Medicine and Health Sciences Commons^™

Il-12 Reprograms Car-Expressing Natural Killer T Cells To Long-Lived Th1-Polarized Cells With Potent Antitumor Activity, Elisa Landoni, Mark G Woodcock, Gabriel Barragan, Gabriele Casirati, Vincenzo Cinella, Simone Stucchi, Leah M Flick, Tracy A Withers, Hanna Hudson, Giulia Casorati, Paolo Dellabona, Pietro Genovese, Barbara Savoldo, Leonid S Metelitsa, Gianpietro Dotti

Faculty, Staff and Students Publications

Human natural killer T cells (NKTs) are innate-like T lymphocytes increasingly used for cancer immunotherapy. Here we show that human NKTs expressing the pro-inflammatory cytokine interleukin-12 (IL-12) undergo extensive and sustained molecular and functional reprogramming. Specifically, IL-12 instructs and maintains a Th1-polarization program in NKTs in vivo without causing their functional exhaustion. Furthermore, using CD62L as a marker of memory cells in human NKTs, we observe that IL-12 maintains long-term CD62L-expressing memory NKTs in vivo. Notably, IL-12 initiates a de novo programming of memory NKTs in CD62L-negative NKTs indicating that human NKTs circulating in the peripheral blood possess an intrinsic …

Evidence Based Practice: A Decision-Making Guide For Health Information Professionals, Jonathan Eldredge

Faculty Book Display Case

This Guide introduces Evidence Based Practice to newcomers as well as serves as a resource for experienced practitioners. It focuses on health information professionals (informaticists, health sciences librarians, informationists, information scientists, data managers, archivists, etc.) within the US context, although others outside of the US health context might find elements of it to be valuable.

Enhancing Reproducibility In Single Cell Research With Biocytometry: An Inter-Laboratory Study, Pavel Fikar, Laura Alvarez, Laura Berne, Martin Cienciala, Christopher Kan, Hynek Kasl, Mona Luo, Zuzana Novackova, Sheyla Ordonez, Zuzana Sramkova, Monika Holubova, Daniel Lysak, Lyndsay Avery, Andres A. Caro, Roslyn N. Crowder, Laura A. Diaz-Martinez, David W. Donley, Rebecca R. Giorno, Irene K. Guttilla Reed, Lori Hensley

Research, Publications & Creative Work

Biomedicine today is experiencing a shift towards decentralized data collection, which promises enhanced reproducibility and collaboration across diverse laboratory environments. This inter-laboratory study evaluates the performance of biocytometry, a method utilizing engineered bioparticles for enumerating cells based on their surface antigen patterns. In centralized and aggregated inter-lab studies, biocytometry demonstrated significant statistical power in discriminating numbers of target cells at varying concentrations as low as 1 cell per 100,000 background cells. User skill levels varied from expert to beginner capturing a range of proficiencies. Measurement was performed in a decentralized environment without any instrument cross-calibration or advanced user training outside …

In Vivo Scanning Laser Fundus And High-Resolution Oct Imaging Of Retinal Ganglion Cell Injury In A Non-Human Primate Model With An Activatable Fluorescent-Labeled Tat Peptide Probe, Xudong Qiu, Seth T Gammon, Carol Rasmussen, Federica Pisaneschi, Charlene B Y Kim, James Ver Hoeve, Steven W Millward, Edward M Barnett, T Michael Nork, Paul L Kaufman, David Piwnica-Worms

Faculty, Staff and Student Publications

The optical imaging agent TcapQ488 has enabled imaging of retinal ganglion cell (RGC) injury in vivo in rodents and has potential as an effective diagnostic probe for early detection and intervention monitoring in glaucoma patients. In the present study, we investigated TcapQ488 in non-human primates (NHPs) to identify labeling efficacy and early signals of injured RGC, to determine species-dependent changes in RGC probe uptake and clearance, and to determine dose-limiting toxicities. Doses of 3, 6, and 12 nmol of TcapQ488 were delivered intravitreally to normal healthy NHP eyes and eyes that had undergone hemiretinal endodiathermy axotomy (HEA) in the inferior …

