Medicine and Health Sciences Commons^™

Ldm-Morph: Latent Diffusion Model Guided Deformable Image Registration, Jiong Wu, Tinsu Pan, Kuang Gong

Faculty, Staff and Student Publications

Deformable image registration plays an essential role in various medical image tasks. Existing deep learning-based deformable registration frameworks primarily utilize convolutional neural networks (CNNs) or Transformers to learn features to predict the deformations. However, the lack of semantic information in the learned features limits the registration performance. Furthermore, the similarity metric of the loss function is often evaluated only in the pixel space, which ignores the matching of high-level anatomical features and can lead to deformation folding. To address these issues, in this work, we proposed LDM-Morph, an unsupervised deformable registration algorithm for medical image registration. LDM-Morph integrated features extracted …

Artificial Intelligence In Healthcare: Ethical Frameworks, Challenges, And Global Approaches To Responsible Implementation: A Perspective, Vikas Vaibhav, Arwinder Singh, Yashpal S, Raviprakash Meshram, Varun Chandran A, Kshitiza Sharma

Graduate Medical Education Research Journal

Major advancements in domains like diagnosis and personalised treatment will be seen because of the rapid growth of artificial intelligence (AI) in healthcare. But this shift additionally presents significant ethical concerns that need to be carefully considered at every stage of AI's research, use, and impact on society in medical settings.

This review summarises the findings of multiple systematic and scoping reviews, as well as studies of specific AI applications published between 2013 and 2025. Additionally, it examines ethical norms from different countries and international organisations objectively. Consistent ethical challenges identified include the "black box" problem (lack of transparency and …

Rural-Urban Differences In Hypertension Prevalence And Control In A Large Regional Health System Cohort, Yordanos M Tiruneh, Susan Mcbride, Huaxin Song, Matthew Decaro, Alaa Rihan, Theresa Byrd, Alex Baham, Jared W Magnani, Elmer V Bernstam, Jarett D Berry

Faculty, Staff and Student Publications

Background: Rural US populations bear a high burden of cardiovascular disease, with hypertension contributing to the rural-urban gap. We examined hypertension prevalence and control across the rural-urban continuum among patients in a large healthcare system in Northeast Texas.

Methods: We analyzed the electronic health record data from 363,539 patients with at least one outpatient encounter between September 2021 and April 2025. We defined hypertension using ICD-10-CM codes, antihypertensive prescriptions, or a blood pressure ≥ 130/80 mm Hg. We classified rurality using the Rural-Urban Commuting Area categories. Multivariable logistic regression models were adjusted for age, sex, race, ethnicity, insurance, and level …

Postoperative Pain After Dental Procedures: The National Dental Practice-Based Research Network Observational Study Using An Mobile Health Platform, Muhammad F Walji, Alfa-Ibrahim Yansane, Sayali Tungare, Rahma Mungia, David Holmes, Kim Funkhouser, Celeste Machen, Janelle Urata, Christoffer Redlund, Gautam Shirodkar, Hans Malmstrom, David H Yu, Oluwabunmi Tokede, D Brad Rindal, Gregg H Gilbert, Heiko Spallek, Joel White, Elsbeth Kalenderian

Faculty, Staff and Student Publications

Background: Managing postoperative dental pain is essential, but patient-reported data on pain and medication practices are scarce. The authors examined patient-reported pain, functional interference, and medication use after common dental procedures.

Methods: In this prospective observational cohort study, 2,674 adults in a multicenter US practice-based network reported pain intensity and interference via a mobile application. Outcomes were collected on days 0, 1, 3, 5, and 7 to assess trajectories across endodontics, periodontal surgery, oral surgery, and implant dentistry as well as multiple procedures within these 4 categories.

Results: Among 2,674 participants (mean age, 49.8 years; 55.6% were female), mean (SD) …

Agentic Scientific Machine Learning For Autonomous Model Discovery In Systems Pharmacology, Nazanin Ahmadi, George Karniadakis

Biology and Medicine Through Mathematics Conference

No abstract provided.

