Medicine and Health Sciences Commons^™

In-Hospital Mortality Patterns And Readmissions In Patients With Chronic Obstructive Pulmonary Disease: An Analysis Of The Role Of Pulmonary Hypertension, Saad Afzal Khan, Trishna Parikh, Adishwar Rao, Akriti Agrawal, Aarohi Parikh, Farah Kazzaz, Sarah Shin, Harry Karmouty-Quintana, Maulin Patel, Kha Dinh, Bela Patel, Bindu Akkanti

Faculty, Staff and Student Publications

Chronic obstructive pulmonary disease (COPD) may be complicated by pulmonary hypertension (PH). We aimed to understand the impact of PH on in-hospital mortality and quantify the 30-day readmission rate among patients with COPD. For this cross-sectional study, we used the Nationwide Readmissions Database from 2017-2020 to identify adults ≥18 years with COPD. Patients were stratified according to PH diagnosis. Baseline characteristics between groups were compared using the Pearson chi-square test and two-sample t-test. Predictors of in-hospital mortality were determined using multivariate logistic regression analysis adjusted for demographics and confounders. The 30-day readmission rate and prevalence of PH subgroups by baseline …

Ai-Driven Biomarker Discovery & Progression Modeling For Precision Diagnosis Of Glaucoma, Cheng Huang

Computer Science and Engineering Theses and Dissertations

This dissertation presents a comprehensive study on the integration of artificial intelligence (AI) for glaucoma diagnosis and retinal image analysis. Leveraging multimodal imaging data including fundus photography, Optical Coherence Tomography Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA), the research develops a suite of deep learning frameworks designed to detect early glaucomatous changes with high precision, robustness, and interpretability. A series of novel architectures are introduced, spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models, all designed to enhance diagnostic accuracy and generalizability across diverse populations. To facilitate reproducible and scalable ophthalmic AI research, this work …

Cyanobacteria-Derived Near-Infrared Autofluorescent Exosomes Enabling Synergistic Brain Lesion Imaging And Neuroprotection, Jingmei Pan, Yayun Wang, Yikun Feng, Jiaoyang Wang, Qiongya Huang, Lei Yan, Xiaobo Zhou, Huili Sun, Huaiyu Wang, Qu Wei

Faculty, Staff and Student Publications

Precision diagnosis and treatment of central nervous system (CNS) diseases are hindered by limited probe penetration, toxicity risks, and low imaging signal-to-noise ratio (SNR). The blood-brain barrier (BBB) further restricts drug delivery, especially in stroke therapy. This study proposes and validates a natural exosome (sExos) from cyanobacteria, featuring intrinsic near-infrared-I (NIR-I) autofluorescence, with strong imaging and neuroprotective functions. As a theranostic nanoplatform, sExos enable integrated diagnosis and treatment of stroke and other brain disorders. Enriched with the fluorescent phycobiliprotein ApcE, sExos support label-free, high-SNR brain imaging in the NIR-I window. In vivo, sExos cross the BBB and accumulate in …

Targeting Grasp-Related Cortical Areas For Intracortical Brain-Machine Interfaces, Tyler R Johnson, Crispin Foli, Emily C Conlan, Katherine A Koenig, Mark J Lowe, William D Memberg, Robert F Kirsch, Eric Z Herring, Stanley F Bazarek, Emily L Graczyk, Dawn M Taylor, A Bolu Ajiboye, Jennifer A Sweet

Faculty, Staff and Student Publications

This study aimed to improve intracortical microelectrode array implantation sites for grasp-related motor decoding by integrating anatomical, functional, and vascular imaging with preoperative 3D modeling. A participant with C5 tetraplegia underwent anatomical MRI, diffusion-weighted imaging, and task-based fMRI to identify grasp-related cortical regions while avoiding vasculature and speech-critical areas. Quicktome software was used to refine target selection by integrating structural connectivity and functional activation data. A 3D-printed skull and cortical model enabled preoperative planning, including craniotomy and electrode positioning simulations. Electrode placement was validated postoperatively using neural data collected from the implanted arrays during attempted movements of the arm and …