An Automated Approach For Identifying Erroneous Is-A Relations In Snomed Ct, Ran Hu, Jay Shi, Licong Cui, Rashmie Abeysinghe

Faculty, Staff and Student Publications

SNOMED CT is the most comprehensive clinical terminology employed worldwide and enhancing its accuracy is of utmost importance. In this work, we introduce an automated approach to identifying erroneous IS-A relations in SNOMED CT. We first extract linked concept-pairs from which we generate Term Difference Pairs (TDPs) that contain differences between the concepts. Given a TDP, if the reversed TDP also exists and the number of linked-pairs generating this TDP is less than those generating the reversed TDP, then we suggest the former linked-pairs as potentially erroneous IS-A relations. We applied this approach to the Clinical finding and Procedure subhierarchies …

Facilitating Clinical Information Extraction With Synthetic Data And Ontology Using Large Language Models, Yan Hu, Huan He, Qingyu Chen, Xiaoqian Jiang, Kirk Roberts, Hua Xu

Faculty, Staff and Student Publications

The rapid growth of unstructured clinical text in electronic health records necessitates robust information extraction systems, yet their development is hindered by the scarcity of high-quality annotated data. This study explores the potential of large language models to generate synthetic data for clinical named entity recognition and examines its impact on model performance. We propose a novel framework that integrates self-verified synthetic data generation with domain-specific semantic mapping using SNOMED-CT. By leveraging GPT-4o-mini for synthetic data creation and refining its quality through iterative verification and anomaly detection, we systematically evaluate the influence of synthetic data quality and quantity on fine-tuning …

Xuebijing Improves Intestinal Microcirculation Dysfunction In Septic Rats By Regulating The Vegf-A/Pi3k/Akt Signaling Pathway, A-Ling Tang, Yan Li, Li-Chao Sun, Xiao-Yu Liu, Nan Gao, Sheng-Tao Yan, Guo-Qiang Zhang

Faculty, Staff and Student Publications

BACKGROUND: This study aims to explore whether Xuebijing (XBJ) can improve intestinal microcirculation dysfunction in sepsis and its mechanism.

METHODS: A rat model of sepsis was established by cecal ligation and puncture (CLP). A total of 30 male SD rats were divided into four groups: sham group, CLP group, XBJ + axitinib group, and XBJ group. XBJ was intraperitoneally injected 2 h before CLP. Hemodynamic data (blood pressure and heart rate) were recorded. The intestinal microcirculation data of the rats were analyzed via microcirculation imaging. Enzyme-linked immunosorbent assay (ELISA) kits were used to detect the serum levels of interleukin-6 (IL-6), …

A Transformer-Based Deep Learning Approach For Fairly Predicting Post-Liver Transplant Risk Factors, Can Li, Xiaoqian Jiang, Kai Zhang

Faculty, Staff and Student Publications

Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations. The current MELD scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. However, the donor-patient matching should also consider post-transplant risk factors, such as cardiovascular disease, chronic rejection, etc., which are all common complications after transplant. Accurate prediction of these risk scores remains a significant challenge. In this study, we used predictive models to solve the above challenges. Specifically, …

Perspectives On Enhancing Clinical Informatics Education In The Artificial Intelligence Era, Hua Min, Xia Jing, Yang Gong, Ping Yu

Faculty, Staff and Student Publications

Objectives: This paper aims to analyze clinical informatic (CI) - a subfield of biomedical and health informatics (BHI) - programs to identify challenges and provide solutions for CI education. Using an online clinical decision support system (CDSS) course as a case study, we demonstrate how these challenges can be addressed. In addition, we discuss the potential impact of generative artificial intelligence (AI), along with the opportunities and risks it presents for CI education.

