang, Yan Wang, Tobias Weiss, Mathias Wilhelm, Robert Winkler, Bernd Wollscheid, Limsoon Wong, Linhai Xie, Wei Xie, Tao Xu, Tianhao Xu, Liying Yan, Jing Yang, Xiao Yang, John Yates, Tao Yun, Qiwei Zhai, Bing Zhang, Hui Zhang, Lihua Zhang, Lingqiang Zhang, Pingwen Zhang, Yukui Zhang, Yu Zi Zheng, Qing Zhong, Yunping Zhu, Π-Hub Consortium

Faculty, Staff and Students Publications

The human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies. Recent advances in proteomic technology and computational sciences now provide opportunities to investigate the intricate biology of the human body …

Health Implications Of Racial Differences In Serum Growth Differentiation Factor Levels Among Men With Obesity, Siresha Bathina, Virginia Fuenmayor Lopez, Mia Prado, Elliot Ballato, Georgia Colleluori, Maryam Tetlay, Dennis Tan Villareal, Sanjay Mediwala, Rui Chen, Clifford Qualls, Reina Armamento-Villareal

Faculty, Staff and Students Publications

Growth differentiation factor (GDF15) has been considered a biomarker and recently a hormonal driver for diseases in different populations. However, the role of GDF15 as a biomarker of health outcomes in obese men from different racial/ethnic background has not been evaluated. The objective of this study was to investigate the racial/ethnic differences on the relationship between GDF15 and markers of glucometabolic status, hormonal profile, body composition and bone mineral density (BMD) in obese men. One hundred ninety-three obese men from diverse racial/ethnic backgrounds were enrolled. BMD and body composition were measured by dual energy X-ray absorptiometry. Serum GDF15, osteocalcin, C-terminal …

Experiences In Providing A Community Educational Resource For The All Of Us Researcher Workbench, Deborah I Ritter, Jinyoung Byun, Jun Wang, Stephen Richards, Pamela N Luna, Laterrica Williams, Julie R Coleman, Jasmine N Baker, Shamika Ketkar, Ashley M Butler, Latanya Hammonds-Odie, Elizabeth G Atkinson, Kim C Worley, Debra D Murray, Brendan Lee, Steven E Scherer

Faculty, Staff and Students Publications

OBJECTIVE: Educational offerings to fill the bioinformatics knowledge gap are a key component to enhancing access and use of health data from the All of Us Research Program. We developed a Train the Trainer-based, innovative training series including project-based learning, modular on-demand demonstrations, and unstructured tutorial time as a model for educational engagement in the All of Us community.

MATERIALS AND METHODS: We highlight our training modules and content, with training survey data informing cycles of development in the creation of a 6-module training series with modular demonstrations.

RESULTS: We have conducted 2 public iterations of the Train the Trainer …

Correlating Clinical Course With Baseline Subcortical Shape In Provisional Tic Disorder, Tiffanie Che, Soyoung Kim, Deanna J Greene, Ashley Heywood, Jimin Ding, Tamara Hershey, Bradley L Schlaggar, Kevin J Black, Lei Wang

2020-Current year OA Pubs

OBJECTIVE: This study examined children at the onset of tic disorder (tics for less than 9 months: NT group), a population on which little research exists. Here, we investigate relationships between the baseline shape and volume of subcortical nuclei, diagnosis, and tic symptom outcomes.

METHODS: 187 children were assessed at baseline and a 12-month follow-up: 88 with NT, 60 tic-free healthy controls (HC), and 39 with chronic tic disorder/Tourette syndrome (TS), using T1-weighted MRI and total tic scores (TTS) from the Yale Global Tic Severity Scale to evaluate symptom change. Subcortical surface maps were generated using FreeSurfer-initialized large deformation diffeomorphic …

A Novel Phenotype Imputation Method With Copula Model, Jianjun Zhang, Jane Zizhen Zhao, Samantha Gonzales, Xuexia Wang, Qiuying Sha

Michigan Tech Publications

BACKGROUND: Jointly analyzing multiple phenotype/traits may increase power in genetic association studies by aggregating weak genetic effects. The chance that at least one phenotype is missing increases exponentially as the number of phenotype increases especially for a real dataset. It is a common practice to discard individuals with missing phenotype or phenotype with a large proportion of missing values. Such a discarding method may lead to a loss of power or even an insufficient sample size for analysis. To our knowledge, many existing phenotype imputing methods are built on multivariate normal assumptions for analysis. Violation of these assumptions may lead …

