About

Pei Zhuang, Ph.D., M.Eng., joined the UF College of Pharmacy as a Research Assistant Professor in the Department of Pharmaceutics. Her current research focuses on engineered vascularized functional tissue models, including organoids, 3D bioprinting, and microfluidic systems. She also investigates the blood-brain barrier and blood-tumor barrier, EV-mediated therapeutic delivery, and the development of novel bio-inks that modulate the tissue microenvironment. Additionally, her work encompasses neuromuscular tissue modeling, contributing to advancements in tissue engineering and regenerative medicine.

Research topics

• Computer Science
• Biology
• Medicine
• Materials science
• Cell biology
• Computational biology
• Cancer research
• Biomedical engineering
• Anatomy
• Internal medicine
• Engineering
• Immunology
• Nanotechnology

Selected publications

• Development and validation of a lipid metabolism-related prognostic model for gastric adenocarcinoma

  Translational Cancer Research · 2025-09-01

  articleOpen access

  Background: Lipid metabolism plays a critical role in the development, progression, and immune regulation of gastric adenocarcinoma (GA). However, its prognostic significance and relationship with the tumor microenvironment (TME) remain unclear. This study aimed to construct a GA lipid metabolism-related prognostic model and evaluate its clinical relevance in GA. Methods: Lipid metabolism-related differentially expressed genes (DEGs) were identified from The Cancer Genome Atlas (TCGA) database. A prognostic lipid metabolism-related signature for GA (GA-LMRS) was developed via least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. The model's predictive performance was validated in multiple cohorts. Functional enrichment, tumor mutation burden (TMB), and immune correlation analyses were performed. Drug sensitivity analysis was conducted to assess the immunotherapy response. Results: A 14-gene GA-LMRS was established, effectively stratifying patients into high- and low-risk groups. High-risk patients exhibited significantly poorer survival (P<0.001), and the model demonstrated robust predictive ability [area under the curve (AUC) >0.71]. Functional analysis revealed enrichment of the high-risk genes in extracellular matrix remodeling, immune evasion, and cancer-related pathways, whereas low-risk genes were associated with DNA repair and metabolic processes. High-risk patients had higher TMB, upregulated immune checkpoint expression, and lower sensitivity to CTLA4 inhibitors, suggesting immunotherapy resistance. In contrast, low-risk and C2 subtype patients were more likely to benefit from immune checkpoint inhibitors (ICIs). Conclusions: GA-LMRS serves as a reliable prognostic tool and reflects the immune status of patients with GA. Targeting lipid metabolism may improve immunotherapy efficacy. Future studies should integrate single-cell sequencing and multicenter clinical data to enhance model applicability and therapeutic strategies.

  PublisherDOI

• AutoRADP: An Interpretable Deep Learning Framework to Predict Rapid Progression for Alzheimer’s Disease and Related Dementias Using Electronic Health Records

  medRxiv · 2025-04-07

  preprintOpen access

  Alzheimer's disease (AD) and AD-related dementias (ADRD) exhibit heterogeneous progression rates, with rapid progression (RP) posing significant challenges for timely intervention and treatment. The increasingly available patient-centered electronic health records (EHRs) have made it possible to develop advanced machine learning models for risk prediction of disease progression by leveraging comprehensive clinical, demographic, and laboratory data. In this study, we propose AutoRADP, an interpretable autoencoder-based framework that predicts rapid AD/ADRD progression using both structured and unstructured EHR data from UFHealth. AutoRADP incorporates a rule-based natural language processing method to extract critical cognitive assessments from clinical notes, combined with feature selection techniques to identify essential structured EHR features. To address the data imbalance issue, we implement a hybrid sampling strategy that combines similarity-based and clustering-based upsampling. Additionally, by utilizing SHapley Additive exPlanations (SHAP) values, we provide interpretable predictions, shedding light on the key factors driving the rapid progression of AD/ADRD. We demonstrate that AutoRADP outperforms existing methods, highlighting the potential of our framework to advance precision medicine by enabling accurate and interpretable predictions of rapid AD/ADRD progression, and thereby supporting improved clinical decision-making and personalized interventions.

  PublisherOA PDFDOI

• Impact of PG-SGA-assessed malnutrition stratification on clinical outcomes in advanced gastrointestinal malignancies: a retrospective cohort study

