Digital Commons Network^™

Information Theory Analysis Of Ctx Shows Consistent Clinical Presentation, Jennifer Hanson, Penelope E Bonnen

Faculty, Staff and Students Publications

Cerebrotendinous xanthomatosis (CTX) is a rare, metabolic disorder caused by pathogenic variants in CYP27A1. The classic clinical presentation includes infantile-onset chronic diarrhea, juvenile-onset bilateral cataracts, with development of tendon xanthomas and progressive neurological dysfunction. These multisystem clinical features typically appear in different decades of life often confounding diagnosis of CTX. Further complicating diagnosis is the generally held belief that the clinical presentation of CTX varies highly between individuals and even within families. We applied information theory analyses to CTX patient data to quantitatively assess clinical variability in CTX. We conducted a systematic review of the literature to identify all CTX …

Gregor: Accelerating Genomics For Rare Diseases, Moez Dawood, Ben Heavner, Marsha M Wheeler, Rachel A Ungar, Jonathan Lotempio, Laurens Wiel, Seth Berger, Jonathan A Bernstein, Jessica X Chong, Emmanuèle C Délot, Evan E Eichler, James R Lupski, Ali Shojaie, Michael E Talkowski, Alex H Wagner, Chia-Lin Wei, Christopher Wellington, Matthew T Wheeler, Gregor Partner Members, Claudia M B Carvalho, Richard A Gibbs, Casey A Gifford, Susanne May, Danny E Miller, Heidi L Rehm, Kaitlin E Samocha, Fritz J Sedlazeck, Eric Vilain, Anne O'Donnell-Luria, Jennifer E Posey, Lisa H Chadwick, Michael J Bamshad, Stephen B Montgomery, Genomics Research To Elucidate The Genetics Of Rare Diseases (Gregor) Consortium

Faculty, Staff and Students Publications

Rare diseases are collectively common, affecting approximately 1 in 20 individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in next-generation sequencing, development of new computational and functional genomics approaches to prioritize genes and variants and increased global sharing of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize and evaluate emerging genomics technologies …

Short Term Hemodynamic Effects Of Atrial Fibrillation In A Closed-Loop Human Cardiac-Baroreflex System, Oluwasanmi Adeodu, Michelle Gee, Babak Mahmoudi, Rajanikanth Vadigepalli, Mayuresh V. Kothare

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

Atrial fibrillation (AF) remains the leading cardiac cause of stroke and AF-related death rate in the United States has been increasing for over twenty years. While the effect of standalone AF on heart rate is well established, there is a lack of clarity on its impact on other critical hemodynamic metrics. This is ostensibly due to interaction with other common comorbidities, especially hypertension. In addition, AF has a complex relationship with the state of the baroreflex. Evidence indicates that baroreflex sensitivity (BRS), the ability of the intrinsic cardiac control system to initiate parasympathetic response, is suppressed during AF. Therefore, a …

Correcting For The Inflated Adult Population Denominator In An English Nationwide Health Care Cohort: Database Analysis Study, Sudhir Venkatesan, Mark Joy, Gavin Jamie, Debasish Kar, Robert Williams, Xuejuan Fan, Wilhelmine Meeraus, Ruby S.M. Tsang, Kathryn S. Taylor, Sylvia Taylor, F. D. Richard Hobbs, Sneha N. Anand, Chris Robertson, Simon De Lusignan

Peninsula Medical School

Background:Electronic health care databases are widely used for epidemiological studies. However, they may contain inactive records of individuals no longer participating in the health care system. These inactive records create a methodological challenge as they systematically appear as unexposed with no recorded outcomes. Given the widespread health care system engagement during the COVID-19 pandemic, the English National Health Service (NHS), which hosts a national pandemic planning and research dataset with linkage to COVID-19 vaccination and emergency care data, makes it an ideal setting to identify the extent of overrepresentation due to inactive health care records and assess ways to mitigate …

A Novel Approach To Calculating Expected Total Fetal Lung Volume In Fetuses With Isolated Congenital Diaphragmatic Hernia And Fetal Growth Restriction: A Theoretical Computational Simulation, Morcos Hanna, Jonathan Davies, Amaryllis Fernandes, Pamela M Ketwaroo, Amy R Mehollin-Ray, Roopali Donepudi, Alice King, Joseph Hagan, Sundeep G Keswani, Sharada H Gowda, Caraciolo J Fernandes

