Digital Commons Network^™

Combining Biology-Based And Mri Data-Driven Modeling To Predict Response To Neoadjuvant Chemotherapy In Patients With Triple-Negative Breast Cancer, Casey E Stowers, Chengyue Wu, Zhan Xu, Sidharth Kumar, Clinton Yam, Jong Bum Son, Jingfei Ma, Jonathan I Tamir, Gaiane M Rauch, Thomas E Yankeelov

Faculty, Staff and Student Publications

Purpose

To combine deep learning and biology-based modeling to predict the response of locally advanced, triple-negative breast cancer before initiating neoadjuvant chemotherapy (NAC).

Materials and Methods

In this retrospective study, a biology-based mathematical model of tumor response to NAC was constructed and calibrated on a patient-specific basis using imaging data from patients enrolled in the MD Anderson A Robust TNBC Evaluation FraMework to Improve Survival trial (ARTEMIS; ClinicalTrials.gov registration no. NCT02276443) between April 2018 and May 2021. To relate the calibrated parameters in the biology-based model and pretreatment MRI data, a convolutional neural network (CNN) was employed. The CNN …

Global Research Trends And Focus On Immunotherapy For Endometrial Cancer: A Comprehensive Bibliometric Insight And Visualization Analysis (2012–2024), Yachen Xu, Tao Wang, Xiaojing Liang, Jie Yang, Yuxiang Zhang, Shan Bao

Faculty, Staff and Student Publications

Background: This study conducted a novel systematic bibliometric and visualization analysis of global literature on immunotherapy for endometrial cancer (EC) to explore dynamic trends, research hotspots, and emerging topics, providing valuable references for future research.

Methods: Articles and reviews on EC immunotherapy published between 2012 and August 2024 were retrieved from the Web of Science Core Collection (WoSCC). Bibliometric tools, CiteSpace and VOSviewer, were used to analyze clustering patterns and research dynamics.

Results: A total of 861 articles were contributed by 5,331 authors from 1,392 institutions across 58 countries or regions, involving 1,823 keywords. China demonstrated outstanding performance in this …

Characterization Of Additive Gene-Environment Interactions For Colorectal Cancer Risk, Claire E Thomas, Yi Lin, Michelle Kim, Eric S Kawaguchi, Conghui Qu, Caroline Y Um, Brigid M Lynch, Bethany Van Guelpen, Kostas Tsilidis, Robert Carreras-Torres, Franzel J B Van Duijnhoven, Lori C Sakoda, Peter T Campbell, Yu Tian, Jenny Chang-Claude, Stéphane Bézieau, Arif Budiarto, Julie R Palmer, Polly A Newcomb, Graham Casey, Loic Le Marchandz, Marios Giannakis, Christopher I Li, Andrea Gsur, Christina Newton, Mireia Obón-Santacana, Victor Moreno, Pavel Vodicka, Hermann Brenner, Michael Hoffmeister, Andrew J Pellatt, Robert E Schoen, Niki Dimou, Neil Murphy, Marc J Gunter, Sergi Castellví-Bel, Jane C Figueiredo, Andrew T Chan, Mingyang Song, Li Li, D Timothy Bishop, Stephen B Gruber, James W Baurley, Stephanie A Bien, David V Conti, Jeroen R Huyghe, Anshul Kundaje, Yu-Ru Su, Jun Wang, Temitope O Keku, Michael O Woods, Sonja I Berndt, Stephen J Chanock, Catherine M Tangen, Alicja Wolk, Andrea Burnett-Hartman, Anna H Wu, Emily White, Matthew A Devall, Virginia Díez-Obrero, David A Drew, Edward Giovannucci, Akihisa Hidaka, Andre E Kim, Juan Pablo Lewinger, John Morrison, Jennifer Ose, Nikos Papadimitriou, Bens Pardamean, Anita R Peoples, Edward A Ruiz-Narvaez, Anna Shcherbina, Mariana C Stern, Xuechen Chen, Duncan C Thomas, Elizabeth A Platz, W James Gauderman, Ulrike Peters, Li Hsu

Faculty, Staff and Student Publications

Background: Colorectal cancer (CRC) is a common, fatal cancer. Identifying subgroups who may benefit more from intervention is of critical public health importance. Previous studies have assessed multiplicative interaction between genetic risk scores and environmental factors, but few have assessed additive interaction, the relevant public health measure.