Evaluating Lingualai: A Prospective Validation Of Ai-Based Real-Time Translation Against Certified Human Interpreters, Uday P Singh, Carlos A Jaimes Garcia, Gabriel M Aisenberg, Javier Barreda Garcia, Jessica A Hernandez-Chilatra, Cecilia Wang, Dalilah Reyes De Jesus, Eileen Whalen, Veronica Santos Canellas, Amanda R Falk Vargas, Brian O Rodriguez Echevarria, Martin J Citardi, Babatope O Fatuyi, Xiaoqian Jiang

Faculty, Staff and Student Publications

Limited English proficiency affects over 25 million people in the United States and is associated with disparities in healthcare access, safety, and outcomes. We conducted a prospective, within-subject, simulation-based comparison to evaluate whether an in-house AI application (LingualAI) achieves non-inferior translation quality versus certified medical interpreters in English-Spanish otorhinolaryngology encounters. Standardized clinician-patient scripts were translated by LingualAI and by certified interpreters, and bilingual clinicians rated anonymized audio across multidomain quality measures. Using a prespecified non-inferiority margin of 0.30 points (Human - AI) on 5-point scales, LingualAI met non-inferiority for 2 of 3 primary factors (terminology accuracy Δ = 0.07; adequacy …

Assessing Trends In Medical Students’ Perceptions Regarding Statistical Analysis, Ethan Noble, Valeriy Kozmenko, Paul Thompson

Scholarship Pathways Program

Assessing Trends in Medical Students’ Perceptions Regarding Data Analysis and Statistics Knowledge and Skills

Ethan Noble, MD | Mentors: Valeriy Kozmenko, MD, Paul Thompson, PhD

Introduction: The use of evidence-based medicine requires that physicians are able to properly analyze and interpret the results of new research. The development of new research and medical knowledge is swift, and a strong foundation in statistics and research is needed for physicians and medical students to keep up with new research. Curriculum in medical education often lacks in-depth coverage of the subject, and additional curriculum has been shown to enhance student confidence and ability …

Cd8+ T Cell Differentiation Into Nk-Like Effector Cells Drives Transplant Rejection, Dawei Zou, Stephanie G Yi, Yulin Dai, Luan Truong, Lillian W Gaber, Richard J Knight, Xiang Xiao, Rafik M Ghobrial, Xian C Li, Zhongming Zhao, Wenhao Chen, A Osama Gaber

Faculty, Staff and Student Publications

T cells are central drivers of transplant rejection, yet the differentiation fates underlying this process remain unclear. Using single-cell transcriptomic profiling of human kidney allograft biopsies, we identified a predominant infiltrating CD8+ T cell subset exhibiting killer cell lectin-like receptor (KLR)+ NK-like features. Mechanistic studies in mice showed that the KLR+ subset emerged de novo post-transplantation and dominated the CD8+ T cell infiltrate in rejecting allografts. These NK-like CD8+ T cells expressed high levels of interferon regulatory factor 4 (IRF4), and Irf4 deletion disrupted their differentiation and induced transplant acceptance. Therapeutically, either costimulation blockade or mTOR inhibition substantially reduced the …

Multi-Scale Data Improves Performance Of Machine Learning Model For Long Covid Identification, Christopher Guardo, Zhang Xinmeng, Srushti Gangireddy, Yan Chao, V Eric Kerchberger, Alyson L Dickson, Emily R Pfaff, Hiral Master, Xin Yi, Melissa Basford, Christopher G Chute, Nguyen K Tran, Salvatore Mancuso, Toufeeq Ahmed Syed, Zhao Zhongming, Feng Qiping, Melissa Haendel, Christopher Lunt, Paul A Harris, Li Lang, Geoffrey S Ginsburg, Joshua C Denny, Dan M Roden, Wei Wei-Qi

Faculty, Staff and Student Publications

Background: Long COVID affects a substantial proportion of the over 778 million individuals infected with SARS-CoV-2, yet predictive models remain limited in scope. While existing efforts, such as the National COVID Cohort Collaborative (N3C), have leveraged electronic health record (EHR) data for risk prediction and identification, accumulating evidence points to additional contributions from social, behavioral, and genetic factors.