Faculty, Staff and Students Publications

Objectives: Congenital diaphragmatic hernia (CDH) often coexists with fetal growth restriction (FGR). The observed-to-expected (O/E) total fetal lung volume (TFLV) is used to assess CDH severity, predict outcomes, and direct fetal interventions. Expected TFLV measurements traditionally rely only on gestation age (GA). This simulation assesses how incorporating weight-adjusted GA norms affects O/E TFLV calculations in patients with isolated CDH and FGR.

Methods: A simulated dataset (n=1,005) utilized published mean fetal weight and TFLV references. Computer-generated variables included observed weights (3rd-10th %ile), O/E TFLV (10-65 %), and percent liver herniation (0-42 %). GA estimates were corrected by weight and used to …

Enhancing Cyberattack Resiliency Through The Radiotherapy Backup And Recovery Dashboard Tool, Justin Pijanowski, Eric Nguyen, Yasin Abdulkadir, Justin Hink, Yevgeniy Vinogradskiy, James Lamb

Department of Radiation Oncology Faculty Papers

PURPOSE: Radiation Oncology departments impacted by recent cyberattacks were unable to access data backups or their Record and Verify (R&V) system and therefore faced challenges to resume patient treatments in a timely manner. We present a novel software tool that backs-up critical radiotherapy treatment information and displays essential information for on-treatment patients in an intuitive and accessible dashboard allowing clinics to continue radiotherapy treatments. The purpose of this report is to describe implementation details, challenges, and share open-source code to facilitate radiation oncology clinics' efforts to develop tools to improve cyberattack resiliency.

METHODS: The Radiotherapy Backup and Recovery Dashboard Tool …

Type I Hybrid Effectiveness-Implementation Randomised Controlled Trial To Address Intergenerational Impact Of War Trauma And Resilience Among Second-Generation Refugee Children In The Usa: Resettled Refugee Families For Healing (Rrf4h) Study Protocol, Nhial T Tutlam, Tewodros W Liyew, Theresa S Betancourt, Byron J Powell, Shenyang Guo, Mary Mckay, Fred M Ssewamala

2020-Current year OA Pubs

INTRODUCTION: Children from refugee families resettled in the USA face higher risks of serious mental health challenges compared with their native-born peers. Research shows that refugee youth in high-income countries frequently suffer from trauma-associated disorders such as post-traumatic stress disorder (PTSD), depression and anxiety. The high prevalence of trauma-associated mental health problems among these youth may be attributed to their own trauma exposure, especially if born in conflict zones, and post-resettlement challenges like poverty, acculturation difficulties, racism and discrimination. However, they may also suffer from the effects of intergenerational trauma, where parental war trauma impacts them. This study aims to …

Large Language Models As Information Providers For Appropriate Antimicrobial Use: Computational Text Analysis And Expert-Rated Comparison Of Chatgpt, Claude And Gemini, Marcello Di Pumpo, Maria Rosaria Gualano, Danilo Buonsenso, Francesca Raffaelli, Daniele Donà, Vittorio Maio, Patrizia Laurenti, Walter Ricciardi, Leonardo Villani

College of Population Health Faculty Papers

OBJECTIVES: Antimicrobial resistance is a critical public health threat. Large language models (LLMs) show great capability for providing health information. This study evaluates the effectiveness of LLMs in providing information on antibiotic use and infection management.

METHODS: Using a mixed-method approach, responses to healthcare expert-designed scenarios from ChatGPT 3.5, ChatGPT 4.0, Claude 2.0 and Gemini 1.0, in both Italian and English, were analysed. Computational text analysis assessed readability, lexical diversity and sentiment, while content quality was assessed by three experts via DISCERN tool.