Methods: Using resources from CRC consortia, including 45,247 CRC cases and 52,671 controls, we assessed multiplicative and additive interaction (relative excess risk due to interaction, RERI) using logistic regression between 13 harmonized environmental factors and genetic risk score, including 141 variants associated with CRC risk.

Results: There was no evidence of multiplicative interaction …

The Tripod-Llm Reporting Guideline For Studies Using Large Language Models, Jack Gallifant, Majid Afshar, Saleem Ameen, Yindalon Aphinyanaphongs, Shan Chen, Giovanni Cacciamani, Dina Demner-Fushman, Dmitriy Dligach, Roxana Daneshjou, Chrystinne Fernandes, Lasse Hyldig Hansen, Adam Landman, Lisa Lehmann, Liam G Mccoy, Timothy Miller, Amy Moreno, Nikolaj Munch, David Restrepo, Guergana Savova, Renato Umeton, Judy Wawira Gichoya, Gary S Collins, Karel G M Moons, Leo A Celi, Danielle S Bitterman

Faculty, Staff and Student Publications

Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion. The guidelines introduce a modular format accommodating various LLM research designs and tasks, with 14 main items and 32 subitems applicable across all categories. Developed through an expedited Delphi process and expert consensus, TRIPOD-LLM emphasizes …

Characterizing Spatial Immune Architecture In Metastatic Melanoma Using High-Dimensional Multiplex Imaging, Joel Eliason, Santhoshi Krishnan, Yasunari Fukuda, Matias A Bustos, Dan Winkowski, Sungnam Cho, Akshay Basi, Regan Baird, Elizabeth A Grimm, Michael A Davies, Dave S B Hoon, Arvind Rao, Jared K Burks, Suhendan Ekmekcioglu

Faculty, Staff and Student Publications

Introduction: Immune checkpoint inhibitors (ICIs) have significantly improved survival for patients with metastatic melanoma, yet many experienceresistance due to immunosuppressive mechanisms within the tumor immune microenvironment (TIME). Understanding how the spatial architecture of immune and inflammatory components changes across disease stages may reveal novel prognostic biomarkers and therapeutic targets.

Methods: We performed high-dimensional spatial profiling of two melanoma tissue microarrays (TMAs), representing Stage III (n = 157) and Stage IV (n = 248) metastatic tumors. Using imaging mass cytometry (IMC) and multiplex immunofluorescence (mIF), we characterized the phenotypic, functional, and spatial properties of the TIME. Cellular neighborhoods were …

Safety Of Indocyanine Green Microdosing For Clinical Imaging Of Csf Ventricular Dynamics And Extracranial Outflow, Miriam Zamorano, Banghe Zhu, Ahmed T Massoud, Jonathan Hendricks, H Alex Choi, Pedram Peesh, Brandon A Miller, Xinhai Robert Zhang, Manish N Shah, Eva M Sevick-Muraca

Faculty, Staff and Student Publications

Background and purpose: Intravenous administration of indocyanine green (ICG) has been approved in brain surgeries for decades, yet concerns about neurotoxicity prevent its direct administration into the cerebrospinal fluid (CSF). Armed with prior animal studies demonstrating the feasibility of using ICG microdosing into the CSF, we sought to evaluate its nonclinical safety profile and obtain surrogate measures in adults prior to its use in human neonates.