Methods: Using a diverse cohort of SARS-CoV-2-infected individuals (n > 17,200) from the NIH All of Us Research Program, we investigated whether integrating EHR data with survey-based and genomic information improves model performance.

Results: Our multi-scale approach outperforms EHR-only model's area under the …

Ethnic And Sex Differences In Salt Sensitivity Amongst Normotensive Young Adult Nigerians: Implications For Hypertension Prevention, Ahmed Oloyo, Abdullahi Adejare, Oluwakemi Odukoya, Simiat Elias, Oyewole Kushimo, Olusoga Sofola

Biomedical Sciences, East Africa

Background: High dietary salt intake is a well-established modifiable risk factor for hypertension and cardiovascular disease. Salt sensitivity – a blood pressure (BP) phenotype defined by exaggerated BP response to salt loading, remains underrecognised in normotensive populations despite its strong link to adverse cardiovascular outcomes. The burden and determinants of salt sensitivity amongst young Nigerians remain poorly understood.

Aims: This study aimed to identify demographic and behavioral risk factors associated with salt sensitivity, explore potential ethnic and sex-related differences, and determine the independent predictors of salt-sensitive (SS) BP response amongst normotensive young adult Nigerians.

Subjects and Methods: An …

Segmental Trans-Endplate Pedicle Screws Do Not Induce Spinal Deformity In A Porcine Model, Taylor R Johnson, Talissa O Generoso, Christine Farnsworth, Amishi Jobanputra, Jonathan H Wen, Austin J Stoner, Arianne Salunga, Vivian Ho, Miranda Guzman, David Berry, Vidyadhar Upasani, John S Vorhies

Faculty, Staff and Student Publications

Purpose: Growth modulation is an established technique for limb deformity correction and is increasingly applied to spinal deformities. While distraction-based posterior and anterior compressive methods have been explored, spinal growth modulation through fixation across vertebral growth centers remains unstudied. We hypothesized that unilateral trans-endplate screws-spinal epiphysiodesis trajectory (SET) screws-could induce partial anterior growth arrest and promote scoliotic deformity in a porcine model.

Methods: Four male piglets (two experimental, two control) underwent unilateral posterior spinal instrumentation at four lower thoracic levels at eight weeks of age. Experimental animals received trans-endplate SET screws; controls received pedicle screws. Radiographs obtained three months postoperatively …

Long Covid Persistence And Surveillance Gaps Across 58 Us Hospitals, Jiazi Tian, Alaleh Azhir, Matthew Decaro, Ngan Chau, Jonas Hügel, Michele Morris, Jingya Cheng, Pedram Fard, Ingrid V Bassett, Douglas S Bell, Elmer V Bernstam, Shyam Visweswaran, Jeffrey G Klann, Shawn N Murphy, Hossein Estiri

Faculty, Staff and Student Publications

Importance: Surveillance of postacute sequelae of SARS-CoV-2 infection (PASC) depends on diagnostic coding systems that capture fewer than one-half of affected individuals, rendering millions invisible to health systems and policymakers.

Objective: To quantify the gap between true PASC burden and diagnostic code-based estimates, determine the proportion representing chronic disease, and characterize organ system heterogeneity and temporal trends across diverse populations.

