Publications by authors named "P Xien Chen"

In this study, spatial and single-cell transcriptome techniques were used to investigate the role of beta-galactoside alpha-2,6-sialyltransferase 1 (ST6GAL1) in promoting peritoneal metastasis in ovarian cancer epithelial cells. We collected single-cell transcriptomic (GSE130000) and spatial transcriptomic datasets (GSE211956) from the Gene Expression Omnibus and RNA-sequencing data from The Cancer Genome Atlas. The Robust Cell Type Decomposition (RCTD) approach was implemented to integrate spatial and single-cell transcriptomic data.

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Single photon emission computed tomography (SPECT), a technique capable of capturing functional and molecular information, has been widely adopted in theranostics applications across various fields, including cardiology, neurology, and oncology. The spatial resolution of SPECT imaging is relatively poor, which poses a significant limitation, especially the visualization of small lesions. The main factors affecting the limited spatial resolution of SPECT include projection sampling techniques, hardware and software.

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Purpose: To analyze the clinical data of five patients involving intravesical magnetic beads, summarizing diagnostic and therapeutic experiences.

Methods: From January 2018 to November 2023, five pediatric patients were treated for intravesical magnetic beads at Shenzhen Children's Hospital. We retrospectively reviewed and analyzed the records of these patients, including demographic characteristics, clinical symptoms, imaging studies, and treatment methods.

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Background: Closed head injury (CHI) provokes a prominent neuroinflammation that may lead to long-term health consequences. Microglia plays pivotal and complex roles in neuroinflammation-mediated neuronal insult and repair following CHI. We previously reported that induced neural stem cells (iNSCs) can block the effects of CXCL12/CXCR4 signaling on NF-κB activation in activated microglia by CXCR4 overexpression.

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Background: Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developmental processes, and disease mechanisms. As scRNA-seq technology advances, its role in modern biology has become increasingly vital. This study explores the application of deep learning to single-cell data clustering, with a particular focus on managing sparse, high-dimensional data.

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