Bridging Data Gaps In Retinal Imaging: From Structural Domain Adaptation To Topology-Aware Synthesis, Gözde Merve Demirci

Dissertations, Theses, and Capstone Projects

Comprehensive visualization of the retina is essential for diagnosing and monitoring blinding diseases such as Diabetic Retinopathy and Retinopathy of Prematurity (ROP), where pathological changes often extend beyond a single field of view. Despite significant advances in automated retinal image analysis, clinical deployment remains limited by two fundamental data gaps: a structural learning gap, arising from scarce expert annotations and poor generalization across imaging domains, and a spatial coverage gap, caused by the difficulty of acquiring multi-view retinal images in fragile populations. Although these challenges are often addressed independently, this dissertation argues that they are tightly coupled: accurate, …

Beyond Lipid-Lowering: Pleiotropic Effects Of Statins In Inflammation, Coagulation, And Cell Proliferation – Mechanisms And Clinical Implications, Kasidid Lawongsa, Yutthana Pansuwan, Supanut Kumjan

Chulalongkorn Medical Journal

Statins are widely used lipid-lowering agents that significantly reduce cardiovascular morbidity and mortality through inhibition of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase. Beyond their cholesterol-lowering properties, increasing evidence demonstrates that statins exert several pleiotropic effects independent of lipid reduction. These effects include anti-inflammatory, anti-thrombotic, and anti-proliferative actions mediated primarily through inhibition of the mevalonate pathway and downstream signaling molecules such as Rho, Rac, and Ras. Experimental and clinical studies have suggested potential benefits of statins in various non-cardiovascular conditions, including autoimmune diseases, venous thromboembolism, sepsis-associated coagulopathy, and certain malignancies. In addition, pharmacoepidemiologic studies have provided insights into the real-world effects and …

Machine Learning-Based Gene Signature Detection Highlights Cxcl12 As A Key Marker For Aml Prediction, Selahattin Alperen Uysal, Burçin Kaymaz

Chulalongkorn Medical Journal

Background: Acute myeloid leukemia (AML) is a severe hematologic malignancy marked by uncontrolled proliferation and impaired differentiation of myeloid cells, disrupting normal hematopoiesis and resulting in poor clinical outcomes. Conventional diagnostic approaches often lack the precision to accurately classify AML subtypes, necessitating the integration of advanced computational methods to improve diagnostic and therapeutic strategies.

Objectives: This study aimed to apply machine learning (ML) techniques to transcriptomic data in order to identify a concise and informative gene signature capable of distinguishing AML cases from normal samples. Additionally, the study sought to evaluate the performance of predictive models in supporting AML prediction …

Spcc: Inferring Spatial Cell-Cell Interaction By Integrating Single-Cell And Spatial Transcriptomics, Tiangang Wang, Yu Zhou, Xi Liu, Sijia Wu, Liyu Huang, Kexin Huang, Xiaobo Zhou

Faculty, Staff and Student Publications

Study of single-cell spatial biology reveals the importance of integrating single-cell and spatial data for capturing spatial structure at individual cell resolution in various fields. With the lack of cellular-level information in most spatial data, it is necessary to integrate single-cell and spatial data. Here, we developed a deep learning computational framework for alignment and mapping of unpaired single-cell and the spatial data by using adversarial joint-variational autoencoder and Random Forest model (SPCC). SPCC will generate a mapping matrix for single-cell and spatial positions that can transfer spatial location to individual cells. SPCC can be used to perform downstream analysis, …

Antigen Discovery In Multiple Myeloma Using Integrated Long-Read Transcriptomics And Immunopeptidomics, Aya Albittar

Dissertations and Theses (Open Access)

Multiple myeloma (MM) remains an incurable plasma cell malignancy, with relapse driven by persistent chemotherapy-resistant cells and residual disease following treatment. Although immunotherapies have transformed MM care, their success depends on identifying tumor-specific antigens that can be safely targeted. While aberrant genomic structural variation and transcriptional dysregulation are increasingly recognized as sources of novel antigens in MM, the HLA-presented antigenic landscape of MM remains incompletely characterized, particularly for non-canonical, structurally-derived peptides, and long-read transcriptome-informed immunopeptidomic data specific to MM remain scarce.