Methods: This is a perspective paper. The viewpoint analysis is based on a review of formal academic and training programs offered by the American Medical Informatics Association …

Soft Sets Extensions Used In Bioinformatics, Florentin Smarandache, Daniela Gifu

Branch Mathematics and Statistics Faculty and Staff Publications

This comprehensive review delves into the intricate realm of Soft Sets and their extensions, including HyperSoft Set, IndetermSoft Set, IndetermHyperSoft Set, and TreeSoft Set, within the context of biomedical data analysis. Soft Sets serve as a foundational framework for managing the inherent uncertainty and imprecision inherent in biological data, thereby facilitating informed decision-making and knowledge discovery. The exploration of Soft Set Products, particularly in the context of multiple soft sets, underscores their pivotal role in advancing biomedical research. By extending these concepts to HyperSoft Sets, researchers can unlock deeper insights into complex biological phenomena, enabling more accurate predictions and classification.

Effect Of S-Ketamine On Postoperative Nausea And Vomiting In Patients Undergoing Video-Assisted Thoracic Surgery: A Randomized Controlled Trial, Yu Qi, Meiyan Zhou, Wenting Zheng, Yaqi Dong, Weihua Li, Long Wang, Haijun Xu, Miao Zhang, Dunpeng Yang, Liwei Wang, Hai Zhou

Faculty, Staff and Student Publications

PURPOSE: Postoperative nausea and vomiting (PONV) frequently occur in patients after surgery. In this study, the authors investigated whether perioperative S-ketamine infusion could decrease the incidence of PONV in patients undergoing video-assisted thoracoscopic surgery (VATS) lobectomy.

PATIENTS AND METHODS: This prospective, randomized, double-blinded, controlled study was conducted a total of 420 patients from September 2021 to May 2023 at Xuzhou Central Hospital in China, who underwent elective VATS lobectomy under general anesthesia with tracheal intubation. The patients were randomly assigned to either the S-ketamine group or the control group. The S-ketamine group received a bolus injection of 0.5 mg/kg S-ketamine …

Design And Implementation Of Multicenter Pediatric And Congenital Studies With Cardiovascular Magnetic Resonance: Big Data In Smaller Bodies, Michael P Dilorenzo, Simon Lee, Rahul H Rathod, Francesca Raimondi, Kanwal M Farooqi, Supriya S Jain, Margaret M Samyn, Tiffanie R Johnson, Laura J Olivieri, Mark A Fogel, Wyman W Lai, Pierangelo Renella, Andrew J Powell, Sujatha Buddhe, Caitlin Stafford, Jason N Johnson, Willem A Helbing, Kuberan Pushparajah, Inga Voges, Vivek Muthurangu, Kimberley G Miles, Gerald Greil, Colin J Mcmahon, Timothy C Slesnick, Brian M Fonseca, Shaine A Morris, Jonathan H Soslow, Lars Grosse-Wortmann, Rebecca S Beroukhim, Heynric B Grotenhuis

Faculty, Staff and Student Publications

Cardiovascular magnetic resonance (CMR) has become the reference standard for quantitative and qualitative assessment of ventricular function, blood flow, and myocardial tissue characterization. There is a preponderance of large CMR studies and registries in adults; However, similarly powered studies are lacking for the pediatric and congenital heart disease (PCHD) population. To date, most CMR studies in children are limited to small single or multicenter studies, thereby limiting the conclusions that can be drawn. Within the PCHD CMR community, a collaborative effort has been successfully employed to recognize knowledge gaps with the aim to embolden the development and initiation of high-quality, …

Erratum: Toward Standardization, Harmonization, And Integration Of Social Determinants Of Health Data: A Texas Clinical And Translational Science Award Institutions Collaboration - Corrigendum, Catherine K Craven, Linda Highfield, Mujeeb Basit, Elmer V Bernstam, Byeong Yeob Choi, Robert L Ferrer, Jonathan A Gelfond, Sandi L Pruitt, Vaishnavi Kannan, Paula K Shireman, Heidi Spratt, Kayla J Torres Morales, Chen-Pin Wang, Zhan Wang, Meredith N Zozus, Edward C Sankary, Susanne Schmidt

Faculty, Staff and Student Publications

This corrects the article "Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration" in volume 8, e17.