Design, setting, and participants: This retrospective cohort study used electronic health record data from 58 hospitals and affiliated clinics in 4 US regions, from 2017 to 2025. Adults (aged ≥18 years) with laboratory-confirmed SARS-CoV-2 infection or a COVID-19 diagnosis code …

Ucva Ontology: Standardizing Local Context Factors To Support The Analysis Of Unwarranted Clinical Variation, Apollo Mcowiti, Xubing Hao, Rebecca Z Lin, Laila Rasmy-Bekhet, Cui Tao, Susan Fenton

Faculty, Staff and Student Publications

Objectives: Unwarranted clinical variation (UCV), defined as care provided to a patient that is not proportional to the patient's needs, clinical characteristics, or preferences, results in negative patient outcomes. UCV detection is performed to compare healthcare outcomes across regions or organizations and reduce UCV incidences. However, the lack of standardized UCV concepts and the local context factors hinder the interoperability of UCV analyses between organizations. Currently, UCV analysis relies on centralized analysis, posing challenges due to the proprietary nature of an organization's healthcare and operational data. Standardizing analysis factors enables interoperable analyses by establishing a common context between organizations. This …

Essays On Ai-Driven Analytics For Enhanced Healthcare Delivery: From Onset To Outcomes In Neurological Diseases Management, Gabriel Owusu

Theses and Dissertations

Chronic neurological diseases, particularly Alzheimer's disease and related dementias (ADRDs), pose mounting global healthcare burdens. Despite advances in AI and health information technologies, early detection, precise diagnosis, and effective caregiver support remain elusive. This dissertation proposes innovative AI-enabled analytical frameworks to address these challenges across the full ADRD continuum.

The first essay designs an explainable AI-enabled clinical decision support system (CDSS) using a graph neural network for early AD identification, evaluated through usability studies demonstrating improved risk awareness and clinical engagement. The second essay introduces MADT-RNN, a multi-level attention-based deep transfer recurrent neural network that fuses convolutional and recurrent architectures …

Potential Impact Of Parasites In The Transmission Of Chronic Wasting Disease, Paulina Soto, Rodrigo Morales

Faculty, Staff and Student Publications

No abstract provided.

Optimized Ultrasound Imaging Of Phase-Change Nanodroplets, Charles R Dyall, Dmitry Nevozhay, Andy Liu, Trevor M Mitcham, George J Lu, Konstantin V Sokolov, Richard R Bouchard

Faculty, Staff and Student Publications

Phase-change nanodroplets (PCNDs) continue to generate significant research interest due to their potential to extravasate into tissue, to be targeted for molecular imaging and drug delivery, and to undergo an induced phase-change to "activated" microbubbles (MBs) for ultrasound (US) imaging. To accurately quantify molecular markers, however, one assumes a consistent proportion of PCNDs in a region of interest (ROI) are stably activated and imaged. Herein we present a framework for developing a diagnostic sequence that is optimized for PCND activation uniformity, contrast, and acquisition time. To develop this framework, activation was examined at three scales of increasing complexity: single, adjacent, …

Growth Hormone Pathway As A Prognostic And Therapeutic Biomarker In Patients With Unresectable Hepatocellular Carcinoma Treated With Radiation Therapy, Joe R Eid, Safa Kaseb, Lianchun Xiao, Ryan Sun, Mahesh Kumar Kannan, Manal Hassan, Asif Rashid, Hop S Tran Cao, Hesham M Amin, Eugene J Koay

Faculty, Staff and Student Publications

Purpose: Radiation therapy (RT) has emerged as an effective local therapy option for patients with unresectable hepatocellular carcinoma (HCC) and is associated with improved overall survival (OS). However, some patients still have poor outcomes after RT, and biomarkers are needed to stratify prognostic groups. High circulating growth hormone (GH) levels promote HCC proliferation and survival and are associated with aggressive disease. In this study, we evaluated whether pretreatment GH levels are associated with OS and progression-free survival (PFS) in patients who underwent RT for unresectable HCC.

