Digital Commons Network^™

Large Language Models Struggle In Token-Level Clinical Named Entity Recognition, Qiuhao Lu, Rui Li, Andrew Wen, Jinlian Wang, Liwei Wang, Hongfang Liu

Faculty, Staff and Student Publications

Large Language Models (LLMs) have revolutionized various sectors, including healthcare where they are employed in diverse applications. Their utility is particularly significant in the context of rare diseases, where data scarcity, complexity, and specificity pose considerable challenges. In the clinical domain, Named Entity Recognition (NER) stands out as an essential task and it plays a crucial role in extracting relevant information from clinical texts. Despite the promise of LLMs, current research mostly concentrates on document-level NER, identifying entities in a more general context across entire documents, without extracting their precise location. Additionally, efforts have been directed towards adapting ChatGPTfor token-level …

Barriers And Facilitators Of Digital Health Use For Self-Management Of Hypertensive Disorders By Black Pregnant Women, Morgan A Foreman, Angela Ross, Angela P H Burgess, Sahiti Myneni, Amy Franklin

Faculty, Staff and Student Publications

Although digital health tools are increasingly common for managing health conditions, these applications are often developed without consideration of differences across user populations. A reproducible framework is needed to support tailoring applications to include cultural considerations, potentially leading to better adoption and more effective use. As a first step, this study captures a snapshot of Black women's barriers and facilitators in using digital health products for self-management of hypertensive disorders of pregnancy (HDP). One-on-one semi-structured interviews were conducted with 17 Black pregnant women with HDP. We established a unique model for cultural tailoring with these experiences using Black feminist theory …

Enhancing Patient Medication Safety At Home: A Patient-Facing Technology Architecture Integrating Redcap, Visualization Dashboards, And An Ai- Driven Chatbot, Yue Yu, Yuheng Shi, Eric Yang, Katie Gahn, Heidi Mason, Yun Jiang, Yang Gong

Faculty, Staff and Student Publications

This work demonstrates a novel architecture for a patient-facing technology (PFT) that supports patients with cancer in self-managing medication concerns and symptoms after care transitions back home. Patient-generated data are collected and stored using the framework of Research Electronic Data Capture (REDCap), which serves as a key component of the architecture. Individual patient and administrator dashboards are integrated with REDCap to visualize medication or symptom-related data and generate customized report summaries. Additionally, a Q&A chatbot, using a retrieval augmented generation (RAG) framework, is intended to integrate into the architecture to further enhance the interactivity of the PFT. The proposed architecture …

Pferm: A Fair Empirical Risk Minimization Approach With Prior Knowledge, Bojian Hou, Andrés Mondragón, Davoud Ataee Tarzanagh, Zhuoping Zhou, Andrew J Saykin, Jason H Moore, Marylyn D Ritchie, Qi Long, Li Shen

Faculty, Staff and Student Publications

Fairness is crucial in machine learning to prevent bias based on sensitive attributes in classifier predictions. However, the pursuit of strict fairness often sacrifices accuracy, particularly when significant prevalence disparities exist among groups, making classifiers less practical. For example, Alzheimer's disease (AD) is more prevalent in women than men, making equal treatment inequitable for females. Accounting for prevalence ratios among groups is essential for fair decision-making. In this paper, we introduce prior knowledge for fairness, which incorporates prevalence ratio information into the fairness constraint within the Empirical Risk Minimization (ERM) framework. We develop the Prior-knowledge-guided Fair ERM (PFERM) framework, aiming …

Privacy-Preserving Fingerprinting Against Collusion And Correlation Threats In Genomic Data, Tianxi Ji, Erman Ayday, Emre Yilmaz, Pan Li

Faculty, Staff and Student Publications

Sharing genomic databases is critical to the collaborative research in computational biology. A shared database is more informative than specific genome-wide association studies (GWAS) statistics as it enables “do-it-yourself” calculations. Genomic databases involve intellectual efforts from the curator and sensitive information of participants, thus in the course of data sharing, the curator (database owner) should be able to prevent unauthorized redistributions and protect individuals’ genomic data privacy. As it becomes increasingly common for a single database be shared with multiple recipients, the shared genomic database should also be robust against collusion attack, where multiple malicious recipients combine their individual copies …

Fairness-Aware Estimation Of Graphical Models, Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Qi Long, Li Shen

Faculty, Staff and Student Publications

This paper examines the issue of fairness in the estimation of graphical models (GMs), particularly Gaussian, Covariance, and Ising models. These models play a vital role in understanding complex relationships in high-dimensional data. However, standard GMs can result in biased outcomes, especially when the underlying data involves sensitive characteristics or protected groups. To address this, we introduce a comprehensive framework designed to reduce bias in the estimation of GMs related to protected attributes. Our approach involves the integration of the pairwise graph disparity error and a tailored loss function into a nonsmooth multi-objective optimization problem, striving to achieve fairness across …

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 …

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.