De-Identification Is Not Enough: A Comparison Between De-Identified And Synthetic Clinical Notes, Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang

Faculty, Staff and Student Publications

For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative models and the breakthroughs in large generative language models raise the question of whether synthetically generated clinical notes could be a viable alternative to real notes for research purposes. In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated …

Lung Tissue Multilayer Network Analysis Uncovers The Molecular Heterogeneity Of Chronic Obstructive Pulmonary Disease, Nuria Olvera, Jon Sánchez-Valle, Iker Núñez-Carpintero, Joselyn Rojas-Quintero, Guillaume Noell, Sandra Casas-Recasens, Alen Faiz, Philip Hansbro, Angela Guirao, Rosalba Lepore, Davide Cirillo, Alvar Agustí, Francesca Polverino, Alfonso Valencia, Rosa Faner

Faculty, Staff and Students Publications

Rationale: Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition. Objectives: We hypothesized that the unbiased integration of different COPD lung omics using a novel multilayer approach might unravel mechanisms associated with clinical characteristics.

Methods: We profiled mRNA, microRNA and methylome in lung tissue samples from 135 former smokers with COPD. For each omic (layer), we built a patient network on the basis of molecular similarity. The three networks were used to build a multilayer network, and optimization of multiplex modularity was used to identify patient communities across the three distinct layers. Uncovered communities were related to clinical features.

Measurements …

Dilated Cardiomyopathy-Associated Skeletal Muscle Actin (Acta1) Mutation R256h Disrupts Actin Structure And Function And Causes Cardiomyocyte Hypocontractility, Ankit Garg, Silvia Jansen, Lina Greenberg, Rui Zhang, Kory J Lavine, Michael J Greenberg

2020-Current year OA Pubs

Skeletal muscle actin (ACTA1) mutations are a prevalent cause of skeletal myopathies consistent with ACTA1's high expression in skeletal muscle. Rare de novo mutations in ACTA1 associated with combined cardiac and skeletal myopathies have been reported, but ACTA1 represents only ~20% of the total actin pool in cardiomyocytes, making its role in cardiomyopathy controversial. Here we demonstrate how a mutation in an actin isoform expressed at low levels in cardiomyocytes can cause cardiomyopathy by focusing on a unique ACTA1 variant, R256H. We previously identified this variant in a family with dilated cardiomyopathy, who had reduced systolic function without clinical skeletal …

A Reward Self-Bias Leads To More Optimal Foraging For Ourselves Than Others, Luis Sebastian Contreras-Huerta, M. Andrea Pisauro, Svenja Küchenhoff, Arno Gekiere, Campbell Le Heron, Patricia L. Lockwood, Matthew A.J. Apps

School of Psychology

People are self-biased for rewards. We place a higher value on rewards if we receive them than if other people do. However, existing work has ignored one of the most powerful theorems from behavioural ecology of how animals seek resources in everyday life, the Marginal Value Theorem (MVT), which accounts for optimal behaviour for maximising resources intake rate. Does this self-bias help humans maximise rewards when foraging for their own benefit compared to foraging for the benefit of others? Participants had to decide when to leave patches where reward intake was gradually depleting, in environments with different average reward rates. …

Enhancing Data Standards To Advance Translation In Spinal Cord Injury, Vanessa K. Noonan, Suzanne Humphreys, Fin Biering-Sørensen, Susan Charlifue, Yuying Chen, James D. Guest, Linda A. T. Jones, Jennifer French, Eva Widerström-Noga, Vance P. Lemmon, Allen W. Heinemann, Jan M. Schwab, Aaron A. Phillips, Marzieh M. Rizi, John L. K. Kramer, Catherine R. Jutzeler, Abel Torres-Espin

Department of Physical Therapy Faculty Papers

Data standards are available for spinal cord injury (SCI). The International SCI Data Sets were created in 2002 and there are currently 27 freely available. In 2014 the National Institute of Neurological Disorders and Stroke developed clinical common data elements to promote clinical data sharing in SCI. The objective of this paper is to provide an overview of SCI data standards, describe learnings from the traumatic brain injury (TBI) field using data to enhance research and care, and discuss future opportunities in SCI. Given the complexity of SCI, frameworks such as a systems medicine approach and Big Data perspective have …

Estimating The Number Of Polygenic Diseases Among Six Mutually Exclusive Entities Of Non-Tumors And Cancer, C I Edvard Smith, Jan A Burger, Rula Zain