Methods and materials: Patients undergoing RT for HCC during 2017-2023 were identified from a …

Corrigendum To 'Risk Factors For Bronchiolitis Obliterans Syndrome After Initial Detection Of Pulmonary Impairment After Hematopoietic Cell Transplantation' [Transplantation And Cellular Therapy 29/3 (2023) 204-204], Mansour Alkhunaizi, Badar Patel, Luis Bueno, Neel Bhan, Tahreem Ahmed, Muhammad H Arain, Rima Saliba, Gabriela Rondon, Burton F Dickey, Lara Bashoura, David E Ost, Liang Li, Shikun Wang, Elizabeth Shpall, Richard E Champlin, Rohtesh Mehta, Uday R Popat, Chitra Hosing, Amin M Alousi, Ajay Sheshadri

Faculty, Staff and Student Publications

No abstract provided.

Network Analysis: An Application Of Graph Theory In Biology, Kiana Dunbar

Honors Theses

Graphs are simple, visual representations of entities as nodes connected by edges. They are used to convey relationships within a system. Networks are comprised of interrelated entities that are not easily separable, producing complex, structured data. These can be seen in social groups, highway systems, and even in biological systems. This paper surveys concepts in graph theory for application to the analysis of network data to understand disease. The features of graphs provide a useful framework for thinking about relationships between different genes or proteins. The structure of graphs is also compatible with a variety of machine learning tools. Especially …

Risk Of Alzheimer Dementia After High-Dose Vs Standard-Dose Influenza Vaccination, Avram Samuel Bukhbinder, Yaobin Ling, Lauren Jhin, Elizabeth He, Kristofer Harris, Mya Rodriguez, Jenna Thomas, Gabriela Cruz, Kamal Phelps, Yejin Kim, Luyao Chen, Xiaoqian Jiang, Paul E Schulz

Faculty, Staff and Student Publications

Background and objectives: Previous studies, including large cohort analyses comparing vaccinated and unvaccinated adults, suggest that routine immunizations such as inactivated influenza vaccines (IIVs) may reduce Alzheimer dementia (AD) risk. Whether AD risk differs after high-dose IIV (H-IIV) vs standard-dose IIV (S-IIV) remains unexamined. We hypothesized that AD risk would be lower among adults ≥65 years after H-IIV compared with S-IIV.

Methods: This retrospective cohort study analyzed data spanning 2014-2019 from IQVIA PharMetrics Plus for Academics, a US health care claims database. Eligible participants were ≥65 years with ≥2 years of continuous medical and pharmaceutical coverage and no previous diagnostic …

Development Of A Multiplex Assay For Early Detection Of Pancreatic Cancer In High-Risk Groups, Sheng Pan, Ramesh Karki, Lisa A Lai, Chad J Creighton, Teresa A Brentnall, Randall E Brand, Ru Chen

Faculty, Staff and Student Publications

No abstract provided.

Robust Non-Invasive Cardiac Index Prediction Via Feature Integration And Data-Augmented Neural Networks, Chih-Hao Chang, Mei-Ling Chan, Yu-Hung Fang, Po-Lin Huang, Tsung-Yi Chen, Tsun-Kuang Chi, I Elizabeth Cha, Tzong-Rong Ger, Kuo-Chen Li, Shih-Lun Chen, Liang-Hung Wang, Jia-Ching Wang, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Concurrent with the rising consumption of ultra-processed, high-calorie diets and the decline in physical activity, obesity and related cardiovascular conditions among young adults have continued to increase, becoming an important global public health concern. This study integrates non-invasive Internet of Things (IoT) sensing devices, including the TERUMO ES-P2000 blood pressure monitor (Terumo Corp., Tokyo, Japan) and the PhysioFlow PF07 Enduro cardiac hemodynamic analyzer (Manatec Biomedical, Poissy, France), with an artificial neural network (ANN) for cardiac index (CI) prediction. Through appropriate data preprocessing and model training strategies, the generalization ability and stability of the proposed CI prediction model were significantly enhanced. …