Linking Artificial Sweetener Intake With Kidney Function: Insights From Nhanes 2003-2006 And Findings From Mendelian Randomization Research, Zhuoling Ran, Yuxuan Zheng, Lin Yu, Yuxian Zhang, Zhenjiang Zhang, Huijie Li, Xuhan Li, Jing Song, Li Zhang, Ran Zhang, Chang Lu, Yang Gong, Jian Gong

Faculty, Staff and Student Publications

BACKGROUND: The current investigation examines the association between artificial sweetener (AS) consumption and the likelihood of developing chronic kidney disease (CKD), along with its impact on kidney function.

METHODS: We utilized data from the National Health and Nutrition Examination Survey from 2003-2006 to conduct covariance analysis and weighted adjusted logistic regression, aiming to assess the association between artificial sweetener intake and CKD risk, as well as kidney function indicators. Subsequently, we employed Mendelian randomization methods to validate the causal relationship between the intake of artificial sweeteners, CKD risk, and kidney function indicators. Instrumental variable analysis using inverse-variance weighting and Robust …

Siglec15, Negatively Correlated With Pd-L1 In Hcc, Could Induce Cd8+ T Cell Apoptosis To Promote Immune Evasion, Zheng Chen, Mincheng Yu, Bo Zhang, Lei Jin, Qiang Yu, Shuang Liu, Binghai Zhou, Jiuliang Yan, Wentao Zhang, Xiaoqiang Li, Yongfeng Xu, Yongsheng Xiao, Jian Zhou, Jia Fan, Mien-Chie Hung, Qinghai Ye, Hui Li, Lei Guo

Faculty, Staff and Student Publications

Functional roles of SIGLEC15 in hepatocellular carcinoma (HCC) were not clear, which was recently found to be an immune inhibitor with similar structure of inhibitory B7 family members. SIGLEC15 expression in HCC was explored in public databases and further examined by PCR analysis. SIGLEC15 and PD-L1 expression patterns were examined in HCC samples through immunohistochemistry. SIGLEC15 expression was knocked-down or over-expressed in HCC cell lines, and CCK8 tests were used to examine cell proliferative ability in vitro. Influences of SIGLEC15 expression on tumor growth were examined in immune deficient and immunocompetent mice respectively. Co-culture system of HCC cell lines and …

Modernizing And Harmonizing Regulatory Data Requirements For Genetically Modified Crops-Perspectives From A Workshop, Nicholas P Storer, Abigail R Simmons, Jordan Sottosanto, Jennifer A Anderson, Ming Hua Huang, Debbie Mahadeo, Carey A Mathesius, Mitscheli Sanches Da Rocha, Shuang Song, Ewa Urbanczyk-Wochniak

Faculty, Staff and Student Publications

Genetically modified (GM) crops that have been engineered to express transgenes have been in commercial use since 1995 and are annually grown on 200 million hectares globally. These crops have provided documented benefits to food security, rural economies, and the environment, with no substantiated case of food, feed, or environmental harm attributable to cultivation or consumption. Despite this extensive history of advantages and safety, the level of regulatory scrutiny has continually increased, placing undue burdens on regulators, developers, and society, while reinforcing consumer distrust of the technology. CropLife International held a workshop at the 16th International Society of Biosafety Research …

Vagus Nerve Stimulation For The Therapy Of Dravet Syndrome: A Systematic Review And Meta-Analysis, Shuang Chen, Man Li, Ming Huang

Faculty, Staff and Student Publications

OBJECTIVE: Dravet syndrome (DS) is a refractory developmental and epileptic encephalopathy characterized by seizures, developmental delay and cognitive impairment with a variety of comorbidities, including autism-like behavior, speech dysfunction, and ataxia. Vagus nerve stimulation (VNS) is one of the common therapies for DS. Here, we aim to perform a meta-analysis and systematic review of the efficacy of VNS in DS patients.