Methods: Evaluation of ICG toxicity was conducted in mixed primary CNS cell cultures and in an extended safety study of juvenile rat pups deploying intraventricular injections of saline (as control) or ICG. Analysis of …

Flexible Hybrid Self-Powered Piezo-Triboelectric Nanogenerator Based On Bto-Pvdf/Pdms Nanocomposites For Human Machine Interaction, Wentao Dong, Mengyun Li, Chang Chen, Kun Xie, Jinhua Hong, Lin Yang

Civil & Environmental Engineering Faculty Publications

As flexible and wearable electronics play more and more important role in smart watches, smart glass and virtual reality, and the power supply to the wearable electronics have been revealed more attentions for long-term usage and continuous healthy monitoring. To overcome the challenge, flexible self-powered BTO-PVDF/PDMS piezoelectric-triboelectric electric hybrid generators (BPP-HNG) are developed to human gesture monitoring and human machine interaction (HMI) application without external power supply. BPP-HNG based on BTO-PVDF and PDMS films are prepared by sol-gel and spin-coating method. When the BTO content is 20 wt.%, BPP-HNG exhibits better electrical performance with an output voltage of 20.51 V. …

Group Antenatal Care Positively Transforms The Care Experience: Results Of An Effectiveness Trial In Malawi, Crystal L. Patil, Kathleen F. Noor, Esnath Kapito, Li C. Liu, Xiaohan Mei, Elizabeth Chodzaza, Genesis Chorwe-Sungani, Ursula Kafuulafula, Elizabeth T. Abrams, Allissa Desloge, Ashley Gresh, Rohan D. Jeremiah, Dhruvi R. Patel, Anne Batchelder, Heidy Wang, Jocelyn Faydenko, Sharon S. Rising, Ellen Chirwa

Department of Medicine Faculty Publications

Background

We developed and tested a Centering-based group antenatal (ANC) model in Malawi, integrating health promotion for HIV prevention and mental health. We present effectiveness data and examine congruence with only the Group ANC theory of change model, which identifies key processes as supportive relationships, empowered partners in learning and care, and meaningful services, leading to better ANC experiences and outcomes.

Methods

We conducted a hybrid effectiveness-implementation trial at seven clinics in Blantyre District, Malawi, comparing outcomes for 1887 pregnant women randomly assigned to Group ANC or Individual ANC. Group effects on outcomes were summarized and evaluated using t-tests, Mann-Whitney, …

Mortality Prediction After Major Surgery In A Mixed Population Through Machine Learning: A Multi-Objective Symbolic Regression Approach, Pietro Arina, Davide Ferrari, Nicholas Tetlow, Amy Dewar, Robert Stephens, Daniel Martin, Ramani Moonesinghe, Vasa Curcin, Mervyn Singer, John Whittle, Evangelos B Mazomenos

Peninsula Medical School

INTRODUCTION: Understanding 1-year mortality following major surgery offers valuable insights into patient outcomes and the quality of peri-operative care. Few models exist that predict 1-year mortality accurately. This study aimed to develop a predictive model for 1-year mortality in patients undergoing complex non-cardiac surgery using a novel machine-learning technique called multi-objective symbolic regression.

METHODS: A single-institution database of patients undergoing major elective surgery with previous cardiopulmonary exercise testing was divided into three datasets: pre-operative clinical data; cardiorespiratory and physiological data; and combined. A multi-objective symbolic regression model was developed and compared against existing models. Model performance was evaluated using the …

A Qualitative Analysis Of College Students' Interest In Mhealth Solutions, Leslie Hoglund, Craig M. Becker, Cara Tonn

Health Behavior, Policy & Management Faculty Publications

This study explores college students' perceptions of an AI-driven mHealth application designed to promote well-being. With rising mental health challenges in academic settings, students increasingly seek digital tools that provide holistic support for physical, mental, and financial health. Through focus groups, this qualitative study examines students' preferences for personalized health tracking, educational content, and flexible reminders within a private, supportive community. Key findings emphasize students' desire for a balanced, all-in-one app that integrates health and wellness tools without overwhelming them with notifications. Students also highlighted the importance of social media integration for outreach, though concerns were raised about potential stress …

Digital Otoscopy With Computer-Aided Composite Image Generation: Impact On The Correct Diagnosis, Confidence, And Time, Seda Camalan, Carl D Langefeld, Amy Zinnia, Brigham Mckee, Matthew L Carlson, Nicholas L Deep, Michael S Harris, Taha A Jan, Vivian F Kaul, Nathan R Lindquist, Jameson K Mattingly, Jay Shah, Kevin Y Zhan, Metin N Gurcan, Aaron C Moberly

Faculty, Staff and Student Publications

Objective: This study investigated the comparative performance of ear, nose, and throat (ENT) physicians in correctly detecting ear abnormalities when reviewing digital otoscopy imaging using 3 different visualization methods, including computer-assisted composite images called "SelectStitch," single video frame "Still" images, and video clips. The study also explored clinicians' diagnostic confidence levels and the time to make a diagnosis.