We established an integrated antigen discovery platform combining long-read RNA sequencing (Iso-Seq) with mass spectrometry-based HLA immunoprecipitation and immunopeptidomics …

A Measeles Susceptibility Screening Strategy For Perinatal Women, Sandra N. Davidson

Doctor of Nursing Practice Final Project Abstract

Purpose: This quality improvement project aimed to implement a standardized measles susceptibility screening strategy for perinatal patients at a large academic teaching hospital in Texas. The project aimed to improve provider compliance with routine measles immunity screening and laboratory ordering by integrating screening into existing prenatal workflows. Background: Measles remains a highly contagious disease, and ongoing outbreaks in Texas showed gaps in prenatal measles immunity screening. The absence of standardized screening increases the risk of missed opportunities to identify non-immune patients and provide appropriate follow-up. Methodology: Using the Plan-Do-Study-Act (PDSA) quality improvement model, provider education, electronic medical record (EMR) workflow …

Prescription Opioid Misuse Among High School Athletes During The Opioid Prescribing Limit Law Era: Yrbss 2017-2023, Francine R Vega, Andrea J Yatsco, Audrey Sarah Cohen, James R Langabeer, Tiffany Champagne-Langabeer, Rakshitha Vijendra, Christine Bakos-Block

Faculty, Staff and Student Publications

Background/Objectives: School-age athletes occupy a paradoxical position in the adolescent opioid crisis, combining health-oriented activity with elevated risk of sports-related injury and prescription opioid exposure. In response to the opioid epidemic, states enacted opioid prescribing limit laws (OPLLs) beginning in 2016-2017, yet whether these supply-side policies reached high-risk youth subgroups remains unclear. This study evaluated national temporal trends in prescription opioid misuse among U.S. high school students during the period of OPLL implementation and examined differences across athletic participation, concussion history, sex, grade, race/ethnicity, and substance-use profiles.

Methods: Using four biennial waves of the Youth Risk Behavior Surveillance System with …

Agent-Based Modeling Of Cellular Dynamics In Adoptive Cell Therapies, Yujia Wang

Dissertations and Theses (Open Access)

Adoptive cell therapies (ACT) have shown promising progress in combating cancer, but clinical responses remain heterogeneous. Key determinants, including the functional states of infused cells, tumor–immune interactions, and dosing strategies, are difficult to resolve experimentally in the dynamic and heterogeneous tumor microenvironment, limiting optimization of ACT products and treatment regimens. To address the gap, we developed ABMACT (Agent-Based Model for Adoptive Cell Therapies), a computational framework that reconstruct key cellular functions, interactions, and molecular signatures to recapitulate cell population dynamics associated with differential treatment responses. ABMACT encodes autonomous “virtual cells” using literature-informed rules calibrated with experimental data, enabling simulation of …

Spae: Deciphering Cell Cycle Dynamics And Cell States In Single-Cell Rna-Seq Data, Jiahao Yi, Jiajia Liu, Peng Guo, Yuan-Nong Ye, Xiaobo Zhou

Faculty, Staff and Student Publications

Rapid advances in single-cell RNA sequencing (scRNA-seq) technology have enabled the investigation of gene expression changes at the single-cell level, particularly for elucidating the heterogeneity among cells and complex biological processes. This technique reveals subtle molecular differences within individual cells, thereby offering a unique viewpoint for the investigation of cell cycle progression, cellular differentiation, and disease pathogenesis. However, accurately identifying and analyzing cell cycle dynamics in scRNA-seq data remains challenging due to the complexity of the data and the subtle differences between cell states. To address this challenge, we developed the integrated Sinusoidal and Piecewise AutoEncoder (SPAE), an autoencoder-based piecewise …

Tau Seeds Induce Neurofibrillary Tangle Formation Across Brain Regions Via Individual-Specific Connectivity, Audrey J Weber, Bernard Ng, Kelsey M Greathouse, David A Bennett, Shinya Tasaki, Chris Gaiteri, Jeremy H Herskowitz