Predicting Endothelium-Dependent Diastolic Function (Fmd)And Its Correlation With The Degree Of Coronary Artery Disease (Cad) And Plaque Vulnerability For Cardiovascular Events, Guangming Zhang, Jing Yang, Hanghang Xing, Hongning Yin, Guoqing Gu

Faculty, Staff and Student Publications

OBJECTIVE: This study aims to investigate the correlation between vascular endothelium-dependent diastolic function (FMD) and the degree of coronary artery disease (CAD), plaque vulnerability, and its predictive value for cardiovascular events.

METHODS: Initially, patients (n=100) who were admitted from January 2020 to January 2021 and intended to undergo percutaneous coronary intervention (PCI) were selected. Further, FMD in all patients was determined before the procedure and divided into a high-FMD group (≥4.2%) and a low-FMD group (

RESULTS: No significant differences were observed concerning general information, number of coronary arteries-associated branches, lesion type, involvement of the left main stem (LM), the …

Case Report: The Effect Of Automated Manual Lymphatic Drainage Therapy On Lymphatic Contractility In 4 Distinct Cases, Melissa B Aldrich, John C Rasmussen, Ron J Karni, Caroline E Fife, Frank Aviles, Kristen A Eckert, M Mark Melin

Faculty, Staff and Student Publications

INTRODUCTION: Automated manual lymphatic drainage therapy (AMLDT) is available for home use in the form of a pneumatic mat of 16 pressurized air channels that inflate and deflate to mimic the stretch and release action of manual lymphatic drainage therapy. Four cases (a patient with complex regional pain syndrome and lymphedema, a healthy patient, a breast cancer survivor with chronic pain, and a patient with a history of abdominal surgery) underwent near-infrared fluorescence lymphatic imaging (NIRFLI) with AMLDT to evaluate the effect of AMLDT on lymphatic pumping and pain.

METHODS: Each patient received 32-36 injections of 25 μg indocyanine green …

Association Between Symptom Burden And Early Lymphatic Abnormalities After Regional Nodal Irradiation For Breast Cancer, Alison K Yoder, Tianlin Xu, Peter Youssef, Sarah Desnyder, Kathryn E Marqueen, Lynn Isales, Ruitao Lin, Benjamin D Smith, Wendy A Woodward, Michael C Stauder, Eric A Strom, Melissa B Aldrich, Simona F Shaitelman

Faculty, Staff and Student Publications

PURPOSE: Dermal backflow visualized on near-infrared fluorescence lymphatic imaging (NIRF-LI) signals preclinical lymphedema that precedes the development of volumetrically defined lymphedema. We sought to evaluate whether dermal backflow correlates with patient-reported lymphedema outcomes (PRLO) surveys in breast cancer patients treated with regional nodal irradiation (RNI).

METHODS AND MATERIALS: Patients with breast cancer planned for axillary dissection and RNI prospectively underwent perometry, NIRF-LI, and PRLOs (the Lymphedema Symptom Intensity and Distress Survey [LSIDS] and QuickDASH) at baseline, after surgery, and at 6, 12, and 18 months after radiation. Clinical lymphedema was defined as an arm volume increase ≥5% over baseline. Trends …

Characterizing Nanopore Sequencing Artifacts With Deep Learning, David Zhou

Master's Projects

Oxford Nanopore sequencing is a revolutionary new technology for sequencing DNA molecules in long stretches. However, it has a significantly higher error rate than conventional short-read sequencing, resulting in numerous sequencing artifacts. These artifacts can be indistinguishable from low frequency somatic variants, which is a roadblock for cancer diagnosis using liquid biopsies. In this study, benchmarked human genome samples from Genome in a Bottle were used to create a dataset of labeled variants, including artifacts and true variants. Variant features, including sequence context, were used to train various deep learning models. The multi-input neural network combining sequence context features and …