Study design: Clinician diagnostic reader study.

Setting: Online diagnostic survey of ENT physicians.

Methods: Nine ENT physicians reviewed digital otoscopy examinations from 86 ears with various diagnoses (normal, perforation, retraction, middle ear effusion, tympanosclerosis). Otoscopy examinations used artificial-intelligence (AI)-based computer-aided composite image …

The Utility Of Brain Biomarkers In Predicting Survival And Neurological Outcomes In Pediatric Patients After Cardiac Arrest: A Systematic Review And Meta-Analysis, Halla Kamińska, Krzysztof Kurek, Michał Zembala, Sagar Galwankar, Monika Tomaszewska, Shraddha Singh, Nicola Luigi Bragazzi, Michał Pruc, Basar Cander, Francesco Chirico, Amelia Rizzo, Jacek Kubica, Ayman El-Menyar, Anne Lepetit, Pawel Patrzylas, Zubaid Rafique, W Frank Peacock, Łukasz Szarpak

Faculty, Staff and Students Publications

Background: Cardiac arrest in children is associated with high morbidity and mortality, primarily due to neurological injury. Biomarkers linked to brain injury, released into circulation from compromised elements of the neurovascular unit, act as significant prognostic indicators in patients suffering from hypoxic-ischemic brain injury (HIBI) subsequent to the restoration of spontaneous circulation (ROSC) after pediatric cardiac arrest. The aim of this systematic review and meta-analysis is to evaluate the prognostic utility of brain injury biomarkers in predicting neurological outcomes and survival in patients following cardiac arrest in the pediatric population.

Methods: Bibliographic databases (PubMed, the Cochrane Library, and Embase) were …

The Association Of Human Milk Intake And Outcomes In Biliary Atresia, Mary Elizabeth M Tessier, Jeremy M Schraw, Stacey Beer, Sanjiv Harpavat, M Kyle Jensen, John C Magee, Vicky Ng, Michael E Scheurer, Sarah A Taylor, Benjamin L Shneider

Faculty, Staff and Students Publications

Objectives: Human milk intake has many benefits which could influence outcomes in biliary atresia (BA). However, the role of human milk in BA has not been examined. We hypothesized that human milk intake would be associated with improved outcomes in BA.

Methods: We assessed the impact of any human milk (AHM) as compared to formula only (FO) intake before Kasai portoenterostomy (KP) on outcomes in 447 infants with BA using the PROBE database (NCT00061828) post hoc. The primary outcome was clearance of jaundice (COJ = total bilirubin (TB) < 2 mg/dL by 3 months post-KP). Secondary outcomes included 2-year survival with native liver (SNL), bilirubin levels, cholangitis, ascites, and growth. We assessed the fecal microbiome (n = 8) comparing AHM versus FO.

Results: At baseline, 211 infants received AHM and 215 received …

Testosterone Plus Lifestyle Therapy Improves Skeletal Muscle Glycolysis In Older Men With Obesity And Hypogonadism, Viola Viola, Marlene Aguilar, Maria Liza Duremdes Nava, Alessandra Celli, Reina Armamento-Villareal, Yoann Barnouin, Nicola Napoli, Nagireeddy Putluri, Clifford Qualls, Dennis T Villareal

Faculty, Staff and Students Publications

Objective: Weight loss in older men with obesity and hypogonadism accelerates musculoskeletal decline, yet the underlying metabolic mechanisms remain unclear. Testosterone replacement therapy (TRT), when added to lifestyle therapy (LT), mitigates this decline, but its metabolic basis has not been defined. We examined skeletal muscle metabolomic adaptations to LT with or without TRT, focusing on glycolysis, the pentose phosphate pathway (PPP), the tricarboxylic acid (TCA) cycle, and carnitine metabolism to identify dominant pathways of metabolic adaptation.