Faculty, Staff and Student Publications

The spread of tau pathology across the cerebral cortex is closely tied to cognitive decline in Alzheimer's disease (AD). To investigate mechanisms underlying tau spread, we measured bioactivity of tau seeds from inferior temporal gyrus (ITG) and superior frontal gyrus (SFG) synaptosomes in 128 individuals and demonstrated that tau seed bioactivity associates with tau phosphorylation, neurofibrillary tangles (NFTs), and cognitive impairment. Incorporating genotype data from the same individuals within a Mendelian randomization framework showed that tau seeds in ITG induce NFTs locally as well as drive tau seeds and NFTs in SFG. Integrating antemortem functional magnetic resonance imaging data from …

Gene Transcriptional Expression Of Cerebral Blood Flow Alterations In Parkinson’S Disease: A Transcription-Neuroimaging Association Study, Jiaqi Cui, Qiane Yu, Haifeng Ran, Kexin Huang, Jie Hu, Tijiang Zhang

Faculty, Staff and Student Publications

Aim: Despite the intimate link between cerebral blood flow (CBF) alterations and Parkinson's disease (PD) pathogenesis and cognitive decline, the precise pathophysiological mechanisms driving this relationship remain elusive. This study seeks to correlate changes in CBF with regional gene expression to advance mechanistic understanding of the disease.

Methods: CBF group differences were first determined from ASL data in 53 PD and 40 healthy controls (HC) and subsequently correlated with cognitive scores (Dataset 1). A coordinate-based meta-analysis of published literature provided a second set of CBF differences (Dataset 2). Transcriptomic data from the Allen Human Brain Atlas were then correlated with …

Aberrant Ciliogenesis Induced By Enhanced Bmp Signaling Causes Heterotopic Ossification, Hiroyuki Yamaguchi, Jianbo Wang, Fangfang Yan, Jiarui Bi, Radbod Darabi, William R. Lagor, Zhongming Zhao, Aris N. Economides, Yuji Mishina, Yoshihiro Komatsu

Faculty, Staff and Student Publications

Bone morphogenetic protein (BMP) signaling is a principal driver of heterotopic ossification (HO), yet how aberrant BMP activity structurally reprograms cellular signaling machinery to develop HO remains unclear. Here, we identify BMP signaling as a direct upstream regulator of ciliogenesis that coordinates a multi-stage, pro-osteochondrogenic signaling relay during HO. Using a conditional gain-of-function BMP mouse model (Acvr1Q207D/+), we demonstrate that enhanced BMP signaling promotes primary cilium biogenesis and axonemal elongation through canonical Smad1/5/9-dependent transcriptional activation of intraflagellar transport (IFT) Ift20, a core component of the IFT machinery. Rather than operating via a singular downstream cascade, these elongated …

Advancing Bioinformatics With Language Models: Components, Applications, And Perspectives, Jiajia Liu, Mengyuan Yang, Yankai Yu, Haixia Xu, Tiangang Wang, Kang Li, Xiaobo Zhou

Faculty, Staff and Student Publications

Large language models (LLMs) are deep learning-based artificial intelligence models that have achieved remarkable success in natural language processing. Typically composed of neural networks with billions of parameters, they are trained on massive unlabeled datasets using self-supervised or semi-supervised learning. Beyond language, LLMs hold immense potential for addressing complex bioinformatics challenges. This review provides a comprehensive overview of transformer-based model applications in genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. We discuss critical components, including tokenization strategies for diverse biological data, transformer architectures, attention mechanisms, and pretraining approaches. We also survey currently available foundation models and their downstream applications across …

Medadl: High-Throughput Information Extraction Of Functional Status From Electronic Health Records To Advance Frailty Assessment In Older Adults, Sunyang Fu, Zhiyi Yue, Jennyly T Nguyen, Huipeng Liu, Jaerong Ahn, Vanessa Ramirez, Jude Des Bordes, Hongfang Liu, Min Ji Kwak, Nahid J Rianon

Faculty, Staff and Student Publications

Background: Functional status is essential for assessing frailty and planning care in older adults but is often under-documented in the structured fields of electronic health records. Manual chart review can capture functional status information but is labor-intensive and time-consuming. In this study, we developed and validated a scalable natural language processing (NLP) model to extract functional status information from unstructured electronic health record (EHR) notes.