RESULTS: 16 scenarios were developed. A total of 101 outputs and 5454 Likert-scale (1-5) scores were obtained …

Optimal Control In Combination Therapy For Heterogeneous Cell Populations With Drug Synergies., Simon F. Martina-Perez, Samuel W S Johnson, Rebecca M. Crossley, Jennifer C. Kasemeier, Paul M. Kulesa, Ruth E. Baker

Manuscripts, Articles, Book Chapters and Other Papers

Cell heterogeneity plays an important role in patient responses to drug treatments. In many cancers, it is associated with poor treatment outcomes. Many modern drug combination therapies aim to exploit cell heterogeneity, but determining how to optimise responses from heterogeneous cell populations while accounting for multi-drug synergies remains a challenge. In this work, we introduce and analyse a general optimal control framework that can be used to model the treatment response of multiple cell populations that are treated with multiple drugs that mutually interact. In this framework, we model the effect of multiple drugs on the cell populations using a …

Causal Ai-Based Clinical And Radiomic Analysis For Optimizing Patient Selection In Combined Immunotherapy And Sabr In Early-Stage Nsclc: A Secondary Analysis Of The Phase Ii I-Sabr Trial, Maliazurina B Saad, Eman Showkatian, Vivek Verma, Qasem Al-Tashi, Muhammad Aminu, Xinyan Xu, Muhamed Qayati Mohamed, Morteza Salehjahromi, Sheeba J Sujit, Yuliya Kitsel, Steven H Lin, Zhongxing Liao, Saumil Gandhi, David Qian, David Jaffray, Caroline Chung, Natalie I Vokes, Jianjun Zhang, J Jack Lee, John V Heymach, Jia Wu, Joe Y Chang

Faculty, Staff and Student Publications

Background: The recent phase II randomized stereotactic ablative radiotherapy with and without immunotherapy (I-SABR) trial has shown improved event-free survival (EFS) when adding immunotherapy to stereotactic ablative radiotherapy (SABR) for early-stage inoperable non-small cell lung cancer (NSCLC). However, optimizing patient selection thereof is critical, because not every patient benefits from immunotherapy. Leveraging the powerful use of artificial intelligence, this secondary analysis of the I-SABR trial developed a modeling system (named "I-SABR-SELECT") based on clinical and radiomic factors to address which patients should receive additional immunotherapy.

Methods: The discovery/validation cohorts were from the I-SABR trial, with external validation from the single-arm …

Vaccine Value Profile For Schistosomiasis, Gavin Yamey, Kaci Kennedy Mcdade, Roy M Anderson, Sarah M Bartsch, Maria Elena Bottazzi, David Diemert, Peter J Hotez, Bruce Y Lee, Donald Mcmanus, Adebayo J Molehin, Meta Roestenberg, David Rollinson, Afzal A Siddiqui, Miriam Tendler, Joanne P Webster, Hong You, Raphaël M Zellweger, Caroline Marshall

Faculty, Staff and Students Publications

Schistosomiasis is caused by parasitic flatworms (Schistosoma). The disease in humans can be caused by seven different species of Schistosoma: S. mansoni, S. japonicum, S. haematobium, S. malayensis, S. mekongi, S. guineensis and S. intercalatum, as well as by hybrids between species, including livestock schistosome species. People are infected when exposed to infested water and the parasite larvae penetrate the skin. Poor and rural communities are typically the most affected, and the general population who lives in affected areas and is exposed to contaminated water is at risk. Areas with poor access to safe water and adequate sanitation are also …

First Experience With Third-Party Validations: A Robust Calibration And Qa Procedure For Proton Flash Delivery, Chih-Chiang Chang, Balaji Selvaraj, Xingyi Zhao, Jacob Rembish, Paige A Taylor, Alexander Bookbinder, Chingyun Cheng, J Isabelle Choi, Charles B Simone, Haibo Lin, Minglei Kang

Faculty, Staff and Student Publications

Background: Proton FLASH radiotherapy, delivering ultra-high dose rates, shows promise in reducing normal tissue toxicity while maintaining tumor control. However, accurate dosimetry and quality assurance (QA) for FLASH remain challenging due to the extreme dose rates involved. Developing reliable calibration and QA procedures is crucial for advancing FLASH towards clinical implementation.

Purpose: To present an effective routine calibration and QA procedure for proton FLASH delivery to ensure high-quality dosimetry performance for preclinical and clinical delivery.

