About

Hui Zong is a researcher associated with the Department of Microbiology, Immunology, and Cancer Biology. His work focuses on molecularly targeted therapy for cancer, addressing the challenge of drug resistance in cancer cells. He utilizes a mouse genetic mosaic model called MADM to study tumor cell attack from the tumor-initiating stage and at the single-cell resolution. Hui Zong has contributed to understanding the cell of origin in cancer, the tumor microenvironment, and mechanisms of tumor progression, particularly in glioma and medulloblastoma. He has delivered lectures on brain tumor research and mouse genetic models at notable scientific courses and symposiums, and his research continues to explore the cellular and molecular mechanisms underlying cancer development and therapy.

Research topics

• Cell biology
• Cancer research
• Biology
• Genetics
• Chemistry
• Pharmacology
• Internal medicine
• Traditional medicine
• Neuroscience
• Medicine
• Anesthesia

Selected publications

• CirRFKB: A knowledgebase of circadian-related risk factors for cancer pathogenesis and personalized medicine

  Computational and Structural Biotechnology Journal · 2025-01-01

  articleOpen access

  Circadian rhythms regulate numerous physiological and biochemical processes in humans, and their disruption is linked to elevated cancer risk and progression. Although substantial research has elucidated interactions between circadian mechanisms and cancer pathways, these findings remain fragmented and poorly integrated, impeding a holistic understanding. To address this gap, we developed the Circadian-Related Risk Factor Knowledgebase for Cancer (CirRFKB), a manually curated repository documenting validated associations between the circadian clock and cancer. CirRFKB curates data from 471 articles, encompassing 46 cancer types and 4052 records, categorizing risk factors into 1449 single factors and 340 combinations. Single factors were categorized into 681 genetic factors, 106 environmental factors, 244 physiological factors, and 418 behavioral factors. These factors were further classified as 254 protective factors, 323 risk factors, 291 no-influencing factors, and 921 unclear factors. The user-friendly interface enables researchers to explore, visualize, and retrieve data through comprehensive browsing and query tools. CirRFKB provides a foundational resource that structures circadian-cancer interactions, offering systematic evidence to advance clinical applications in deep phenotyping for precision oncology and the optimization of chronotherapy. CirRFKB is publicly accessible at: http://bioinf.org.cn:9876/.

  PublisherOA PDFDOI

• Integrated UPLC–MS/MS and UPLC–Q–TOF–MS/MS analysis to reveal pharmacokinetics, tissue distribution, metabolism, and excretion of sipeimine in rats

  Frontiers in Pharmacology · 2025-05-30 · 1 citations

  articleOpen access1st author

  Background and Objective Fritillaria Bulbus is a traditional Chinese medicine used to treat respiratory diseases such as cough, expectoration, and asthma for more than 2000 years. Sipeimine, a major isosteroidal alkaloid isolated from Fritillaria Bulbus, has attracted considerable attention from the research community owing to its antitussive, anti-inflammatory, and lung-protective activities. However, there exist few reports regarding the in vivo disposition of sipeimine. This study aims to investigate the disposition of sipeimine in rats. Methods A rapid, sensitive, and selective UPLC–MS/MS approach was developed to the quantification of sipeimine in various biological samples and successfully applied to the investigation of pharmacokinetic characteristics, tissue distribution, and excretion of sipeimine in rats. A reliable UPLC–Q–TOF–MS/MS combined with a scientific metabolite identification strategy was used to characterize the metabolic transformation of sipeimine in rat plasma and urine. Results The established UPLC–MS/MS method was accurate and reliable with a good linearity (r 2 > 0.99) in the respective concentration range, satisfying the quantitative requirements. Sipeimine exhibited the characteristics of rapid absorption and slow elimination in rats, with an average oral bioavailability of 40%. Furthermore, sipeimine was rapidly distributed in all the organs except brain, and the plasma protein binding ratio of sipeimine was found to be approximately 30%. The metabolism of sipeimine in rats is chiefly accomplished via its hydroxylation, sulfation, and glucose conjugation. Analysis of fecal and urinary samples revealed that sipeimine is predominantly excreted unchanged via renal elimination. Conclusion The pharmacokinetics, tissue distribution, metabolism, and excretion of sipeimine were comprehensively characterized and elucidated. These results are expected to prove useful for the interpretation of the pharmacokinetic and pharmacodynamic characteristics of sipeimine and the traditional Chinese medicines containing sipeimine.