Faculty, Staff and Student Publications

In the era of precision medicine with increasing amounts of sequenced cancer and non-cancer genomes of different ancestries, we here enumerate the resulting polygenic disease entities. Based on the cell number status, we first identified six fundamental types of polygenic illnesses, five of which are non-cancerous. Like complex, non-tumor disorders, neoplasms normally carry alterations in multiple genes, including in 'Drivers' and 'Passengers'. However, tumors also lack certain genetic alterations/epigenetic changes, recently named 'Goners', which are toxic for the neoplasm and potentially constitute therapeutic targets. Drivers are considered essential for malignant transformation, whereas environmental influences vary considerably among both types of …

Data-Driven Cutoff Selection For The Patient Health Questionnaire-9 Depression Screening Tool, Brooke Levis, Parash Mani Bhandari, Dipika Neupane, Suiqiong Fan, Ying Sun, Chen He, Yin Wu, Ankur Krishnan, Zelalem Negeri, Mahrukh Imran, Danielle B Rice, Kira E Riehm, Marleine Azar, Alexander W Levis, Jill Boruff, Pim Cuijpers, Simon Gilbody, John P A Ioannidis, Lorie A Kloda, Scott B Patten, Roy C Ziegelstein, Daphna Harel, Yemisi Takwoingi, Sarah Markham, Sultan H Alamri, Dagmar Amtmann, Bruce Arroll, Liat Ayalon, Hamid R Baradaran, Anna Beraldi, Charles N Bernstein, Arvin Bhana, Charles H Bombardier, Ryna Imma Buji, Peter Butterworth, Gregory Carter, Marcos H Chagas, Juliana C N Chan, Lai Fong Chan, Dixon Chibanda, Kerrie Clover, Aaron Conway, Yeates Conwell, Federico M Daray, Janneke M De Man-Van Ginkel, Jesse R Fann, Felix H Fischer, Sally Field, Jane R W Fisher, Daniel S S Fung, Bizu Gelaye, Leila Gholizadeh, Felicity Goodyear-Smith, Eric P Green, Catherine G Greeno, Brian J Hall, Liisa Hantsoo, Martin Härter, Leanne Hides, Stevan E Hobfoll, Simone Honikman, Thomas Hyphantis, Masatoshi Inagaki, Maria Iglesias-Gonzalez, Hong Jin Jeon, Nathalie Jetté, Mohammad E Khamseh, Kim M Kiely, Brandon A Kohrt, Yunxin Kwan, Maria Asunción Lara, Holly F Levin-Aspenson, Shen-Ing Liu, Manote Lotrakul, Sonia R Loureiro, Bernd Löwe, Nagendra P Luitel, Crick Lund, Ruth Ann Marrie, Laura Marsh, Brian P Marx, Anthony Mcguire, Sherina Mohd Sidik, Tiago N Munhoz, Kumiko Muramatsu, Juliet E M Nakku, Laura Navarrete, Flávia L Osório, Brian W Pence, Philippe Persoons, Inge Petersen, Angelo Picardi, Stephanie L Pugh, Terence J Quinn, Elmars Rancans, Sujit D Rathod, Katrin Reuter, Alasdair G Rooney, Iná S Santos, Miranda T Schram, Juwita Shaaban, Eileen H Shinn, Abbey Sidebottom, Adam Simning, Lena Spangenberg, Lesley Stafford, Sharon C Sung, Keiko Suzuki, Pei Lin Lynnette Tan, Martin Taylor-Rowan, Thach D Tran, Alyna Turner, Christina M Van Der Feltz-Cornelis, Thandi Van Heyningen, Paul A Vöhringer, Lynne I Wagner, Jian Li Wang, David Watson, Jennifer White, Mary A Whooley, Kirsty Winkley, Karen Wynter, Mitsuhiko Yamada, Qing Zhi Zeng, Yuying Zhang, Brett D Thombs, Andrea Benedetti, Depression Screening Data (Depressd) Phq Group

Faculty, Staff and Students Publications

IMPORTANCE: Test accuracy studies often use small datasets to simultaneously select an optimal cutoff score that maximizes test accuracy and generate accuracy estimates.

OBJECTIVE: To evaluate the degree to which using data-driven methods to simultaneously select an optimal Patient Health Questionnaire-9 (PHQ-9) cutoff score and estimate accuracy yields (1) optimal cutoff scores that differ from the population-level optimal cutoff score and (2) biased accuracy estimates.