Methods: A high temporospatial resolution strip ionization chamber array (SICA) detector was mounted to the treatment nozzle, which was calibrated using an Advanced Markus ion …

Lesion Absorbed Dose-Response Relationship In Patients With Metastatic Castration-Resistant Prostate Cancer Undergoing [177lu]Lu-Psma-617 Radiopharmaceutical Therapy, Milan Grkovski, Simone S Krebs, Joseph A O'Donoghue, Jonathan Kuten, Audrey Mauguen, Parnian Shobeiri, Daniel Lafontaine, Maria Thor, Finn Augensen, Josef J Fox, Neeta Pandit-Taskar, Mark P Dunphy, Lisa Bodei, John L Humm, Heiko Schöder

Faculty, Staff and Student Publications

The relationship between lesion absorbed dose (AD) and response in patients with metastatic castration-resistant prostate cancer undergoing [177Lu]Lu-PSMA-617 radiopharmaceutical therapy (RPT) remains poorly understood. The objective of this work was to investigate the AD-response relationship at both the patient and lesion levels.

Methods: Sixty-five patients underwent serial SPECT/CT imaging after receiving 7.31 ± 0.27 GBq of [177Lu]Lu-PSMA-617. Single-time-point (STP) (Hänscheid approximation at 72 h) and multiple-time-point voxelwise dosimetry were performed. Patient response was evaluated by changes in serum prostate-specific antigen level before and after cycle 1 of RPT. The response of individual lesions was evaluated by the change in the …

Family Income And Polygenic Scores Are Independently But Not Interactively Associated With Cognitive Performance Among Youth Genetically Similar To European Reference Populations, S E Paul, N M Elsayed, S M C Colbert, R Bogdan, A S Hatoum, D M Barch

2020-Current year OA Pubs

Cognitive abilities are heritable and influenced by socioeconomic status (SES). It is critical to understand the association between SES and cognition beyond genetic propensity to inform potential benefits of SES-based interventions and to determine if such associations vary across (i) cognitive domains, (ii) facets of SES, and/or (iii) genetic propensity for different aspects of cognition. We examined the contributions of neighborhood socioeconomic advantage, family income, and polygenic scores (PGS) for domains of cognition (i.e., general cognitive ability, executive function, learning and memory, fluid reasoning) in a sample of children (ages 9-10;

Do Complex Psychometric Analyses Really Matter? Comparing Multiple Approaches Using Individual Participant Data From Antidepressant Trials, David Byrne, Frank Doyle, Susan Brannick, Robert M Carney, Pim Cuijpers, Alexandra L Dima, Kenneth E Freedland, Suzanne Guerin, David Hevey, Bishember Kathuria, Emma Wallace, Fiona Boland

2020-Current year OA Pubs

BACKGROUND: Psychometric methods are used to remove underperforming items and reduce error in existing measures, albeit different approaches can produce different results. This study aimed to determine the implications of applying different psychometric methods for clinical trial outcomes.

METHODS: Individual participant data from 15 antidepressant treatment trials from Vivli.org were analyzed. Baseline (pretreatment) and 8-week (range 4-12 weeks) outcome data from the Montgomery-Asberg Depression Rating Scale were subjected to best-practice factor analysis (FA), item response theory (IRT), and network analysis (NA) approaches. Trial outcomes for the original summative scores and psychometric-model scores were assessed using multilevel models. Percentage differences in …

Enhancing Stemm Education Through Advanced Technologies And Collaborative Programs, Zhongcheng Shi, Michael Nguyen, Yuan Yao, Debra D Murray, Rayne H Rouce, Veronica Ajewole, Huan Xie, Shixia Huang

Faculty and Staff Publications

Integrating advanced technologies into STEMM (Science, Technology, Engineering, Mathematics, and Medicine) education is essential for preparing a future-ready, diverse scientific workforce capable of addressing complex global challenges. This paper presents three interconnected programs-BRITE (Biotechnology Research Incubator for Teachers), ASPIRATION (AI-guided Scientist-Mentored Primary Literature Adaptation for STEMM Education), and C-REP (Cancer Research Education Program)-developed through collaborations with Advanced Technology Cores and multi-institutional participation, involving mentors ranging from core directors to graduate and medical students. While initially supported by internal funding, two of these initiatives have since secured NIH grant support. These initiatives provide immersive, hands-on training in genomics, proteomics, metabolomics, flow …