Design: Randomized, double-blind, placebo-controlled trial (LITROS).

Methods: Eighty-three men aged 65 years or older with obesity (BMI ≥30 kg/m2), hypogonadism (testosterone < 10.4 nmol/L), and frailty (Physical Performance Test score ≤31) were randomized to 26 weeks of LT plus TRT (LT+TRT) or LT plus placebo (LT+Pbo). A metabolomic substudy was performed in 44 participants, who underwent serial biopsies of the vastus lateralis for targeted LC-MS/MS analysis of intermediates in glycolysis, PPP, TCA cycle, and carnitine metabolism.

Results: Among the …

Surgical Delay-Associated Mortality Risk Varies By Subtype In Loco-Regional Breast Cancer Patients In Seer-Medicare, Macall Leslie Salewon, Rashmi Pathak, William Dooley, Ronald Squires, Hallgeir Rui, Inna Chervoneva, Takemi Tanaka

Department of Pharmacology, Physiology, and Cancer Biology Faculty Papers

Substantial evidence supports that delay of surgery after breast cancer diagnosis is associated with increased mortality risk, leading to the introduction of a new Commission on Cancer quality measure for receipt of surgery within 60 days of diagnosis for non-neoadjuvant patients. Breast cancer subtype is a critical prognostic factor and determines treatment options; however, it remains unknown whether surgical delay-associated breast cancer-specific mortality (BCSM) risk differs by subtype. This retrospective cohort study aimed to assess whether the impact of delayed surgery on survival varies by subtype (hormone [HR] + /HER2 -, HR -/HER2 -, and HER2 +) in patients with …

Stereotactic Cardiac Radiotherapy For Refractory Ventricular Tachycardia In Structural Heart Disease Patients: A Systematic Review, Amulya Gupta, Zeeshan Sattar, Nourhan Chaaban, Sagar Ranka, Cameron Carlson, Farhad Sami, Clifford G Robinson, Phillip S Cuculich, Seth H Sheldon, Madhu Reddy, David Akhavan, Amit Noheria

2020-Current year OA Pubs

AIMS: Among patients with structural heart disease with ventricular tachycardia (VT) refractory to medical therapy and catheter ablation, cardiac stereotactic body radiotherapy (SBRT) is a paradigm-changing treatment option. This study aims to assess the efficacy of cardiac SBRT in refractory VT by comparing the rates of VT episodes, anti-tachycardia pacing (ATP) therapies, and implantable cardioverter-defibrillator (ICD) shocks post-SBRT with pre-SBRT.

METHODS AND RESULTS: We performed a comprehensive literature search and included all clinical studies reporting outcomes on cardiac SBRT for VT. Treatment efficacy was evaluated as random-effects pooled rate-ratios of VT episodes, ATP therapies and ICD shocks post-SBRT (after 6-week …

Quantifying Interpretation Reproducibility In Vision Transformer Models With Tavac., Yue Zhao, Dylan Agyemang, Yang Liu, J Matthew Mahoney, Sheng Li

Faculty Research 2024

Deep learning algorithms can extract meaningful diagnostic features from biomedical images, promising improved patient care in digital pathology. Vision Transformer (ViT) models capture long-range spatial relationships and offer robust prediction power and better interpretability for image classification tasks than convolutional neural network models. However, limited annotated biomedical imaging datasets can cause ViT models to overfit, leading to false predictions due to random noise. To address this, we introduce Training Attention and Validation Attention Consistency (TAVAC), a metric for evaluating ViT model overfitting and quantifying interpretation reproducibility. By comparing high-attention regions between training and testing, we tested TAVAC on four public …

