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Yan Zhong

Publications and source records attributed to Yan Zhong.

2 recordsLinked to original sources

Serum N-glycomics for non-invasive detection of significant liver pathology across clinical phases of treatment-naïve chronic hepatitis B.

BACKGROUND: Early identification of significant liver pathology is crucial for timely antiviral intervention in individuals with chronic hepatitis B (CHB) infection. Current non-invasive methods show limited accuracy in detecting occult liver damage, particularly in those with normal ALT. This study evaluated serum N-glycan profiles for diagnosing significant liver pathology in treatment-na&#xef;ve CHB patients across clinical phases. METHODS: This cross-sectional study analyzed 626 treatment-na&#xef;ve CHB patients confirmed by liver biopsy, classified according to 2025 EASL guidelines. Serum N-glycan profiles were determined using DNA sequencer-assisted fluorophore-assisted carbohydrate electrophoresis. Significant liver pathology was defined as inflammation grade&#x2009;&#x2265;&#x2009;G2 and/or fibrosis stage&#x2009;&#x2265;&#x2009;S2 (per Scheuer scoring system). Multivariate logistic regression models were developed and compared with traditional non-invasive markers. RESULTS: Among 626 CHB patients, 66.0% had significant inflammation and 58.9% had significant fibrosis. Patients with significant pathology showed characteristic alterations, with elevated P1, P3, P6, P7, P11 peaks and decreased P0, P5, P8, P10 peaks (all p&#x2009;<&#x2009;0.0001). Compared to respective infection phases, hepatitis phases showed P1 increases of 19.6% and 36% in HBeAg(+) and HBeAg(-) patients, with P11 increases of 82.4% and 73.4%, while P0 decreased by 20.3% and 27.6%, and P10 by 21.6% and 20.3%. Relative to mild pathology (G and S&#x2009;<&#x2009;2), P1 increased by 27% in significant pathology (G and/or S&#x2009;&#x2265;&#x2009;2), reaching 58.7%/48.7% in G4/S4 stages (vs. G0/S0). In ALT-normal HBeAg(+) infection phase, P1 increased by 80.2%/65.8% in G4/S4 stages (vs. G0/S0), with P2 also increasing by 54.1%/45.2%. Multivariate analysis identified P11 as strongest risk factor (OR&#x2009;=&#x2009;3.84, 95%CI: 1.74-8.45, p&#x2009;=&#x2009;0.0008), followed by P1 (OR&#x2009;=&#x2009;2.04, 95%CI: 1.57-2.64, p&#x2009;<&#x2009;0.0001) and P7 (OR&#x2009;=&#x2009;1.75, 95%CI: 1.31-2.34, p&#x2009;=&#x2009;0.0002), while P2 (OR&#x2009;=&#x2009;0.07, 95%CI: 0.02-0.26, p&#x2009;<&#x2009;0.0001) and P0 (OR&#x2009;=&#x2009;0.30, 95%CI: 0.12-0.79, p&#x2009;=&#x2009;0.0140) served as protective factors. The glycomics combined model (AUC&#x2009;=&#x2009;0.876 (0.844-0.908)) achieved superior performance and outperformed the clinical model (AUC&#x2009;=&#x2009;0.818 (0.779-0.857)), LSM (AUC&#x2009;=&#x2009;0.817 (0.775-0.858)), APRI (AUC&#x2009;=&#x2009;0.830 (0.792-0.867)), and FIB-4 (AUC&#x2009;=&#x2009;0.672 (0.621-0.723)) (all p&#x2009;<&#x2009;0.001), with 78.7% sensitivity and 83.2% specificity. The optimized model reached AUC&#x2009;=&#x2009;0.917 (0.891-0.942) with accuracy 84.2%, with 78.7% sensitivity and 94.6% specificity. Both glycomics-based models maintained diagnostic capability in ALT-normal patients particularly in HBeAg(+) infection. CONCLUSIONS: Serum N-glycomics demonstrates promising potential for non-invasive identification of significant liver pathology in treatment-na&#xef;ve CHB patients, providing an alternative approach for early treatment decisions, especially in ALT-normal patients with occult liver damage.

Humans

scPOEM: robust co-embedding of peaks and genes revealing peak-gene regulation.

MOTIVATION: Identifying regulatory elements in various chromosomal regions that influence gene expression is a fundamental challenge in epigenomics, with profound implications for understanding gene regulation and disease mechanisms. The advent of paired single-cell RNA sequencing and single-cell ATAC sequencing has created unprecedented opportunities to address this challenge by enabling simultaneous profiling of gene expression and chromatin accessibility at single-cell resolution. However, the inherent signals between them are weak due to the highly sparse and noisy nature of data. RESULTS: This article proposes single-cell meta-Path based Omics Embedding (scPOEM), a novel embedding method that jointly projects chromatin accessibility peaks and expressed genes into a shared low-dimensional space. By integrating the relationships among peak-peak, peak-gene, and gene-gene interactions, scPOEM assigns closer representations in the embedding space to related peak-gene pairs. Our experiments demonstrate that scPOEM generates stable representations of peaks and genes, outperforms existing methods in recovering biologically meaningful peak-gene regulatory relationships and enables new insights in subgroup and differential analysis of gene regulation. These results highlight its potential to uncover gene regulatory mechanisms and enhance the understanding of transcriptional regulation at single-cell resolution. AVAILABILITY AND IMPLEMENTATION: The source code of scPOEM is available at https://github.com/Houyt23/scPOEM. The datasets can be obtained from the 10&#xd7; Genomics (https://www.10xgenomics.com/datasets/pbmc-from-a-healthy-donor-granulocytes-removed-through-cell-sorting-10-k-1-standard-1-0-0) and GEO database under access codes GSE194122 and GSE239916.

Gene Expression Regulation