Protein-Centric Omics Analysis Reveals Circulating Complements Linked To Non-Viral Liver Diseases As Potential Therapeutic Targets, Yingzhou Shi, Hang Dong, Shiwei Sun, Xiaoqin Wu, Jiansong Fang, Jianbo Zhao, Junming Han, Zongyue Li, Huixiao Wu, Luna Liu, Wanhong Wu, Yang Tian, Guandou Yuan, Xiude Fan, Chao Xu

Faculty, Staff and Student Publications

BACKGROUND/AIMS: To evaluate the causal correlation between complement components and non-viral liver diseases and their potential use as druggable targets.

METHODS: We conducted Mendelian randomization (MR) to assess the causal role of circulating complements in the risk of non-viral liver diseases. A complement-centric protein interaction network was constructed to explore biological functions and identify potential therapeutic options.

RESULTS: In the MR analysis, genetically predicted levels of complement C1q C chain (C1QC) were positively associated with the risk of autoimmune hepatitis (odds ratio 1.125, 95% confidence interval 1.018-1.244), while complement factor H-related protein 5 (CFHR5) was positively associated with the risk …

Disentangling Accelerated Cognitive Decline From The Normal Aging Process And Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach, Yulin Dai, Yu-Chun Hsu, Brisa S Fernandes, Kai Zhang, Xiaoyang Li, Nitesh Enduru, Andi Liu, Astrid M Manuel, Xiaoqian Jiang, Zhongming Zhao, Alzheimer’S Disease Neuroimaging Initiative

Faculty, Staff and Student Publications

BACKGROUND: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

OBJECTIVE: To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach.

METHODS: We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. …

Preparing Healthcare Education For An Ai-Augmented Future, Jiajie Zhang, Susan H Fenton

Faculty, Staff and Student Publications

Artificial intelligence (AI) fundamentally transforms healthcare education as a knowledge enterprise, creating a distributed cognitive system composed of the human brain, which remains relatively unchanged, and AI-based knowledge and cognitive functions, which have accelerated exponentially in scale and power. Education must focus on developing skills to collaborate with AI and on achieving outcomes like problems solved and discoveries made. Curriculum and education policies also need to adapt to this transformation.

Statins Are Rarely Prescribed For Incidentally Discovered Covert Cerebrovascular Disease: A Retrospective Cohort In A Large Electronic Health Record (Ehr) Identified Using Natural Language Processing, Lester Y Leung, Eric Puttock, David F Kallmes, Patrick Luetmer, Sunyang Fu, Chengyi X Zheng, Hongfang Liu, Wansu Chen, David M Kent

Faculty, Staff and Student Publications

Introduction: While incidentally discovered covert cerebrovascular diseases (id-CCD) are associated with future stroke, it is not known if patients with id-CCD are prescribed statins.

Methods: Patients age ≥50 with id-CCD on neuroimaging from 2009 to 2019 with no prior ischaemic stroke, transient ischaemic attack or dementia were identified using natural language processing in a large real-world cohort. Robust Poisson multivariable regression was used to assess statin prescription among patients without prior statins.

Results: Among 2 41 050 patients, 74 975 patients (31.1%; 4.7% with covert brain infarcts (CBI); 29.0% with white matter disease (WMD)) had id-CCD. 53.5% (95% CI 53.2 …

The Enact Network Is Acting On Housing Instability And The Unhoused Using The Open Health Natural Language Processing Toolkit, Daniel R Harris, Sunyang Fu, Andrew Wen, Alexandria Corbeau, Darren Henderson, Jordan Hilsman, David Oniani, Yanshan Wang