Effects On Maternal And Pregnancy Outcomes Of First-Trimester Malaria Infection Among Nulliparous Women From Kenya, Zambia, And The Democratic Republic Of The Congo, Sequoia I. Leuba, Daniel Westreich, Carl L. Bose, Andrew F. Olshan, Steve M. Taylor, Antoinette Tshefu, Adrien Lokangaka, Waldemar A. Carlo, Elwyn Chomba, Musaku Mwenechanya, Edward A. Liechty, Sherri L. Bucher, Osayame A. Ekhaguere, Fabian Esamai, Paul Nyongesa, Saleem Jessani, Sarah Saleem, Robert L. Goldenberg, Janet L. Moore, Tracy L. Nolen, Jennifer Hemingway-Foday, Elizabeth M. Mcclure, Marion Koso-Thomas, Richard Derman, Matthew Hoffman, Steven R. Meshnick, Melissa Bauserman

Department of Medicine Faculty Papers

BACKGROUND: Few studies have assessed the impact of first-trimester malaria infection during pregnancy. We estimated this impact on adverse maternal and pregnancy outcomes.

METHODS: In a convenience sample of women from the ASPIRIN (Aspirin Supplementation for Pregnancy Indicated risk Reduction In Nulliparas) trial in Kenya, Zambia, and the Democratic Republic of the Congo, we tested for first-trimester Plasmodium falciparum infection using quantitative polymerase chain reaction. We estimated site-specific effects on pregnancy outcomes using parametric g-computation.

RESULTS: Compared to uninfected women, we observed the adjusted site-specific prevalence differences (PDs) among women with first-trimester malaria of the following pregnancy outcomes: preterm birth …

Blinatumomab Maintenance After Allogeneic Hematopoietic Cell Transplantation For B-Lineage Acute Lymphoblastic Leukemia, Yuanxin Wang, Merve Dede, Vakul Mohanty, Jinzhuang Dou, Ziyi Li, Ken Chen

Faculty, Staff and Student Publications

Decoding cellular state transitions is crucial for understanding complex biological processes in development and disease. While recent advancements in single-cell RNA sequencing (scRNA-seq) offer insights into cellular trajectories, existing tools primarily study expressional rather than regulatory state shifts. We present CellTran, a statistical approach utilizing paired-gene expression correlations to detect transition cells from scRNA-seq data without explicitly resolving gene regulatory networks. Applying our approach to various contexts, including tissue regeneration, embryonic development, preinvasive lesions, and humoral responses post-vaccination, reveals transition cells and their distinct gene expression profiles. Our study sheds light on the underlying molecular mechanisms driving cellular state transitions, …

A Statistical Approach For Systematic Identification Of Transition Cells From Scrna-Seq Data, Yuanxin Wang, Merve Dede, Vakul Mohanty, Jinzhuang Dou, Ziyi Li, Ken Chen

Faculty, Staff and Student Publications

Decoding cellular state transitions is crucial for understanding complex biological processes in development and disease. While recent advancements in single-cell RNA sequencing (scRNA-seq) offer insights into cellular trajectories, existing tools primarily study expressional rather than regulatory state shifts. We present CellTran, a statistical approach utilizing paired-gene expression correlations to detect transition cells from scRNA-seq data without explicitly resolving gene regulatory networks. Applying our approach to various contexts, including tissue regeneration, embryonic development, preinvasive lesions, and humoral responses post-vaccination, reveals transition cells and their distinct gene expression profiles. Our study sheds light on the underlying molecular mechanisms driving cellular state transitions, …

An Empirical Model Of Carbon-Ion Relative Biological Effectiveness Based On The Linear Correlation Between Radiosensitivity To Photons And Carbon Ions, David B Flint, Scott J Bright, Conor Mcfadden, Teruaki Konishi, David K J Martinus, Mandira Manandhar, Mariam Ben Kacem, Lawrence Bronk, Gabriel O Sawakuchi

Faculty, Staff and Student Publications

Objective.To develop an empirical model to predict carbon ion (C-ion) relative biological effectiveness (RBE).Approach.We used published cell survival data comprising 360 cell line/energy combinations to characterize the linear energy transfer (LET) dependence of cell radiosensitivity parameters describing the dose required to achieve a given survival level, e.g. 5% (D5%), which are linearly correlated between photon and C-ion radiations. Based on the LET response of the metrics D5%and D37%, we constructed a model containing four free parameters that predicts cells' linear quadratic model (LQM) survival curve parameters for C-ions,αCandβC, from the reference LQM parameters …