METHODS: We systematically searched four databases (PubMed, Embase, Cochrane and CNKI) to identify potentially eligible studies from their inception to January 2024. These studies provided the effective rate of VNS in treating patients with DS. The proportions of DS …

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 …

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 …

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.

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, …

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.

Identifying Genomic Data Sources From Biomedical Literature, Xu Zuo, Ashley Gilliam, Yan Hu, Kalpana Raja, Kirk Roberts, Hua Xu

Faculty, Staff and Student Publications

Genomic research is becoming increasingly data-intensive, yet the proper reference of data remains a persistent challenge. Despite various efforts to establish and standardize data citation practices, scientists frequently fall short of accurately referencing data in their papers. This deficiency complicates the attribution of contributions to data providers and impedes the reproducibility of findings in genomic research. This study addresses this gap by introducing a gold standard corpus designed to identify mentions of genomic data sources and associated attributes, thereby offering insights into data source availability and accessibility. Within this corpus, we categorize entities into six classes, encompassing three primary entities …

Dissecting The Shared Genetic Architecture Between Alzheimer’S Disease And Frailty: A Cross-Trait Meta-Analyses Of Genome-Wide Association Studies, Nitesh Enduru, Brisa S Fernandes, Zhongming Zhao

Faculty, Staff and Student Publications

Introduction: Frailty is the most common medical condition affecting the aging population, and its prevalence increases in the population aged 65 or more. Frailty is commonly diagnosed using the frailty index (FI) or frailty phenotype (FP) assessments. Observational studies have indicated the association of frailty with Alzheimer's disease (AD). However, the shared genetic and biological mechanism of these comorbidity has not been studied.

Methods: To assess the genetic relationship between AD and frailty, we examined it at single nucleotide polymorphism (SNP), gene, and pathway levels.

Results: Overall, 16 genome-wide significant loci (15 unique loci) (p meta-analysis < 5 × 10-8) and 22 genes (21 unique genes) were identified between AD and frailty using cross-trait meta-analysis. The 8 shared loci implicated 11 genes: CLRN1-AS1, CRHR1 …

Contrastive Learning With Transformer For Adverse Endpoint Prediction In Patients On Dapt Post-Coronary Stent Implantation, Fang Li, Zenan Sun, Ahmed Abdelhameed, Tiehang Duan, Laila Rasmy, Xinyue Hu, Jianping He, Yifang Dang, Jingna Feng, Jianfu Li, Yichen Wang, Tianchen Lyu, Naomi Braun, Si Pham, Michael Gharacholou, Delisa Fairweather, Degui Zhi, Jiang Bian, Cui Tao

Faculty, Staff and Student Publications

Background: Effective management of dual antiplatelet therapy (DAPT) following drug-eluting stent (DES) implantation is crucial for preventing adverse events. Traditional prognostic tools, such as rule-based methods or Cox regression, despite their widespread use and ease, tend to yield moderate predictive accuracy within predetermined timeframes. This study introduces a new contrastive learning-based approach to enhance prediction efficacy over multiple time intervals.

Methods: We utilized retrospective, real-world data from the OneFlorida + Clinical Research Consortium. Our study focused on two primary endpoints: ischemic and bleeding events, with prediction windows of 1, 2, 3, 6, and 12 months post-DES implantation. Our approach first …

Comprehensive Analysis Of Craniopharyngioma: Epidemiology, Clinical Characteristics, Management Strategies, And Role Of Radiotherapy, Amir Mushtaq, Mohsin Fayaz, Abdul Rashid Bhat, Abbas F Abdul Hussein, Gianluca Ferini, Giuseppe Emmanuele Umana, Gianluca Scalia, Feroze Ahmad Mir, Aizul Khursheed, Bipin Chaurasia

Faculty, Staff and Student Publications

BACKGROUND/AIM: Craniopharyngiomas pose challenges in diagnosis and management due to their rare occurrence and diverse clinical manifestations. This study aimed to provide a comprehensive analysis of cranio-pharyngioma, including its epidemiological trends, clinical presentations, radiological characteristics, surgical interventions, and the role of radiotherapy.