Faculty, Staff and Student Publications

Housing is an environmental social determinant of health that is linked to mortality and clinical outcomes. We developed a lexicon of housing-related concepts and rule-based natural language processing methods for identifying these housing-related concepts within clinical text. We piloted our methods on several test cohorts: a synthetic cohort generated by ChatGPT for initial infrastructure testing, a cohort with substance use disorders (SUD), and a cohort diagnosed with problems related to housing and economic circumstances (HEC). Our methods successfully identified housing concepts in our ChatGPT notes (recall = 1.0, precision = 1.0), our SUD population (recall = 0.9798, precision = 0.9898), …

Descriptor:Benchmarking Secure Neural Network Evaluation Methods For Protein Sequence Classification (Idash24), Arif Harmanci, Luyao Chen, Miran Kim, Xiaoqian Jiang

Faculty, Staff and Student Publications

To uniformly test and benchmark the secure evaluation of transformer-based models, we designed the iDASH24 homomorphic encryption track dataset. The dataset comprises a protein family classification model with a transformer architecture and an example dataset that is used to build and test the secure evaluation strategies. This dataset was used in the challenge period of iDASH24 Genomic Privacy Competition, where the teams designed secure evaluation of the classification model using a homomorphic encryption scheme. Combined with the benchmarking results and companion methods, iDASH24 dataset is a unique resource that can be used to benchmark secure evaluation of neural network models.

Feasible Diet And Circadian Interventions Reduce In Vivo Progression Of Flt3-Itd-Positive Acute Myeloid Leukemia, Megan Rodriguez, Baharan Fekry, Brianna Murphy, Mary Figueroa, Tiewei Cheng, Margaret Raber, Lisa Wartenberg, Donna Bell, Lisa Triche, Karla Crawford, Huaxian Ma, Kendra Allton, Ruwaida Ahmed, Jaime Tran, Christine Ranieri, Marina Konopleva, Michelle Barton, Cesar Nunez, Kristin Eckel-Mahan, Joya Chandra

Faculty, Staff and Student Publications

BACKGROUND: Acute myeloid leukemia (AML) with an internal tandem duplication in the fms-like tyrosine kinase receptor 3 gene (FLT3-ITD) is associated with poor survival, and few studies have examined the impact of modifiable behaviors, such as nutrient quality and timing, in this subset of acute leukemia.

METHODS: The influence of diet composition (low-sucrose and/or low-fat diets) and timing of diet were tested in tandem with anthracycline treatment in orthotopic xenograft mouse models. A pilot clinical study to test receptivity of pediatric leukemia patients to macronutrient matched foods was conducted. A role for the circadian protein, BMAL1 (brain and muscle ARNT-like …

Transdisciplinary Perspectives For Health Systems Science, Jiang Bian, Nan Liu, Shauna M Overgaard, Rema Padman, Peter A D Steel, Victoria Tiase, Clifford Alan Whitcomb, Jiajie Zhang, Yiye Zhang

Faculty, Staff and Student Publications

Health systems science uses systems thinking as part of a transdisciplinary approach that transcends traditional disciplinary boundaries. It integrates and synthesizes knowledge from multiple disciplines to address real-world problems in healthcare with pragmatic solutions. This editorial defines health systems from the perspectives of systems thinking, science, and engineering, discusses their current challenges and opportunities, and envisions how health systems science as a field can advance the continuous learning and improvement of health systems.

Prioritizing Clinically Significant Lung Cancer Somatic Mutations For Targeted Therapy Through Efficient Ngs Data Filtering System, Jinlian Wang, Hui Li, Hongfang Liu

Faculty, Staff and Student Publications

In the realm of lung cancer treatment, where genetic heterogeneity presents formidable challenges, precision oncology demands an exacting approach to identify and hierarchically sort clinically significant somatic mutations. Current Next-Generation Sequencing (NGS) data filtering pipelines, while utilizing various external databases for mutation screening, often fall short in comprehensive integration and flexibility needed to keep pace with the evolving landscape of clinical data. Our study introduces a sophisticated NGS data filtering system, which not only aggregates but effectively synergizes diverse data sources, encompassing genetic variants, gene functions, clinical evidence, and an extensive body of literature. This system is distinguished by a …

Sequencing Conversational Turns In Peer Interactions: An Integrated Approach For Evidence-Based Conversational Agent For Just-In-Time Nicotine Cravings Intervention, Tavleen Singh, Michael Truong, Kirk Roberts, Sahiti Myneni

Faculty, Staff and Student Publications

BACKGROUND: Risky health behaviors place an enormous toll on public health systems. While relapse prevention support is integrated with most behavior modification programs, the results are suboptimal. Recent advances in artificial intelligence (AI) applications provide us with unique opportunities to develop just-in-time adaptive behavior change solutions.