Cookie-Pro: Covalent Inhibitor Binding Kinetics Profiling On The Proteome Scale, Hanfeng Lin, Bin Yang, Lang Ding, Yen-Yu Yang, Matthew V Holt, Sung Yun Jung, Bing Zhang, Meng C Wang, Jin Wang

Faculty, Staff and Students Publications

Covalent inhibitors are an emerging class of therapeutics, but methods to comprehensively profile their binding kinetics and selectivity across the proteome have been limited. Here we introduce COOKIE-Pro (COvalent Occupancy KInetic Enrichment via Proteomics), an unbiased method for quantifying irreversible covalent inhibitor binding kinetics on a proteome-wide scale. COOKIE-Pro uses a two-step incubation process with mass spectrometry-based proteomics to determine kinact and KI values for covalent inhibitors against both on-target and off-target proteins. We validated COOKIE-Pro using BTK inhibitors spebrutinib and ibrutinib, accurately reproducing known kinetic parameters and identifying both expected and unreported off-targets. The method revealed that …

Non-Invasive Detection Of Choroidal Melanoma Via Tear-Derived Protein Corona On Gold Nanoparticles: A Machine Learning Approach, Hakimeh Rakhshandeh, Ahmad Nasiraei, Hamid Riazi-Esfahani, Babak Masoomian, Fariba Ghassemi, Mojtaba Arjmand, Saeed Heidari Keshel, Fatemeh Atyabi, Rassoul Dinarvand

Wills Eye Hospital Papers

This study investigates the feasibility of using tear sample analysis, based on protein corona formation on gold nanoparticles combined with electrospray ionization mass spectrometry (ESI-MS) and machine learning techniques, as a non-invasive approach for the detection of choroidal melanoma. The aim is to assess whether protein-nanoparticle interactions can support early and reliable identification of this ocular condition. Tear samples were collected using Schirmer strips from six healthy individuals and six patients diagnosed with choroidal melanoma, with subsequent augmentation to 18 samples per group. Gold nanoparticles (AuNPs, ~ 20 nm) were synthesized via citrate reduction and incubated with tear samples to …

Lewy Body Dementia Promotion By Air Pollutants, Xiaodi Zhang, Haiqing Liu, Xiao Wu, Longgang Jia, Kundlik Gadhave, Lena Wang, Kevin Zhang, Hanyu Li, Rong Chen, Ramhari Kumbhar, Ning Wang, Chantelle E Terrillion, Bong Gu Kang, Bin Bai, Minhan Park, Ma Cristine Faye Denna, Shu Zhang, Wenqiang Zheng, Denghui Ye, Xiaoli Rong, Liu Yang, Lili Niu, Han Seok Ko, Weiyi Peng, Lingtao Jin, Mingyao Ying, Liana S Rosenthal, David W Nauen, Alex Pantelyat, Mahima Kaur, Kezia Irene, Liuhua Shi, Rahel Feleke, Sonia García-Ruiz, Mina Ryten, Valina L Dawson, Francesca Dominici, Rodney J Weber, Xuan Zhang, Pengfei Liu, Ted M Dawson, Shizhong Han, Xiaobo Mao

Faculty, Staff and Student Publications

Evidence links air pollution to dementia, yet its role in Lewy body dementia (LBD) remains unclear. Here we showed in a cohort of 56.5 million individuals across the U.S. that PM2.5 exposure raises LBD risk. Mechanistically, we found PM2.5 exposure led to brain atrophy in wild-type mice, an effect not seen in α-synuclein (αSyn)-deficient mice. PM2.5 exposure generated a highly pathogenic αSyn strain, PM-PFF, with enhanced proteinase K-resistance and neurotoxicity, resembling αSyn LBD strains. PM2.5 samples from China, the U.S., and Europe consistently induced proteinase-resistant αSyn strains and in vivo pathology. Transcriptomic analyses revealed shared responses between PM2.5-exposed mice and …

The Causal Pivot: A Structural Approach To Genetic Heterogeneity And Variant Discovery In Complex Diseases, Chad A Shaw, C J Williams, Taotao Tan, Daniel Illera, Nicholas Di, Joshua M Shulman, John W Belmont