A Latent Class Assessment Of Healthcare Access Factors And Disparities In Breast Cancer Care Timeliness, Matthew Dunn, Didong Li, Marc Emerson, Caroline Thompson, Hazel Nichols, Sarah Van Alsten, Mya Roberson, Stephanie Wheeler, Lisa Carey, Terry Hyslop, Jennifer Elston Lafata, Melissa Troester

Kimmel Cancer Center Faculty Papers

BACKGROUND: Delays in breast cancer diagnosis and treatment lead to worse survival and quality of life. Racial disparities in care timeliness have been reported, but few studies have examined access at multiple points along the care continuum (diagnosis, treatment initiation, treatment duration, and genomic testing).

METHODS AND FINDINGS: The Carolina Breast Cancer Study (CBCS) Phase 3 is a population-based, case-only cohort (n = 2,998, 50% black) of patients with invasive breast cancer diagnoses (2008 to 2013). We used latent class analysis (LCA) to group participants based on patterns of factors within 3 separate domains: socioeconomic status ("SES"), "care barriers," and …

Adiposity Throughout Adulthood And Risk Of Young-Onset Breast Cancer Tumor Subtypes In The Young Women's Health History Study, Lydia Marcus Post, Dorothy R. Pathak, Ann S. Hamilton, Kelly A. Hirko, Richard T. Houang, Emily H. Guseman, Dan Sanfelippo, Nicole Bohme Carnegie, L. Karl Olson, Hallgeir Rui, Ann G. Schwartz, Ellen M. Velie

Department of Pharmacology, Physiology, and Cancer Biology Faculty Papers

BACKGROUND: The role of adult adiposity in young-onset breast cancer (YOBC) subtype risk is not well understood.

METHODS: In this population-based case (n = 1812)-control (n = 1,381) study of invasive YOBC (ages <50 >years), cases were identified from the Los Angeles County and Metropolitan Detroit Surveillance, Epidemiology, and End Results registries, 2010 to 2015. Area-based, frequency-matched controls were sampled from the 2010 Census. General adiposity [body mass index (BMI)] and central adiposity (waist circumference and waist-to-height ratio) across adulthood and covariates were collected from in-person interviews and measurements. ORs and 95% confidence intervals (CI) for adiposity and YOBC tumor subtypes …

Investigating Impacts Of The Mycothiazole Chemotype As A Chemical Probe For The Study Of Mitochondrial Function And Aging., Naibedya Dutta, Joe A Gerke, Sofia F. Odron, Joseph D. Morris, Adam Hruby, Juri Kim, Toni Castro Torres, Sarah J. Shemtov, Jacqueline G. Clarke, Michelle C. Chang, Hooriya Shaghasi, Marissa N. Ray, Maxim Averbukh, Sally Hoang, Maria Oorloff, Athena Alcala, Matthew Vega, Hemal H Mehta, Max A Thorwald, Phillip Crews, Marc Vermulst, Gilberto Garcia, Tyler A. Johnson, Ryo Higuchi-Sanabria

Natural Sciences and Mathematics | Faculty Scholarship

Small molecule inhibitors of the mitochondrial electron transport chain (ETC) hold significant promise to provide valuable insights to the field of mitochondrial research and aging biology. In this study, we investigated two molecules: mycothiazole (MTZ) - from the marine sponge C. mycofijiensis and its more stable semisynthetic analog 8-O-acetylmycothiazole (8-OAc) as potent and selective chemical probes based on their high efficiency to inhibit ETC complex I function. Similar to rotenone (Rote), MTZ, a newly employed ETC complex I inhibitor, exhibited higher cytotoxicity against cancer cell lines compared to certain non-cancer cell lines. Interestingly, 8-OAc demonstrated greater selectivity for cancer cells …