  PublisherOA PDFDOI

• Fiber Bragg grating flexible sensor for deformation monitoring in steel structure engineering

  AIP Advances · 2025-06-01

  articleOpen access

  In steel structure engineering, deformation monitoring is one of the key factors to ensure safe construction and stable operation. Traditional deformation monitoring methods usually use contact measurement, which has problems such as high operating costs, limited monitoring range, high monitoring error rate, and low accuracy. To solve the above issues, this article studied the fiber Bragg grating flexible sensor (FBGFS) and applied it to deformation monitoring in steel structure engineering to improve the reliability and accuracy of measurement. The design principle of FBGFS was studied, and the temperature compensation method was used to eliminate the impact of stability on the initial exit. Through SolidWorks, the sensor model was made using environmentally friendly materials, and the final sensor product was completed through 3D printing. The produced sensors were applied to deformation monitoring in steel structure engineering, and the wavelet analysis method was used to preprocess the sensor data. At the same time, a deformation monitoring model for steel structure engineering was constructed using the Kalman filtering algorithm. Using the FBGFS to monitor the deformation of steel structure engineering, the average error rate of 50 experiments was only 1.46%. When the monitoring time for steel structures was 10 years, the operating cost of using the FBGFS for monitoring was 167 200 yuan, which was 152 500 yuan lower than the operating cost of using fiber Bragg grating acceleration sensors for monitoring. Applying the FBGFS to steel structure deformation monitoring can effectively improve the accuracy of monitoring and expand the monitoring range, which is of great significance for the development of steel structures.

  PublisherDOI

• Deep Clinical Phenotyping Enhances Robustness of Prognostic Prediction to Next Level in Sepsis: A Real-World Retrospective Multi-Cohort Study with External Validation

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Real-time prediction system for tunnel deformation induced by excavation and its application

  Science Progress · 2025-04-01 · 2 citations

  articleOpen access

  To enable real-time prediction of tunnel deformation induced by foundation pit excavation, this study develops a predictive framework that integrates an analytical solution based on a two-stage unloading method with a back-propagation neural network for parameter identification. The analytical model accounts for excavation-induced stress redistribution using Mindlin's solution, while the tunnel is modeled as a beam resting on a double-sided elastic foundation to represent its deformation behavior. Five key mechanical parameters are considered for inversion, including the initial residual stress coefficient, unloading reduction coefficient, longitudinal stiffness reduction factor, and the vertical and horizontal subgrade reaction moduli. These parameters are calibrated using field monitoring data through a trained back-propagation neural network. The proposed framework is embedded into a real-time prediction system and applied to the Gubei Road Station of Shanghai Metro Line 15, which is located adjacent to Line 10. The predicted tunnel displacements exhibit strong agreement with field measurements, with maximum relative errors of 21.82% in the vertical direction and 1.95% in the horizontal direction. Except during phases of displacement trend reversal, the system consistently maintains high predictive accuracy. These results verify the reliability of the proposed method for forecasting excavation-induced tunnel deformation and underscore its applicability in proactive risk management for urban tunneling projects.

  PublisherDOI

• First glimpse of immune surveillance during premalignant progression of triple negative breast cancer 3378

  The Journal of Immunology · 2025-11-01

  articleOpen accessSenior author

  Abstract Description Immune surveillance is believed to eradicate pre-cancerous cells to guard against cancer. However, its mechanisms remain unclear, as pre-cancerous cells are difficult to detect. To gain a glimpse into immune surveillance, our lab developed a genetically engineered mouse model recapitulating human triple negative breast cancer (TNBC), which generates rare, p53-Brca1 mutant cells with unequivocal GFP labeling. Guided by the visualization of premalignant cells, we observed tertiary lymphoid structure (TLS)-like immune aggregates near mutant ducts long before tumor formation. GeoMx-based spatial profiling showed not only activation signatures in T and B cells but also elevated interferon response genes in mutant ducts associated with immune aggregates when compared to those free of immune infiltration. FTY720 treatment at premalignancy, which blocks T and B cell egress from lymph nodes, led to increased mutant cells expansion and shortened tumor latency, demonstrating the restraint of mutant cell growth by the immune system. To clarify the roles of specific immune cells, we plan to deplete CD4 T, CD8 T, and B cells at premalignancy. We will also conduct longitudinal studies on T and B cells along the tumor evolution process from premalignancy to malignancy to understand how immune surveillance eventually falters. Taken together, our studies are poised to elucidate key mechanisms of immune surveillance in the premalignant stage of TNBC. Funding Sources Supported by NIH R01-CA256199; Basser Center for BRCA; the Pinn Scholarship (UVA); UVA Cancer Center Spatial Biology Funding; UVA Cancer Center Training Grant to X.Z. Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)

  PublisherOA PDFDOI

• Deep Clinical Phenotyping Enhances Robustness of Prognostic Prediction to Next Level in Sepsis

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Non-loss engraved circuit patterning method of semi-liquid metal for precision recyclable multi-substrate circuits