Decoding Immunotherapy Response Through Computational Modeling, Bingrui Li, Ruihan Luo, Kexin Huang, Jiajia Liu, Weiling Zhao, Xiaobo Zhou

Faculty, Staff and Student Publications

Immunotherapy has seen success in treating patients with cancer, but variable responses underscore the need for effective patient stratification and therapy planning. Computational tools integrating multi-omics, imaging and machine learning have advanced, yet reliable personalized predictions remain challenging. This review analyzes the field through four converging paradigms: classical machine learning, deep learning, graph and network modeling, and mechanistic systems biology. We examine the evolution from correlational features to representation learning, relational inference, and causal simulation of tumor-immune dynamics, highlighting the shift towards multi-modal fusion and interpretable, clinically deployable models. By providing an integrated review of these computational tools, we hope …

Engineering Of Genetically Encoded Programmable Calcium Channel Inhibitory Binders, Xiaoxuan Liu, Sher Ali, Tien-Hung Lan, Decheng Wang, Brendan Mckee, Tatsuki Nonomura, Siyao Liu, Feng Zhao, Michael X Zhu, Yun Huang, Qing Deng, Guolin Ma, Yubin Zhou

Faculty, Staff and Student Publications

Store-operated Ca2+ release-activated Ca2+ (CRAC) channels, composed of STIM and ORAI, are essential for immune and developmental processes, and their dysregulation underlies channelopathies such as Stormorken syndrome. Here, we report the engineering of genetically encoded CRAC channel inhibitory binders (CRABs) derived from the ORAI C-terminal tail. Guided by deep mutational scanning, we optimize a membrane-anchored CRAB variant that potently inhibits Ca2+ influx and NFAT signaling, and rescues thrombocytopenia-like phenotypes in a zebrafish model of Stormorken syndrome. To enable tunable inhibition, we further design oligomeric, optogenetic (Opto-CRAB), and chemogenetic (Chemo-CRAB) variants, providing graded and real-time control of CRAC activity. Chemo-CRAB further …

A Multidomain Peptide Hydrogel-Liposome Composite For Controlled Release Of A Cyclic Dinucleotide In Oral Cancer, Joseph W R Swain, Andrea H Molina, Gemalene M Sunga, Danielle Chew-Martinez, Neeraja Dharmaraj, Alejandra Cobos Perez, Arghadip Dey, Ephraim J Vázquez-Rosado, Simon Young, Jeffrey D Hartgerink

Faculty, Staff and Student Publications

While immunotherapy is a promising treatment strategy for cancer, the majority of head and neck squamous cell carcinoma (HNSCC) patients treated with single-agent immunotherapy do not respond. Therefore, researchers are investigating combination treatments with immunostimulatory molecules that can maximize anti-tumor responses. Cyclic dinucleotides (CDNs) are STING agonists that hold promise in combination approaches, but they require frequent intratumoral administration when used in both preclinical models of HNSCC and clinical trials. To reduce administration frequency, we have created a peptide hydrogel–liposome composite system, K2-Lip(CDN), for local and prolonged availability of CDN. We investigated the loading limits of cationic liposomes in both …

Re-Purposing Pre-Existing Tilapia Aquaculture Open-Design System For Zebrafish Biomedical Research Purposes, Luke Cantu, David Sierra, Skyleigh Curtis, Maya Avila

ATU Scholars Symposium

Title: Re-purposing pre-existing tilapia open-design aquaculture systems for zebrafish biomedical research purposes.

Background: Zebrafish (Danio rerio) models are gaining popularity in biomedical research; however, the high infrastructure cost, at approximately $20,000 per unit, is a significant hurdle for primary undergraduate institutions (PUIs) establishing zebrafish facilities. This study assesses if repurposing existing tilapia open-design aquaculture systems could be a cost-effective method for zebrafish facility establishment.