  BMC Gastroenterology · 2025-10-27 · 1 citations

  articleOpen access1st author

  INTRODUCTION: The traditional TNM staging system fails to explain survival heterogeneity in advanced gastrointestinal cancer, while malnutrition remains an underutilized prognostic modulator. We aimed to establish the prognostic value of dynamic nutritional risk stratification using the Patient-Generated Subjective Global Assessment (PG-SGA). METHODS: In this retrospective cohort study, 102 patients with AJCC 8th edition stage II-IV gastrointestinal malignancies underwent serial PG-SGA assessments at admission and 3-month follow-up. Nutritional risk was stratified as low-risk (PG-SGA 3-8) or high-risk (PG-SGA ≥ 9). Associations with overall survival (OS) were analyzed via Kaplan-Meier curves and multivariable Cox regression. RESULTS: Among 102 advanced gastrointestinal malignancies patients, 68.63% (70/102) were high-risk nutritional group at admission versus 31.37% low-risk, with lower BMI in high-risk group (19.13 vs. 21.52 kg/m², P < 0.001) but comparable inflammatory/nutritional markers (P > 0.05). By 3 months, high-risk prevalence rose to 77.45% (79/102), where low-risk patients showed superior weight (58.00 vs. 50.00 kg), BMI (20.55 vs. 18.92 kg/m²), HB (120.13 vs. 100.91 g/L), ALB (41.15 vs. 34.33 g/L), PNI (47.12 vs. 39.72), CRP (1.63 vs. 11.54 mg/L), and dietary intake (all P < 0.05). Survival analysis confirmed 5-fold higher 3-year mortality risk in high-risk group (HR = 5.00, 95% CI: 2.00-12.47; P < 0.001). Multivariable analysis identified TNM stage IV (HR = 3.78, 95% CI: 1.25-11.42; P = 0.018), persistent high nutritional risk (HR = 4.09, 95% CI: 1.38-12.14; P = 0.011), and elevated CRP (HR = 2.32, 95% CI: 1.14-4.73; P = 0.020) as independent death predictors. CONCLUSION: Longitudinal PG-SGA monitoring identifies critical nutritional deterioration threshold of PG-SGA ≥ 9 at 3-month follow-up and enables early intervention. We advocate integrating dynamic nutritional risk assessment into clinical frameworks to shift from empirical to predictive management in gastrointestinal oncology.

  PublisherOA PDFDOI

• Engineering stimuli-responsive extracellular vesicles for enhanced anticancer therapeutics

  Current Opinion in Biomedical Engineering · 2025-11-26 · 2 citations

  articleOpen access
  PublisherOA PDFDOI

• Blood-tumor barrier organoids recapitulate glioblastoma microenvironment and enable high-throughput modeling of therapeutic delivery

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-11-12 · 1 citations

  preprintOpen access1st author

  The blood-brain barrier (BBB) is a highly specialized system that is critical for regulating transport between the blood and the central nervous system. In brain tumors, the vasculature system is compromised, and is referred to as the blood-tumor barrier (BTB). The ability to precisely model the unique physiological properties of the BTB is essential to decipher its role in tumor pathophysiology and for the rational design of efficacious therapeutics. Here, we introduce a robust and high-throughput in vitro 3D human BTB organoid model that recapitulates various key features of the BTB observed in vivo and in clinical GBM samples. The organoids are composed of patient-derived glioblastoma stem cells (GSCs), human brain endothelial cells (EC), astrocytes and pericytes, which are formed through self-assembly. Transcriptomic and functional analyses reveal that the GSCs in the BTB organoids exhibit enhanced level of stemness, mesenchymal signature, invasiveness and angiogenesis, and this is further confirmed in in vivo studies. We demonstrate the ability of the BTB organoids to model therapeutic delivery and drug efficacy on brain tumor cells. Collectively, our findings show that the BTB organoid model has broad utility as a clinically representative system for studying the BTB and evaluating brain tumor therapies.

  PublisherOA PDFDOI

• ViPal: A framework for virulence prediction of influenza viruses with prior viral knowledge using genomic sequences

  Journal of Biomedical Informatics · 2023-05-11 · 14 citations

  articleOpen access
  PublisherOA PDFDOI

• MODL-42. BLOOD-BRAIN TUMOR BARRIER ORGANOID MODEL FOR THERAPEUTIC DELIVERY TO GLIOBLASTOMA

  Neuro-Oncology · 2023-11-01 · 2 citations

  articleOpen access1st authorCorresponding

  Abstract The integrity of the blood-brain barrier (BBB) is compromised in brain tumors, leading to the formation of the blood-brain tumor barrier (BTB). While the BTB is generally more permeable than the BBB, it is nevertheless heterogeneously restrictive to the entry of therapeutic agents to brain tumors. Modeling the BTB accurately is crucial for understanding its physiological properties and developing effective treatments. Our lab has previously introduced a 3D in vitro human BBB organoid platform that reproduces key BBB markers and predicts compound permeability across the BBB in vivo. Here, we describe incorporating human GBM stem cells (GSC) into the organoid to form BTB organoids, presenting a novel scalable multicellular human brain tumor organoid model that simulates the BTB to facilitate therapeutic development in neuro-oncology. This model involves the self-assembly of essential BTB components, including human brain microvascular endothelial cells, astrocytes, brain pericytes and patient-derived GSCs, co-cultured under ultralow attachment conditions. Immunofluorescence microscopy results show reduced ZO-1 and Mfsd2a expression in the BTB compared to the control BBB organoids, indicating tight junction disruption and BBB breakdown by the GSCs. Correspondingly, we observe increased dextran permeability in the BTB organoids compared to the control BBB organoids, which is dependent on the GSC count per organoid. Electron and confocal microscopy reveal disorganized neovascular structures and invasive GSC behavior in the BTB organoids. Lastly, using a known GBM-targeting peptide (BTP-7), as well as a known BBB-penetrating adeno-associated virus (AAV.CPP.16) as proofs-of-concept, we demonstrate the efficacy of using the BTB organoids for modeling therapeutic delivery to brain tumors. In summary, the BTB organoid platform provides a tractable in vitro system reflecting the brain tumor microenvironment, enabling the study of GBM cell properties, investigation of barrier behavior, and modeling therapeutic delivery to brain tumors.