Methods: This retrospective cohort study included 110 older adults seen at a geriatric osteoporosis clinic. A large language model-augmented symbolic NLP pipeline (MedADL) was developed to extract activities of daily living (ADL) and instrumental activities …

Clinical Document Metadata Extraction: A Scoping Review, Kurt Miller, Qiuhao Lu, William Hersh, Kirk Roberts, Steven Bedrick, Andrew Wen, Hongfang Liu

Faculty, Staff and Student Publications

Objectives: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time challenge harmonization of document metadata. Automated extraction methods have emerged to coalesce metadata from disparate practices into target schema. This scoping review aims to catalog research on clinical document metadata extraction, identify methodological trends and applications, and highlight gaps warranting further investigation.

Methods: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to identify articles from Ovid …

Implementation And Evaluation Of The Owll Intervention To Improve The Quality And Safety Of Pediatric Dental Sedation: A Mixed-Methods Approach, Kawtar Zouaidi, Jan Yeager, Sayali Tungare, Suhasini Bangar, Janelle Urata, Alfa-Ibrahim Yansane, Jungsoo Kim, Emily Sedlock, Krishna K Kookal, Yan Xiao, Oluwabunmi Tokede, Heiko Spallek, Amy Franklin, Gregory Olson, Joel White, Elsbeth Kalenderian, Muhammad F Walji

Faculty, Staff and Student Publications

Background: The OWLL (Open-Wide Learning Lab) intervention was developed using Human-Centered Design to improve the quality and safety of pediatric dental sedation. The intervention includes a patient-facing informational sedation brochure and video, and an enhanced set of clinical sedation records. Here, we report on the process evaluation 6 months after implementation in daily clinical practice.

Methods: This study was conducted at the outpatient pediatric dental clinics of 2 large US academic dental institutions. We used a mixed-methods design to assess the fidelity, acceptability, appropriateness, and feasibility of the OWLL intervention. Quantitative data were gathered through chart reviews, while qualitative data …

Sex Differences In The Protective Effect Of Brain Volume: Age Attenuates Protection In Women, Javier Gomez-Farias, Ngoc Mai Le, Joseph N Samaha, Bruna Kfoury, Hussain M Azeem, Ritesh Bajaj, Mahnoor Khalid, Freya Kanakhara, Shikha Tripathi, Shayan Shams, Luca Giancardo, Eunyoung A Lee, Robert W Regenhardt, Louise D Mccullough, Sunil A Sheth

Faculty, Staff and Student Publications

Background: Sex differences in outcomes after acute ischemic stroke are well recognized, but mechanisms remain unclear. This study evaluates whether parenchymal brain volume (PBV), age, and final infarct volume explain sex-specific differences in poststroke disability.

Methods: We analyzed a prospectively collected multicenter registry of acute ischemic stroke patients treated between 2015 and 2024. Automated pipelines quantified PBV from noncontrast computed tomography and diffusion-weighted imaging-derived final infarct volume from 24- to 48-hour magnetic resonance imaging. With the primary outcome being 90-day functional independence (modified Rankin Scale score 0-2), sex-stratified logistic regression models evaluated its association with PBV. Stratified logistic and linear …

Qeith: Quantifies Tumor Ecosystem Heterogeneity To Predict Cancer Progression And Treatment Benefit, Qiqi Liu, Jiangti Luo, Jiawei Wang, Bangqi Zhao, Xiaosheng Wang, Linjun You

Faculty, Staff and Student Publications

Intratumor heterogeneity (ITH) is a fundamental driver of therapeutic failure and disease progression. However, the complexity of the tumor ecosystem is a critical yet underexplored aspect, making its precise quantification essential for fully deciphering ITH and its clinical implications. To address this, we developed Quantifying Ecosystem Intratumor Heterogeneity (QeITH), a computational framework that applies Shannon entropy to quantify ecosystem heterogeneity by measuring the diversity and distributional entropy of cellular compositions and functional states across single-cell, bulk, and spatial transcriptomics. At the single-cell resolution, QeITH identifies elevated ITH as intrinsic markers of malignant transformation, yet enhanced sensitivity to therapy. Pan-cancer bulk …