PATIENTS AND METHODS: A retrospective observational study was conducted on 23 patients diagnosed with craniopharyngioma at our hospital from August 2017 to July 2019. Data regarding demographics, clinical presentation, radiological findings, surgical interventions, and adjuvant therapies were collected and analyzed.

RESULTS: Craniopharyngiomas exhibited a bimodal age distribution, with peaks in childhood and late adulthood. Clinical presentations varied between …

A Relay Velocity Model Infers Cell-Dependent Rna Velocity, Shengyu Li, Pengzhi Zhang, Weiqing Chen, Lingqun Ye, Kristopher W Brannan, Nhat-Tu Le, Jun-Ichi Abe, John P Cooke, Guangyu Wang

Faculty, Staff and Student Publications

RNA velocity provides an approach for inferring cellular state transitions from single-cell RNA sequencing (scRNA-seq) data. Conventional RNA velocity models infer universal kinetics from all cells in an scRNA-seq experiment, resulting in unpredictable performance in experiments with multi-stage and/or multi-lineage transition of cell states where the assumption of the same kinetic rates for all cells no longer holds. Here we present cellDancer, a scalable deep neural network that locally infers velocity for each cell from its neighbors and then relays a series of local velocities to provide single-cell resolution inference of velocity kinetics. In the simulation benchmark, cellDancer shows robust …

Intermittent Theta Burst Stimulation And Functional Connectivity In People Living With Hiv/Aids Who Smoke Tobacco Cigarettes: A Preliminary Pilot Study, Gopalkumar Rakesh, Thomas G Adams, Rajendra A Morey, Joseph L Alcorn, Rebika Khanal, Amanda E Su, Seth S Himelhoch, Craig R Rush

Faculty, Staff and Student Publications

BACKGROUND: People living with HIV (PLWHA) smoke at three times the rate of the general population and respond poorly to cessation strategies. Previous studies examined repetitive transcranial magnetic stimulation (rTMS) over left dorsolateral prefrontal cortex (L. dlPFC) to reduce craving, but no studies have explored rTMS among PLWHA who smoke. The current pilot study compared the effects of active and sham intermittent theta-burst stimulation (iTBS) on resting state functional connectivity (rsFC), cigarette cue attentional bias, and cigarette craving in PLWHA who smoke.

METHODS: Eight PLWHA were recruited (single-blind, within-subject design) to receive one session of iTBS (n=8) over the L. …

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 …

Fc Gamma Receptors Promote Antibody-Induced Lilrb4 Internalization And Immune Regulation Of Monocytic Aml, Joshua W Morse, Xun Gui, Mi Deng, Ryan Huang, Xiaohua Ye, Peng Zhao, Xuejun Fan, Wei Xiong, Chengcheng Zhang, Ningyan Zhang, Zhiqiang An

Faculty, Staff and Student Publications

The immune checkpoint leukocyte immunoglobulin-like receptor B4 (LILRB4) is found specifically on the cell surface of acute monocytic leukemia (monocytic AML), an aggressive and common subtype of AML. We have developed a humanized monoclonal IgG1 LILRB4-blocking antibody (h128-3), which improved immune regulation but reduced cell surface expression of LILRB4 in monocytic AML models by 40–60%. Interestingly, most of this effect was neutralized by mutation of the Fc region of the antibody (h128-3/N297A), which prevents interaction with Fc gamma receptors (FcγRs). This suggested that there is FcγR-dependent antigenic modulation underlying h128-3’s effects, a mechanism known to alter the function of antibodies …

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) …

Systemic Treatment Of Patients With Metastatic Breast Cancer: Asco Resource-Stratified Guideline Q And A, Sana Al Sukhun, Bogda Koczwara, Sarah Temin, Banu K Arun

Faculty, Staff and Student Publications

The Systemic Treatment of Patients with Metastatic Breast Cancer: @ASCO Resource-Stratified Guideline Q and A presents takeways, rationale, and key recommendations based on #JCOGO full guideline for patients by menopausal and marker status.

Developing Medical Genetics In A Low-Income Country: Unveiling The Journey Of The Pakistani Society Of Medical Genetics And Genomics (Psmg), Aisha Furqan, Syed A Ahmed, Rizwan Naeem, Myla Ashfaq

Faculty, Staff and Student Publications

No abstract provided.