Center on Aging Staff Publications

We present the Causal Pivot (CP) as a structural causal model (SCM) for analyzing genetic heterogeneity in complex diseases. The CP leverages an established causal factor or factors to detect the contribution of additional suspected causes. Specifically, polygenic risk scores (PRSs) serve as known causes, while rare variants (RVs) or RV ensembles are evaluated as candidate causes. The CP incorporates outcome-induced association by conditioning on disease status. We derive a conditional maximum-likelihood procedure for binary and quantitative traits and develop the Causal Pivot likelihood ratio test (CP-LRT) to detect causal signals. Through simulations, we demonstrate the CP-LRT’s robust power and …

Variational Temporal Deconfounder Network For Individualized Treatment Effect Estimation With Longitudinal Observational Data, Hao Dai, Yu Huang, Yuxi Liu, Xing He, Jingchuan Guo, Mattia Prosperi, Jiang Bian

Faculty, Staff and Student Publications

Objective: By leveraging real-world electronic health record (EHR) data, this study set out to estimate individualized treatment effects (ITE) in longitudinal observational settings to advance personalized medicine, addressing key challenges that are often observed in real-world clinical scenarios and pose statistical challenges, including hidden confounding and dynamic treatment regimens.

Methods: We propose the Variational Temporal Deconfounder Network (VTDNet), a novel framework designed to account for time-varying hidden confounding using a variational recurrent transformer-based autoencoder. Specifically, VTDNet comprises three critical components: a temporal Encoder-Decoder structure to capture hidden representation, a Treatment Block that captures interdependencies among multiple treatments, and a Potential …

A Consensus Guide To Preclinical Indirect Calorimetry Experiments, Alexander S Banks, David B Allison, Thierry Alquier, Ansarullah, Steven N Austad, Johan Auwerx, Julio E Ayala, Joseph A Baur, Stefania Carobbio, Gary A Churchill, Morten Dall, Rafael De Cabo, Jose Donato, Nathalia R V Dragano, Carol F Elias, Anthony W Ferrante, Brian N Finck, Jose E Galgani, Zachary Gerhart-Hines, Laurie J Goodyear, Justin L Grobe, Rana K Gupta, Kirk M Habegger, Sean M Hartig, Andrea L Hevener, Steven B Heymsfield, Corey D Holman, Martin Hrabě De Angelis, David E James, Lawrence Kazak, Jae Bum Kim, Martin Klingenspor, Xingxing Kong, Sander Kooijman, Louise Lantier, K C Kent Lloyd, James C Lo, Irfan J Lodhi, Paul S Maclean, Owen P Mcguinness, Gema Medina-Gómez, Raghavendra G Mirmira, Christopher D Morrison, Gregory J Morton, Timo D Müller, Yoshihiro Ogawa, David Pajuelo-Reguera, Matthew J Potthoff, Nathan Qi, Marc L Reitman, Patrick C N Rensen, Jan Rozman, Jennifer M Rutkowsky, Kei Sakamoto, Philipp E Scherer, Gary J Schwartz, Radislav Sedlacek, Mohammed Selloum, Saame Raza Shaikh, Shuai Chen, Gerald I Shulman, Vojtěch Škop, Alexander A Soukas, John R Speakman, Bruce M Spiegelman, Gregory R Steinberg, Katrin J Svensson, John P Thyfault, Tony Tiganis, Paul M Titchenell, Nigel Turner, Licio A Velloso, Antonio Vidal-Puig, Christopher S Ward, Ashley S Williams, Christian Wolfrum, Allison W Xu, Ying Xu, Juleen R Zierath, International Indirect Calorimetry Consensus Committee (Iiccc)

Children’s Nutrition Research Center Staff Publications

Understanding the complex factors influencing mammalian metabolism and body weight homeostasis is a long-standing challenge requiring knowledge of energy intake, absorption and expenditure. Using measurements of respiratory gas exchange, indirect calorimetry can provide non-invasive estimates of whole-body energy expenditure. However, inconsistent measurement units and flawed data normalization methods have slowed progress in this field. This guide aims to establish consensus standards to unify indirect calorimetry experiments and their analysis for more consistent, meaningful and reproducible results. By establishing community-driven standards, we hope to facilitate data comparison across research datasets. This advance will allow the creation of an in-depth, machine-readable data …