DESIGN, SETTING, AND PARTICIPANTS: This study used cross-sectional data from an existing individual participant data meta-analysis (IPDMA) database on PHQ-9 screening accuracy to represent a hypothetical population. Studies in the IPDMA database compared participant PHQ-9 …

Using Causal Diagrams Within The Grading Of Recommendations, Assessment, Development And Evaluation Framework To Evaluate Confounding Adjustment In Observational Studies, Kevin Mcintyre, Karina N Tassiopoulos, Curtis Jeffrey, Saverio Stranges, Janet Martin

Epidemiology and Biostatistics Publications

BACKGROUND AND OBJECTIVES: The current Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system instructs appraisers to evaluate whether individual observational studies have sufficiently adjusted for confounding. However, it does not provide an explicit, transparent, or reproducible method for doing so. This article explores how implementing causal graphs into the GRADE framework can help appraisers and end-users of GRADE products to evaluate the adequacy of confounding control from observational studies.

METHODS: Using modern epidemiological theory, we propose a system for incorporating causal diagrams into the GRADE process to assess confounding control.

RESULTS: Integrating causal graphs into the GRADE framework enables …

Prediction Of Individual Weight Loss Using Supervised Learning: Findings From The CalerieTm 2 Study, Christina Glasbrenner, Christoph Höchsmann, Carl F. Pieper, Paulina Wasserfurth, James L. Dorling, Corby K. Martin, Leanne M. Redman, Karsten Koehler

Clinical Research Faculty Publications

Background: Predicting individual weight loss (WL) responses to lifestyle interventions is challenging but might help practitioners and clinicians select the most promising approach for each individual. Objective: The primary aim of this study was to develop machine learning (ML) models to predict individual WL responses using only variables known before starting the intervention. In addition, we used ML to identify pre-intervention variables influencing the individual WL response. Methods: We used 12-mo data from the comprehensive assessment of long-term effects of reducing intake of energy (CALERIETM) phase 2 study, which aimed to analyze the long-term effects of caloric restriction on human …

Toward A Responsible Future: Recommendations For Ai-Enabled Clinical Decision Support, Steven Labkoff, Bilikis Oladimeji, Joseph Kannry, Anthony Solomonides, Russell Leftwich, Eileen Koski, Amanda L Joseph, Monica Lopez-Gonzalez, Lee A Fleisher, Kimberly Nolen, Sayon Dutta, Deborah R Levy, Amy Price, Paul J Barr, Jonathan D Hron, Baihan Lin, Gyana Srivastava, Nuria Pastor, Unai Sanchez Luque, Tien Thi Thuy Bui, Reva Singh, Tayler Williams, Mark G Weiner, Tristan Naumann, Dean F Sittig, Gretchen Purcell Jackson, Yuri Quintana

Faculty, Staff and Student Publications

BACKGROUND: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging.

OBJECTIVES: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients.

MATERIALS AND METHODS: In May …

A Site-Wise Reliability Analysis Of The Abcd Diffusion Fractional Anisotropy And Cortical Thickness: Impact Of Scanner Platforms, Yezhi Pan, L Elliot Hong, Ashley Acheson, Paul M Thompson, Neda Jahanshad, Alyssa H Zhu, Jiaao Yu, Chixiang Chen, Tianzhou Ma, Ho-Ling Liu, Jelle Veraart, Els Fieremans, Nicole R Karcher, Peter Kochunov, Shuo Chen

Faculty, Staff and Student Publications

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce nonbiological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation …

Fractionated Photoimmunotherapy Stimulates An Anti-Tumour Immune Response: An Integrated Mathematical And In Vitro Study, Mohammad U Zahid, Matthew Waguespack, Rebecca C Harman, Eric M Kercher, Shubhankar Nath, Tayyaba Hasan, Imran Rizvi, Bryan Q Spring, Heiko Enderling

Faculty, Staff and Student Publications

Background: Advanced epithelial ovarian cancer (EOC) has high recurrence rates due to disseminated initial disease presentation. Cytotoxic phototherapies, such as photodynamic therapy (PDT) and photoimmunotherapy (PIT, cell-targeted PDT), have the potential to treat disseminated malignancies due to safe intraperitoneal delivery.