  PublisherOA PDFDOI

• ViPal: A Framework for Virulence Prediction of Influenza Viruses with Prior Viral Knowledge Using Genomic Sequences

  bioRxiv (Cold Spring Harbor Laboratory) · 2022-03-27 · 3 citations

  preprintOpen access

  Abstract Influenza viruses pose significant threats to public health and cause enormous economic loss every year. Previous work has revealed the viral factors that influence the virulence of influenza viruses. However, taking prior viral knowledge represented by heterogeneous categorical and discrete information into account is scarce in the existing work. How to make full use of the preceding domain knowledge into virulence study is challenging but beneficial. This paper proposes a general framework named ViPal for virulence prediction that incorporates discrete prior viral mutation and reassortment information based on all eight influenza segments. The posterior regularization technique is leveraged to transform prior viral knowledge to constraint features and integrated into the machine learning models. Experimental results on influenza genomic datasets validate that our proposed framework can improve virulence prediction performance over baselines. The comparison between ViPal and other existing methods shows the computational efficiency of our framework with superior performance. Moreover, the interpretable analysis through SHAP identifies the scores of constraint features contributing to the prediction. We hope this framework could provide assistance for the accurate detection of influenza virulence and facilitate flu surveillance.

  PublisherDOI

• A Tumor-Homing Peptide Platform Enhances Drug Solubility, Improves Blood–Brain Barrier Permeability and Targets Glioblastoma

  Cancers · 2022-04-28 · 17 citations

  articleOpen access

  BACKGROUND: Glioblastoma (GBM) is the most common and deadliest malignant primary brain tumor, contributing significant morbidity and mortality among patients. As current standard-of-care demonstrates limited success, the development of new efficacious GBM therapeutics is urgently needed. Major challenges in advancing GBM chemotherapy include poor bioavailability, lack of tumor selectivity leading to undesired side effects, poor permeability across the blood-brain barrier (BBB), and extensive intratumoral heterogeneity. METHODS: We have previously identified a small, soluble peptide (BTP-7) that is able to cross the BBB and target the human GBM extracellular matrix (ECM). Here, we covalently attached BTP-7 to an insoluble anti-cancer drug, camptothecin (CPT). RESULTS: We demonstrate that conjugation of BTP-7 to CPT improves drug solubility in aqueous solution, retains drug efficacy against patient-derived GBM stem cells (GSC), enhances BBB permeability, and enables therapeutic targeting to intracranial GBM, leading to higher toxicity in GBM cells compared to normal brain tissues, and ultimately prolongs survival in mice bearing intracranial patient-derived GBM xenograft. CONCLUSION: BTP-7 is a new modality that opens the door to possibilities for GBM-targeted therapeutic approaches.

  PublisherOA PDFDOI

• Extracellular Vesicles as an Advanced Delivery Biomaterial for Precision Cancer Immunotherapy

  Advanced Healthcare Materials · 2021 · 66 citations

  • Cancer research
  • Medicine
  • Computational biology

  In recent years, cancer immunotherapy has been observed in numerous preclinical and clinical studies for showing benefits. However, due to the unpredictable outcomes and low response rates, novel targeting delivery approaches and modulators are needed for being effective to more broader patient populations and cancer types. Compared to synthetic biomaterials, extracellular vesicles (EVs) specifically open a new avenue for improving the efficacy of cancer immunotherapy by offering targeted and site-specific immunity modulation. In this review, the molecular understanding of EV cargos and surface receptors, which underpin cell targeting specificity and precisely modulating immunogenicity, are discussed. Unique properties of EVs are reviewed in terms of their surface markers, intravesicular contents, intrinsic immunity modulatory functions, and pharmacodynamic behavior in vivo with tumor tissue models, highlighting key indications of improved precision cancer immunotherapy. Novel molecular engineered strategies for reprogramming and directing cancer immunotherapeutics, and their unique challenges are also discussed to illuminate EV's future potential as a cancer immunotherapeutic biomaterial.

  PublisherOA PDFDOI

Frequent coauthors

• Choi‐Fong Cho

  Harvard University

  16 shared

• Sean E. Lawler

  Brown University

  12 shared

• Rui Yin

  Jilin Agricultural Science and Technology University

  12 shared

• Benjamin B. Scott

  9 shared

• Niklas von Spreckelsen

  8 shared

• Bradley L. Pentelute

  Massachusetts Institute of Technology

  7 shared

• Zhuoyi Lin

  Institute for Infocomm Research

  7 shared

• Chee Keong Kwoh

  Nanyang Technological University

  7 shared