From Performance To Practice: Knowledge-Distilled Segmentator For On-Premises Clinical Workflows, Qizhen Lan, Aaron Choi, Jun Ma, Bo Wang, Zhongming Zhao, Xiaoqian Jiang, Yu-Chun Hsu

Faculty, Staff and Student Publications

Deploying medical image segmentation models in routine clinical workflows is often constrained by on-premises infrastructure, where computational resources are fixed and cloud-based inference may be restricted by governance and security policies. While high-capacity models achieve strong segmentation accuracy, their computational demands hinder practical deployment and long-term maintainability in hospital environments. We present a deployment-oriented framework that leverages knowledge distillation to translate a high-performing segmentation model into a scalable family of compact student models without modifying the inference pipeline. The framework is primarily evaluated on nnU-Net, with additional validation across transformer and heterogeneous teacher–student architectures. The proposed approach preserves architectural compatibility …

Nmda Receptor Subunit Nmr-2 Regulates Pathogen-Induced Immune Responses Via The Nervous System In C. Elegans, Benson Otarigho, Jonathan Lalsiamthara, Alejandro Aballay

Faculty, Staff and Student Publications

Neural control of innate immunity must balance restraint of basal immune activity with rapid activation upon pathogen encounter. Glutamate, the primary excitatory neurotransmitter in the nervous system, has been implicated in several neurological disorders associated with inflammation, suggesting a potential link to immune regulation. However, how glutamatergic signaling contributes to immune balance remains unknown. Here, we demonstrated that the NMDA-type ionotropic glutamate receptor subunit NMR-2, a component of the NMDA receptor complex, acts in the

Scaling The Production Of Bromine-76 And Bromine-77 Labeled Poly-Adp-Ribose-Polymerase-Targeted Theranostics For Preclinical Use., Taylor R Johnson, Hong Beom Lee, Yngve Guttormsen, Jason C Mixdorf, Todd E Barnhart, Jonathan W Engle, Paul A Ellison

Faculty, Staff and Student Publications

Background: Bromine-76, a positron emitting radionuclide for positron emission tomography (PET) imaging, and bromine-77, an Auger electron (Ae) emitting radionuclide, make a unique halogen theranostic pair. These isotopes have previously been used to synthesize a rucaparib-derived, poly-ADP-ribose polymerase inhibitor ([76/77Br]RD1). As [77Br]bromide activity in [76/77Br]RD1 radiosyntheses increased, coincident with solid target hardware changes in our facility, radiopharmaceutical yield became less reproducible. This work describes the modifications to this hardware, distillation procedures, and [76/77Br]bromide solution chemistry to enable reproducible production of preclinical quantities of [76/77Br]RD1.

Results: Isotopically enriched cobalt selenide targets had production yields of 50.7 ± 8.7 MBq/µAh for bromine-76 …

Full-Body Ai Agent: A Perspective On Multi-Scale Collaborative Ai For Systemic Biology And Precision Medicine., Aoqi Wang, Jiajia Liu, Jianguo Wen, Yangyang Luo, Zhiwei Fan, Liren Yang, Xi Hu, Ruihan Luo, Yankai Yu, Sophia Li, Weiling Zhao, Xiaobo Zhou

Faculty, Staff and Student Publications

Artificial intelligence (AI) is increasingly applied to biomedical research, but most current systems remain limited to specific tasks, data types, or biological scales. This makes it difficult to connect molecular alterations, organelle dysfunction, cellular behavior, tissue remodeling, organ physiology, systemic regulation, and whole-body phenotypes into coherent biological reasoning. In this Perspective, we propose the Full-Body AI Agent as a hypothetical multi-agent framework and conceptual blueprint for future systemic biology and precision medicine, rather than a fully implemented software platform. This framework envisions a supervisory Full-Body AI Agent coordinating seven biological-level agents, namely Molecule, Organelle, Cell, Tissue, Organ, Organ System, and …

Content Matters, Context Matters: Unraveling Behavior Dynamics In An Online Health Community For Tobacco Cessation, Tavleen Singh, Runzhi Zhou, Kayo Fujimoto, Sahiti Myneni

Faculty, Staff and Student Publications

Objectives: The objective of this research was to examine the content and context-specific information diffusion patterns underlying communication pertaining to tobacco use from online health communities (OHCs).