Methods: We use in vitro measurements of EOC tumour cell and T cell responses to chemotherapy, PDT, and epidermal growth factor receptor targeted PIT as inputs to a mathematical model of non-linear tumour and immune effector cell interaction. The model outputs were used to calculate how photoimmunotherapy could be utilised for tumour control.

Results: In vitro measurements of PIT dose …

Contrasting Association Pattern Of Plasma Low-Density Lipoprotein With White Matter Integrity In Apoe4 Carriers Versus Non-Carriers, Zhenyao Ye, Yezhi Pan, Rozalina G Mccoy, Chuan Bi, Chen Mo, Li Feng, Jiaao Yu, Tong Lu, Song Liu, J Carson Smith, Minxi Duan, Si Gao, Yizhou Ma, Chixiang Chen, Braxton D Mitchell, Paul M Thompson, L Elliot Hong, Peter Kochunov, Tianzhou Ma, Shuo Chen

Faculty, Staff and Student Publications

Apolipoprotein E ε4 (APOE4) is a strong genetic risk factor of Alzheimer’s disease and metabolic dysfunction. However, whether APOE4 and markers of metabolic dysfunction synergistically impact the deterioration of white matter (WM) integrity in older adults remains unknown.

In the UK Biobank data, we conducted a multivariate analysis to investigate the interactions between APOE4 and 249 plasma metabolites (measured using nuclear magnetic resonance spectroscopy) with whole-brain WM integrity (measured by diffusion-weighted magnetic resonance imaging) in a cohort of 1,917 older adults (aged 65.0–81.0 years; 52.4% female).

Although no main association was observed between either APOE4 or metabolites with …

A Site-Wise Reliability Analysis Of The Abcd Diffusion Fractional Anisotropy And Cortical Thickness: Impact Of Scanner Platforms, Yezhi Pan, L Elliot Hong, Ashley Acheson, Paul M Thompson, Neda Jahanshad, Alyssa H Zhu, Jiaao Yu, Chixiang Chen, Tianzhou Ma, Ho-Ling Liu, Jelle Veraart, Els Fieremans, Nicole R Karcher, Peter Kochunov, Shuo Chen

2020-Current year OA Pubs

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce nonbiological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation …

An Artificial Intelligence-Generated Model Predicts 90-Day Survival In Alcohol-Associated Hepatitis: A Global Cohort Study, Winston Dunn, Yanming Li, Ashwani K Singal, Douglas A Simonetto, Luis A Díaz, Francisco Idalsoaga, Gustavo Ayares, Jorge Arnold, María Ayala-Valverde, Diego Perez, Jaime Gomez, Rodrigo Escarate, Eduardo Fuentes-López, Carolina Ramirez-Cadiz, Dalia Morales-Arraez, Wei Zhang, Steve Qian, Joseph C Ahn, Seth Buryska, Heer Mehta, Nicholas Dunn, Muhammad Waleed, Horia Stefanescu, Andreea Bumbu, Adelina Horhat, Bashar Attar, Rohit Agrawal, Joaquín Cabezas, Victor Echavaría, Berta Cuyàs, Maria Poca, German Soriano, Shiv K Sarin, Rakhi Maiwall, Prasun K Jalal, Fátima Higuera-De-La-Tijera, Anand V Kulkarni, P Nagaraja Rao, Patricia Guerra-Salazar, Lubomir Skladaný, Natália Kubánek, Veronica Prado, Ana Clemente-Sanchez, Diego Rincon, Tehseen Haider, Kristina R Chacko, Gustavo A Romero, Florencia D Pollarsky, Juan C Restrepo, Luis G Toro, Pamela Yaquich, Manuel Mendizabal, Maria L Garrido, Sebastián Marciano, Melisa Dirchwolf, Victor Vargas, César Jiménez, David Hudson, Guadalupe García-Tsao, Guillermo Ortiz, Juan G Abraldes, Patrick S Kamath, Marco Arrese, Vijay H Shah, Ramon Bataller, Juan Pablo Arab

Faculty, Staff and Students Publications

Background and aims: Alcohol-associated hepatitis (AH) poses significant short-term mortality. Existing prognostic models lack precision for 90-day mortality. Utilizing artificial intelligence in a global cohort, we sought to derive and validate an enhanced prognostic model.