Citation Bias, Diversity, And Ethics, Keisha S Ray, Perry Zurn, Jordan D Dworkin, Dani S Bassett, David B Resnik

Faculty and Staff Publications

How often a researcher is cited usually plays a decisive role in that person's career advancement, because academic institutions often use citation metrics, either explicitly or implicitly, to estimate research impact and productivity. Research has shown, however, that citation patterns and practices are affected by various biases, including the prestige of the authors being cited and their gender, race, and nationality, whether self-attested or perceived. Some commentators have proposed that researchers can address biases related to social identity or position by including a Citation Diversity Statement in a manuscript submitted for publication. A Citation Diversity Statement is a paragraph placed …

Artificial Intelligence Uncertainty Quantification In Radiotherapy Applications – A Scoping Review, Kareem A Wahid, Zaphanlene Y Kaffey, David P Farris, Laia Humbert-Vidan, Amy C Moreno, Mathis Rasmussen, Jintao Ren, Mohamed A Naser, Tucker J Netherton, Stine Korreman, Guha Balakrishnan, Clifton D Fuller, David Fuentes, Michael J Dohopolski

Faculty, Staff and Student Publications

Background/purpose: The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions.

Methods: We followed the PRISMA-ScR scoping review reporting guidelines. We utilized the population (human cancer patients), concept (utilization of AI UQ), context (radiotherapy applications) framework to structure our search and screening process. We conducted a systematic search spanning seven databases, supplemented by manual …

Artificial Intelligence And Machine Learning In Cancer Pain: A Systematic Review, Vivian Salama, Brandon Godinich, Yimin Geng, Laia Humbert-Vidan, Laura Maule, Kareem A Wahid, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Background/objectives: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

Results: Forty four studies from 2006 to 2023 were included. Nineteen studies used …

Explainable Artificial Intelligence And Domain Adaptation For Predicting Hiv Infection With Graph Neural Networks, Evan Yu, Jingcheng Du, Yang Xiang, Xinyue Hu, Jingna Feng, Xi Luo, John A Schneider, Degui Zhi, Kayo Fujimoto, Cui Tao

Faculty, Staff and Student Publications

OBJECTIVE: Investigation of explainable deep learning methods for graph neural networks to predict HIV infections with social network information and performing domain adaptation to evaluate model transferability across different datasets.

METHODS: Network data from two cohorts of younger sexual minority men (SMM) from two U.S. cities (Chicago, IL, and Houston, TX) were collected between 2014 and 2016. Feature importance from graph attention network (GAT) models were determined using GNNExplainer. Domain adaptation was performed to examine model transferability from one city dataset to the other dataset, training with 100% of the source dataset with 30% of the target dataset and prediction …

Tumor Expression Of Cd83 Reduces Glioma Progression And Is Associated With Reduced Immunosuppression, Malcolm F Mcdonald, Rachel Naomi Curry, Isabella O'Reilly, Brittney Lozzi, Alexis Cervantes, Zhung-Fu Lee, Anna Rosenbaum, Peihao He, Carrie Mohila, Arif O Harmanci, Akdes Serin Harmanci, Benjamin Deneen, Ganesh Rao

Faculty, Staff and Student Publications

Immunosuppression in malignant glioma remains a barrier to therapeutic development. CD83 overexpression in human and mouse glioma increases survival. CD83+ tumor cells promote signatures related to cytotoxic T cells, enhanced activation of CD8+ T cells, and increased proinflammatory cytokines. These findings suggest that tumor-expressed CD83 could mediate tumor-immune communications.

Hands-On Training About Data Clustering With Orange Data Mining Toolbox, Janez Demšar, Blaž Zupan

Faculty, Staff and Students Publications

Data clustering is a core data science approach widely used and referenced in the scientific literature. Its algorithms are often intuitive and can lead to exciting, insightful results that are easy to interpret. For these reasons, data clustering techniques could be the first method encountered in data science training. This paper proposes a hands-on approach to data clustering training suitable for introductory courses. The education approach features problem-based training that starts with the data and gradually introduces various data processing and analysis methods, illustrating them through visual representations of data and models. The proposed training is suitable for a general …