Bone Quality Response To Lifestyle Intervention In Older Adults With Obesity (Limb-Q Trial): A Randomised Controlled Trial, Giulia Gregori, Sanjay Mediwala, Michael Liebschner, Daeseung Kim, Mon S Bryant, Nina Klonis, Reina Armamento-Villareal, Clifford Qualls, Dennis T Villareal

Faculty, Staff and Students Publications

Background: Lifestyle interventions for weight loss might exacerbate age-related bone loss and osteoporosis. However, there is limited knowledge about their effects on bone quality. We examined whether lifestyle intervention can preserve or enhance bone quality, despite reductions in bone mineral density.

Methods: The Lifestyle Intervention to Improve Bone Quality (LIMB-Q) study was a randomised controlled trial conducted at Baylor College of Medicine and the Michael E DeBakey VA Medical Center (Houston, TX, USA) including older adults (aged 65-85 years) with obesity (BMI ≥30 kg/m2). Participants were randomly assigned to receive either an intensive lifestyle intervention (intensive lifestyle group, consisting of …

High-Fidelity Finite Element Modeling Technique To Improve Sensitivity To Bone Tissue Changes Of Older Adults With Obesity Undergoing Intensive Lifestyle Intervention, Michael A K Liebschner, Daeseung Kim, Nina Klonis, Giulia Gregori, Reina Armamento-Villareal, Clifford Qualls, Dennis T Villareal

Faculty, Staff and Students Publications

Introduction: Obesity presents a significant health risk for the aging population. Research shows that weight loss and regular exercise can greatly improve the functional status of older adults who are obese. However, weight loss may also result in a decrease in bone mass. To properly assess changes in fracture risk due to lifestyle interventions, a direct biomechanical evaluation of bone strength and fracture risk at metabolically active sites is essential.

Methods: Computed tomography scans taken at two different time points of ten human volunteers provided the foundation for this study. A high-fidelity segmentation and modeling approach was taken to generate …

Expression Graph Network Framework For Biomarker Discovery, Yang Liu, Jason Huse, Kasthuri Kannan

Faculty, Staff and Student Publications

Biomarker discovery for complex diseases, such as cancer, hinges on uncovering molecular signatures that capture intricate, interconnected relationships within biological data-a challenge that traditional statistical and machine learning methods often fail to meet due to the complexity of high-dimensional gene expression profiles. To overcome this, we introduce the expression graph network framework (EGNF). This cutting-edge graph-based approach integrates graph neural networks with network-based feature engineering to enhance the predictive identification of biomarkers. EGNF constructs biologically informed networks by combining gene expression data and clinical attributes within a graph database, utilizing hierarchical clustering to generate dynamic, patient-specific representations of molecular interactions. …

Auprc: A Metric For Evaluating The Performance Of In-Silico Perturbation Methods In Identifying Differentially Expressed Genes, Hongxu Zhu, Amir Asiaee, Leila Azinfar, Jun Li, Han Liang, Ehsan Irajizad, Kim-Anh Do, James P Long

Faculty, Staff and Student Publications

In silico perturbation models, computational methods that can predict cellular responses to perturbations, present an opportunity to reduce the need for costly and time-intensive in vitro experiments. Many recently proposed models predict high-dimensional cellular responses, such as gene or protein expression to perturbations such as gene knockout or drugs. However, evaluating in silico performance has largely relied on metrics such as $R^{2}$, which assess overall prediction accuracy but fail to capture biologically significant outcomes like the identification of differentially expressed (DE) genes. In this study, we present a novel evaluation framework that introduces the AUPRC metric to assess the precision …

Braingenebot: A Framework For Variant Prioritization And Generative Pretrained Transformer-Informed Interpretation Across Polygenic Risk Score Studies, Gang Qu, Nitesh Enduru, Xinyi Liu, Xiaoqian Jiang, Zhongming Zhao