Approach and results: The Global AlcHep initiative, a retrospective study across 23 centers in 12 countries, enrolled patients with AH per National Institute for Alcohol Abuse and Alcoholism criteria. Centers were partitioned into derivation (11 centers, 860 patients) and validation cohorts (12 centers, 859 patients). Focusing on 30 and 90-day postadmission mortality, 3 artificial intelligence algorithms (Random Forest, Gradient Boosting Machines, and eXtreme Gradient Boosting) …

A Large Scale Multi Institutional Study For Radiomics Driven Machine Learning For Meningioma Grading, Mert Karabacak, Shiv Patil, Rui Feng, Raj K. Shrivastava, Konstantinos Margetis

Department of Medicine Faculty Papers

This study aims to develop and evaluate radiomics-based machine learning (ML) models for predicting meningioma grades using multiparametric magnetic resonance imaging (MRI). The study utilized the BraTS-MEN dataset's training split, including 698 patients (524 with grade 1 and 174 with grade 2-3 meningiomas). We extracted 4872 radiomic features from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. LASSO regression reduced features to 176. The data was split into training (60%), validation (20%), and test (20%) sets. Five ML algorithms (TabPFN, XGBoost, LightGBM, CatBoost, and Random Forest) were employed to build models differentiating low-grade (grade 1) from high-grade …

Takotsubo Syndrome: An International Expert Consensus Report On Practical Challenges And Specific Conditions (Part-2: Specific Entities, Risk Stratification And Challenges After Recovery), Kenan Yalta, John E Madias, Nicholas G Kounis, Shams Y-Hassan, Marija Polovina, Servet Altay, Alexandre Mebazaa, Mehmet Birhan Yilmaz, Yuri Lopatin, Mamas A Mamas, Robert J Gil, Ritu Thamman, Abdallah Almaghraby, Biykem Bozkurt, Gani Bajraktari, Thomas Fink, Vassil Traykov, Stephane Manzo-Silberman, Ulvi Mirzoyev, Sekib Sokolovic, Zviad V Kipiani, Cecilia Linde, Petar M Seferovic

Faculty, Staff and Students Publications

Takotsubo syndrome (TTS) still remains as an enigmatic phenomenon. In particular, long-term challenges (including clinical recurrence and persistent symptoms) and specific entities in the setting of TTS have been the evolving areas of interest. On the other hand, a significant gap still exists regarding the proper risk-stratification of this phenomenon in the short and long terms. The present paper, the second part (part-2) of the consensus report, aims to discuss less well-known aspects of TTS including specific entities, challenges after recovery and risk-stratification.

Question Answering For Electronic Health Records: Scoping Review Of Datasets And Models, Jayetri Bardhan, Kirk Roberts, Daisy Zhe Wang

Faculty, Staff and Student Publications

Background: Question answering (QA) systems for patient-related data can assist both clinicians and patients. They can, for example, assist clinicians in decision-making and enable patients to have a better understanding of their medical history. Substantial amounts of patient data are stored in electronic health records (EHRs), making EHR QA an important research area. Because of the differences in data format and modality, this differs greatly from other medical QA tasks that use medical websites or scientific papers to retrieve answers, making it critical to research EHR QA.

Objective: This study aims to provide a methodological review of existing works on …

Ten Challenges And Opportunities In Computational Immuno-Oncology, Riyue Bao, Alan Hutson, Anant Madabhushi, Vanessa D Jonsson, Spencer R Rosario, Jill S Barnholtz-Sloan, Elana J Fertig, Himangi Marathe, Lyndsay Harris, Jennifer Altreuter, Qingrong Chen, James Dignam, Andrew J Gentles, Edgar Gonzalez-Kozlova, Sacha Gnjatic, Erika Kim, Mark Long, Martin Morgan, Eytan Ruppin, David Van Valen, Hong Zhang, Natalie Vokes, Daoud Meerzaman, Song Liu, Eliezer M Van Allen, Yi Xing

Faculty, Staff and Student Publications

Immuno-oncology has transformed the treatment of cancer, with several immunotherapies becoming the standard treatment across histologies. Despite these advancements, the majority of patients do not experience durable clinical benefits, highlighting the imperative for ongoing advancement in immuno-oncology. Computational immuno-oncology emerges as a forefront discipline that draws on biomedical data science and intersects with oncology, immunology, and clinical research, with the overarching goal to accelerate the development of effective and safe immuno-oncology treatments from the laboratory to the clinic. In this review, we outline 10 critical challenges and opportunities in computational immuno-oncology, emphasizing the importance of robust computational strategies and interdisciplinary …

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 …

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 …

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 …

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 …

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 …

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 …