METHODS: In this study, we present an innovative framework, grounded in behavioral theory, and enhanced with social media sequencing and communications scenario builder to architect a conversational agent (CA) specialized in the prevention of relapses in the context of tobacco cessation. We modeled peer interaction data (n = 1000) using the taxonomy of behavior …

Linking Cancer Clinical Trials To Their Result Publications, Evan Pan, Kirk Roberts

Faculty, Staff and Student Publications

The results of clinical trials are a valuable source of evidence for researchers, policy makers, and healthcare professionals. However, online trial registries do not always contain links to the publications that report on their results, instead requiring a time-consuming manual search. Here, we explored the application of pre-trained transformer-based language models to automatically identify result-reporting publications of cancer clinical trials by computing dense vectors and performing semantic search. Models were fine-tuned on text data from trial registry fields and article metadata using a contrastive learning approach. The best performing model was PubMedBERT, which achieved a mean average precision of 0.592 …

Community Scientist Program Provides Bi-Directional Communication And Co-Learning Between Researchers And Community Members, Jessica Alvarado, Larkin L Strong, Birnur Buzcu-Guven, Leonetta B Thompson, Erica Cantu, Chelsea C Carrier, Chiamaka D Chukwu, Cassandra L Harris, Luz K Melendez, Crystal L Roberson, Angela M Ross, Sophia C Russell, Pablo Sanchez, Amirali Tahanan, Blair C Zdenek, Belinda M Reininger, Lorna H Mcneill

Faculty, Staff and Student Publications

Community involvement in research is key to translating science into practice, and new approaches to engaging community members in research design and implementation are needed. The Community Scientist Program, established at the MD Anderson Cancer Center in Houston in 2018 and expanded to two other Texas institutions in 2021, provides researchers with rapid feedback from community members on study feasibility and design, cultural appropriateness, participant recruitment, and research implementation. This paper aims to describe the Community Scientist Program and assess Community Scientists' and researchers' satisfaction with the program. We present the analysis of the data collected from 116 Community Scientists …

New-Onset Diabetes Is A Predictive Risk Factor For Pancreatic Lesions In High-Risk Individuals: An Observational Cohort Study, Seyda Baydogan, Chirayu Mohindroo, Merve Hasanov, Maria F Montiel, Pompeyo Quesada, Irina M Cazacu, Adrianna A Luzuriaga Chavez, Maureen E Mork, Wenli Dong, Lei Feng, Y Nancy You, Banu Arun, Eduardo Vilar, Powel Brown, Matthew H G Katz, Suresh T Chari, Anirban Maitra, Eric P Tamm, Michael P Kim, Manoop S Bhutani, Florencia Mcallister

Faculty, Staff and Student Publications

BACKGROUND AND OBJECTIVES: Pancreatic cancer (PC) is the third cause of cancer-related deaths. Early detection and interception of premalignant pancreatic lesions represent a promising strategy to improve outcomes. We evaluated risk factors of focal pancreatic lesions (FPLs) in asymptomatic individuals at hereditary high risk for PC.

METHODS: This is an observational single-institution cohort study conducted over a period of 5 years. Surveillance was performed through imaging studies (EUS or magnetic resonance imaging/magnetic resonance cholangiopancreatography) and serum biomarkers. We collected demographic characteristics and used univariate and multivariate logistic regression models to evaluate associations between potential risk factors and odd ratios (ORs) …