Materials and methods: We utilized a mixed-methods approach comprising multidimensional qualitative coding to identify themes and communication attributes, automated text analysis leveraging advances in large language models (LLMs) to classify message content and context, and social network analysis to examine the dynamics of peer interactions in this study. Using QuitNet, an online tobacco cessation forum (n = 64 632 members, n = 2.39 million forum messages spanning 2000-2015), we extracted message-level features …

Exploring Csf Microrna Signatures As Diagnostic Biomarkers In Adult-Type Diffuse Gliomas, Maryam Pirhoushiaran, Kamilah Walker-Charles, Tsung-Hung Yao, Satwikreddy Putluri, Nehal Patel, Daniel H Wang, Isabel Wang, Srividya Arjuna, Antonio Dono, Angel Bueno, Sophia Nguyen, Ashish P Balar, Jason T Huse, Suprateek Kundu, Yoshua Esquenazi, Chirag B Patel, Sujit S Prabhu, Frederick F Lang, Leomar Y Ballester

Faculty, Staff and Student Publications

Mutations in isocitrate dehydrogenase (IDH) genes, specifically IDH1 and IDH2, are frequently observed in diffuse gliomas (DG) and define distinct molecular subtypes, namely IDH-wildtype and IDH-mutant. Abnormal expression of extracellular vesicle-derived microRNAs (EV-miRNAs) in the cerebrospinal fluid (CSF) of DG patients may serve as minimally invasive diagnostic and prognostic biomarkers. To investigate this potential, we employed miRNA-sequencing (miRNA-seq), quantitative real-time PCR (qRT-PCR), and multivariable logistic regression (MLR) to identify differentially expressed microRNAs (DE-miRNAs) in CSF samples from DG patients. qRT-PCR analysis demonstrated that EV-miR-21-5p effectively differentiated CSF from glioblastoma (GBM) patients versus controls (p = 0.012, AUC = 0.84) and …

Exploring Csf Microrna Signatures As Diagnostic Biomarkers In Adult-Type Diffuse Gliomas, Maryam Pirhoushiaran, Kamilah Walker-Charles, Tsung-Hung Yao, Satwikreddy Putluri, Nehal Patel, Daniel H Wang, Isabel Wang, Srividya Arjuna, Antonio Dono, Angel Bueno, Sophia Nguyen, Ashish P Balar, Jason T Huse, Suprateek Kundu, Yoshua Esquenazi, Chirag B Patel, Sujit S Prabhu, Frederick F Lang, Leomar Y Ballester

Faculty, Staff and Student Publications

Mutations in isocitrate dehydrogenase (IDH) genes, specifically IDH1 and IDH2, are frequently observed in diffuse gliomas (DG) and define distinct molecular subtypes, namely IDH-wildtype and IDH-mutant. Abnormal expression of extracellular vesicle-derived microRNAs (EV-miRNAs) in the cerebrospinal fluid (CSF) of DG patients may serve as minimally invasive diagnostic and prognostic biomarkers. To investigate this potential, we employed miRNA-sequencing (miRNA-seq), quantitative real-time PCR (qRT-PCR), and multivariable logistic regression (MLR) to identify differentially expressed microRNAs (DE-miRNAs) in CSF samples from DG patients. qRT-PCR analysis demonstrated that EV-miR-21-5p effectively differentiated CSF from glioblastoma (GBM) patients versus controls (p = 0.012, AUC = 0.84) and …

Nlrp3 Inflammasome: A Link Between Systemic Infection And Alzheimer’S Disease, Tatiana Barichello, Felipe Dal-Pizzol

Faculty, Staff and Student Publications

No abstract provided.