Methods: Between November-2025 to February-2026, the study used a pre-existing tilapia aquaculture open-design system with personnel modified outlet filters. The water quality of this system was monitored and recorded. The study compared the stabilization phase or …

Development And Application Of A Multiplex Bead-Based Platform For Quantifying Host-Pathogen Protein-Protein Interactions, Larah Gorayeb

Electronic Theses and Dissertations

Host-pathogen protein-protein interactions are the key drivers of infectious diseases, where events such as pathogen adhesion and signaling are directly associated with disease severity. One example of this is Malaria, in which the Plasmodium falciparum infected erythrocytes adhere to endothelial receptors to avoid splenic clearance. This study investigated interactions between PfEMP1 CIDR1 field domains and host receptor CD36 using a multiplex platform to quantitatively determine binding affinities. Variants RP18 and RP27 exhibited strong affinity, suggesting a role in cytoadherence and disease severity. To evaluate broader applicability, the platform was expanded to the interaction between host protein SIVA1 and Helicobacter pylori …

Correction: Exploiting Prmt5 As A Target For Combination Therapy In Mantle Cell Lymphoma Characterized By Frequent Atm And Tp53 Mutations, Yuxuan Che, Yang Liu, Yixin Yao, Holly A Hill, Yijing Li, Qingsong Cai, Fangfang Yan, Preetesh Jain, Wei Wang, Lixin Rui, Michael Wang

Faculty, Staff and Student Publications

No abstract provided.

An Artificial Intelligence Optimized Hepatic Differentiation Unveils Nr5a2 And Ap-1 Transcriptional Regulation In Hepatic Maturation, Zijun Huo, Jian Tu, Wei-Lei Yang, Mo-Fan Huang, Ruoyu Wang, Chih-Wei Chu, An Xu, Yao Yu, Tara N Tavakol, Mikal Kizilbash, Megan E Fisher, Yu-Wen Huang, Dandan Zhu, Trinh T T Phan, Rachel Shoemaker, Ya-Wen Chen, Yang Zhang, Chad D Huff, Shih-Yu Chen, Tien-Jen Liu, Haipeng Xiao, Dung-Fang Lee, Ruiying Zhao

Faculty, Staff and Student Publications

The generation of hepatocyte-like cells (HLCs) from human pluripotent stem cells (hPSCs) holds great promise for drug discovery and cell-based therapy for liver disease. However, current differentiation protocols are complicated and unstable, and the underlying gene regulatory mechanisms of hepatic differentiation remain incompletely defined. Here, we developed a machine learning-based artificial intelligence (AI) tool using phase-contrast images of hepatic progenitor cells (HPCs), which are essential for generating HLCs. The AI tool significantly improves the success rate of hepatic differentiation without the need for immunostaining or lineage tracing. By optimizing the methodology, we achieved an impressive purity of 90-95% for HLCs …

Integration Of Aged Brain Multi-Omics Reveals Cross-System Mechanisms Underlying Alzheimer’S Disease Heterogeneity, Lucas P Scheidemantel, Katia De Paiva Lopes, Chris Gaiteri, Vilas Menon, Philip L De Jager, Julie A Schneider, Aron S Buchman, Yanling Wang, Shinya Tasaki, Roberto T Raittz, David A Bennett, Ricardo A Vialle

Faculty, Staff and Student Publications

The molecular correlates of Alzheimer's disease (AD) are increasingly being defined by multi-omics. However, findings from different data types are often difficult to reconcile. Here, we apply a data-driven multi-omics framework integrating seven omics layers from up to 1,358 aged human brain samples from the Religious Orders Study and Rush Memory and Aging Project. We demonstrate sprawling cross-omics biological factors relating to AD phenotypes. The strongest AD-associated factor (factor 8) is characterized by elevated immune activity at the epigenetic level, decreased heat shock gene expression in the transcriptome, and disrupted energy metabolism and cytoskeletal dynamics in the proteome. Unsupervised clustering …