  Nature Communications · 2025-11-27 · 1 citations

  articleOpen access

  Room-temperature liquid metal alloys have emerged as promising materials for flexible electronics due to their unique fluidity, conductivity, and biocompatibility. However, traditional patterning techniques for liquid metal circuits, including additive and subtractive manufacturing, face challenges such as high costs, complex processes, and environmental issues, limiting their large-scale application. This study presents a non-loss method for fabricating high-precision semi-liquid metal circuits by leveraging ethanol to modulate interfacial adhesion between liquid metal and substrates. By precisely controlling adhesion through a custom-designed displacement apparatus, the approach enables seamless patterning from 5 μm to centimeter scales across diverse substrates with features like stretchability (1000% strain), reusability, and recyclability. The technique overcomes limitations of conventional methods, offering advantages in cost-effectiveness, operational simplicity, and substrate compatibility. Demonstrations include multifunctional flexible circuits for wearable electronics, aerospace, and smart home applications, highlighting its potential to advance sustainable, scalable liquid metal electronics manufacturing.

  PublisherOA PDFDOI

• Uncovering the role of Tregs in immune surveillance against cancer with a mouse model that reveals premalignancy at single cell resolution 3379

  The Journal of Immunology · 2025-11-01

  articleOpen accessSenior author

  Abstract Description The immune surveillance hypothesis posits that the immune system controls pre-cancerous cells prior to tumor formation. However, direct visualization of the process has been nearly impossible, because pre-cancerous cells are difficult to identify in conventional cancer models. To address this technical gap, our lab established a unique mouse model called MADM (Mosaic Analysis with Double Markers) that generates rare mutant cells with unequivocal GFP labeling. In a MADM model for triple negative breast cancer, we have discovered the specific and prominent presence of T/B cell-dominated immune aggregates adjacent to premalignant mutant ducts. Characterization via flow cytometry and spatial staining has revealed these aggregates are heterogenous in size, composition, and organization, and tend to dissipate as malignant tumors form. Interestingly, a significant portion of T cells in these aggregates are CD4+ Foxp3+ Tregs, which typically play an important role in preventing excess inflammation. We hypothesize that Tregs regulate immune surveillance against premalignancy to prevent excess inflammation/autoimmunity, but in doing so also prevent eradication of premalignant cells. We are testing this hypothesis using Treg ablation during premalignancy and expect that Treg ablated mice will show increased T/B cell presence and activation in the mammary gland and decreased premalignant cell expansion. Funding Sources Supported by the Basser Center for BRCA; University of Virginia (UVA) Pinn Scholarship; NIH/NCI R01-CA256199; UVA Cancer Center Training Grant Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)

  PublisherOA PDFDOI

• Temporal single-cell analysis reveals age-associated delay in immune resolution after respiratory viral infection

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-12-17

  articleOpen access

  Abstract Aging is a major risk factor for increased morbidity and mortality following acute respiratory virus infections. To elucidate the immune determinants underlying viral pathogenesis and delayed lung repair in the aged lung, a comprehensive time-course study was conducted. Single-cell RNA sequencing (scRNAseq) and high-dimensional flow cytometry were utilized to compare lungs from young and aged mice infected with influenza A virus (IAV). Aged hosts displayed diminished alveolar macrophage (AM) and dendritic cell (DC) but elevated monocyte-derived macrophage (MoM) and interstitial macrophage (IM) presence following infection. Additionally, enhanced accumulation of adaptive immune cells, including CD4 + tissue-resident helper (T RH ) cells, CD8 + tissue-resident memory (T RM ) cells, and a B cell subset resembling age-associated B cells, was observed in the memory phase. Pathway analysis revealed that elevated type I and II interferon (IFNα/γ) signaling, especially in MoM/IM subsets, distinguished the aged hosts from the young. Inhibition of IFNα/γ signaling after viral clearance improved long-term respiratory outcomes and reduced both IM and T RH populations in aged mice. These findings highlight the pivotal role of IFNα/γ signaling, likely within MoM/IM subsets, in driving the exuberant persistence of adaptive immune cells and chronic immunopathology in the aged lung following acute viral infection.

  PublisherDOI

Recent grants

• NIH Grant R55CA136495

  NIH · $100k · 2010

• Deconstruct tumor microenvironment in medulloblastoma

  NIH · $2.0M · 2016–2022

• Characterizing the glioma cell of origin in vivo using MADM, a mouse genetic mosa

  NIH · $1.7M · 2010–2016

Frequent coauthors

• Chong Liu

  53 shared

• Hua He

  50 shared

• Yicheng Lu

  State Key Laboratory of Digital Medical Engineering

  50 shared

• David Schiff

  University of Virginia

  50 shared

• Guido Lenz

  Universidade Federal do Rio Grande do Sul

  50 shared

• Pítia Flores Ledur

  D’Or Institute for Research and Education

  50 shared

• Darlan Conterno Minussi

  The University of Texas MD Anderson Cancer Center

  49 shared

• Haiyan Zhou

  49 shared

Labs

• Zong LabPI