Faculty, Staff and Student Publications

Polygenic risk scores (PRS) are widely used to assess genetic susceptibility in Alzheimer's disease (AD) research. However, the rapid expansion of PRS studies has led to dataset-specific biases-stemming from factors like population makeup, genotyping methods, and analysis pipelines-that result in inconsistent variant prioritization and limit generalizability and reproducibility. To address these challenges, we propose a transductive learning framework that integrates multiple PRS datasets for more robust risk variant prioritization, incorporating genome-wide association study (GWAS) priority scores as biologically informed priors. Additionally, we introduce BrainGeneBot, an AI-driven tool leveraging generative pretrained transformers with retrieval-augmented generation technology to streamline genomic analyses in …

Human Interpretable Grammar Encodes Multicellular Systems Biology Models To Democratize Virtual Cell Laboratories, Jeanette A I Johnson, Daniel R Bergman, Heber L Rocha, David L Zhou, Eric Cramer, Ian C Mclean, Yoseph W Dance, Max Booth, Zachary Nicholas, Tamara Lopez-Vidal, Atul Deshpande, Randy Heiland, Elmar Bucher, Fatemeh Shojaeian, Matthew Dunworth, André Forjaz, Michael Getz, Inês Godet, Furkan Kurtoglu, Melissa Lyman, John Metzcar, Jacob T Mitchell, Andrew Raddatz, Jacobo Solorzano, Aneequa Sundus, Yafei Wang, David G Denardo, Andrew J Ewald, Daniele M Gilkes, Luciane T Kagohara, Ashley L Kiemen, Elizabeth D Thompson, Denis Wirtz, Laura D Wood, Pei-Hsun Wu, Neeha Zaidi, Lei Zheng, Jacquelyn W Zimmerman, Jude M Phillip, Elizabeth M Jaffee, Joe W Gray, Lisa M Coussens, Young Hwan Chang, Laura M Heiser, Genevieve L Stein-O'Brien, Elana J Fertig, Paul Macklin

Faculty, Staff and Student Publications

Cells interact as dynamically evolving ecosystems. While recent single-cell and spatial multi-omics technologies quantify individual cell characteristics, predicting their evolution requires mathematical modeling. We propose a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This enables systematic integration of biological knowledge and multi-omics data to generate in silico models, enabling virtual "thought experiments" that test and expand our understanding of multicellular systems and generate new testable hypotheses. This paper motivates and describes the grammar, offers a reference implementation, and demonstrates its use in developing both de novo mechanistic models and those informed …

Modeling The Importance Of Life Exposure Factors On Memory Performance In Diverse Older Adults: A Machine Learning Approach, Evan Fletcher, Marianne Chanti-Ketterl, Emily Hokett, Yi Lor, Umesh Venkatesan, Ruijia Chen, Omonigho M. Bubu, Rachel Whitmer, Paola Gilsanz, Zvinka Z. Zlatar

Moss-Magee Rehabilitation Papers

INTRODUCTION: Many health life exposure factors (LEFs) influence cognitive decline and dementia incidence, but their relative importance to episodic memory (an early indicator of cognitive decline) among diverse older adults is unclear. We used machine learning to rank LEFs for memory performance in a large and diverse US cohort.

METHODS: Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) and Study of Healthy Aging in African Americans (STAR), participants underwent neuropsychological testing and answered questionnaires about multiple LEFs. XGBoost and Shapley Additive exPlanation values ranked the importance of factors influencing cross-sectional episodic memory in the full sample and by sex and …

Mathematical Modeling And Association Analysis Decipher The Impact Of The Gut Microbiome On Cancer Immunotherapy, Andreas G Hadjigeorgiou, Constantinos Harkos, Aditya K Mishra, Golnaz Morad, Sarah B Johnson, Nadim J Ajami, Jennifer A Wargo, Lance L Munn, Triantafyllos Stylianopoulos, Rakesh K Jain

Faculty, Staff and Student Publications

The gut microbiome has emerged as a key regulator of response to cancer immunotherapy. However, a better understanding of the underlying mechanisms by which the microbiome influences immunotherapy is needed to identify strategies to optimize outcomes. To this end, we developed a mathematical model to obtain insights into the effect of the microbiome on the immune system and immunotherapy response. This model was based on (i) gut microbiome data derived from preclinical studies, (ii) mathematical modeling of the antitumor immune response, (iii) association analysis of microbiome profiles with model-predicted immune profiles, and (iv) statistical models that correlate model parameters with …