Biomedical subjects

Yuanyuan Yu

Publications and source records attributed to Yuanyuan Yu.

3 recordsLinked to original sources

PubMed2026

Enrichment Performance Assessment of Extracellular Vesicles Using Different Functionalized Magnetic Materials and Application in Urinary Proteomics of Prostate Cancer.

Extracellular vesicles (EVs) are lipid bilayer nanovesicles that mediate intercellular communication and hold significant potential for clinical applications. Although material-based isolation strategies offer promising alternatives to conventional methods, their relative performances have not been systematically evaluated. In this study, we conducted a comparative assessment of magnetic nanomaterials with distinct surface functionalities, including metal oxides (TiO2), metal-organic frameworks (UiO-66), biopolymeric materials (chitosan), and lipid probes (DSPE-PEG, DOPE-PEG, and CLS-PEG). A comprehensive evaluation across multiple dimensions including capture capacity, capture rate, sample volume, and product purity reveals that the bifunctional magnetic nanomaterial Fe3O4@UiO-66@DSPE material exhibits superior EV capture performance. This material enables the efficient and stable enrichment of high-purity EVs by synergizing Zr4+-phosphate coordination with lipid bilayer anchoring, and preserves EV biological integrity and activity. Meanwhile, this method could be highly compatible with proteomics, and over 1000 proteins are identified by proteomic analysis of urinary EVs, while 34 proteins are upregulated and 25 proteins are downregulated in prostate cancer patients relative to healthy donors. Notably, the differentially expressed proteins, such as AGT, ITIH4, and PGLYRP2, are associated with disease progression. Overall, this work highlights the superior performance of the Fe3O4@UiO-66@DSPE material for efficient and selective EV isolation. It provides a powerful tool for clinical liquid biopsy and proteomic biomarker discovery, enabling early diagnosis, prognostic evaluation, and precision therapy.

Humans↗

PubMed2026

Systematic Approach for Compound Angus Populations Revealing Positional Candidate Genes and Improving Prediction Accuracy in Carcass Traits.

Carcass traits, which reflect growth performance and muscle development, are economically important in beef cattle, yet their genetic determinants remain poorly characterized. Both single-population Genome-wide association studies (GWAS) methods, such as BLINK, and cross-population meta-analysis approaches are widely used to identify genetic variants, yet their comparative performance in genomic prediction for complex traits in structured populations remains underexplored. Few studies have directly compared these methods in genomic prediction. To address this gap, this study aims to (i) identify positional candidate genes associated with carcass traits and (ii) evaluate the context-dependent advantages of Covariate Adjustment (CA) and meta in genomic prediction. In this study, we analyzed carcass weight (CW), live weight (LW), and dressing percentage (DP) in 279 crossbred Angus cattle genotyped with the PHR0105_Bt140K_v1.0 SNP chip. GWAS was performed on the full population using BLINK, and results from three subpopulations were combined via meta-analysis, with significance thresholds for both approaches determined by a shuffle-based method. Candidate genes located within ±10 kb of significant SNPs were associated with different carcass traits, including STRIT1, SEL1L3, NOC4L and ANK1 for DP; SNCA and DNAH5 for CW; and GYPC, GPR158, and GUCY1A1 for LW. Prediction accuracy under MAS and MABLUP showed meta slightly outperformed BLINK in MAS, while BLINK was better with covariate adjustment; after incorporating kinship in MABLUP, meta achieved higher accuracy and population partitioning was negligible. Overall, MABLUP yielded the highest accuracy (0.52-0.79) versus MAS (0.37-0.54) in all traits. These findings provide a methodological basis for selecting appropriate GWAS strategies in structured populations and highlight candidate genes.

GS↗

PubMed2026

Bifunctional covalent organic framework for rapid isolation of extracellular vesicles and proteomics-based biomarker discovery.

Extracellular vesicles (EVs), serving as crucial carriers of biomarkers for tumor diagnosis and prognostic evaluation, as well as drug delivery vehicles and therapeutic targets, making it a research hotspot. The isolation methods represent a key aspect of EV-associated research. In this work, an alkynyl-functionalized covalent organic framework (COF) was synthesized under acidic conditions at room temperature and further modified by photo-initiated thiol-yne click reaction, yielding a bifunctionalized COF material decorated with distearoyl phosphatidylethanolamine (DSPE) and Ti4+. This bifunctional COF material (COF-DSPE-Ti) can leverage the bifunctional synergistic effect between DSPE and Ti4+ sites, thereby facilitating the efficient isolation of EVs. This synergistic effect enables the efficient isolation of EVs within 3 min. Proteomic analysis reveals that this isolation method significantly outperforms ultracentrifugation, and an effective EV isolation and analysis can be completed using only 10 μL of plasma sample. For clinical liquid biopsy, the integration of the COF-DSPE-Ti method with proteomics lead to the identification of 64 upregulated proteins in plasma samples from colorectal cancer (CRC) patients, among which S100A9 emerged as a potential EV biomarker. In addition, KLK2, KLK3, and FOLH1, which have been established as diagnostic markers for prostate cancer (PCa), are successfully identified in EVs isolated from the urine of PCa patients. These findings demonstrate the reliability of this approach for screening EV-associated biomarkers and provide a novel strategy for the early diagnosis and prognostic assessment of CRC and PCa.

Proteomics↗