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外周血免疫图谱及NXPE3作为高血压性脑出血风险预测和靶向治疗的新型生物标志物

Peripheral blood immune landscape and NXPE3 as a novel biomarker for hypertensive intracerebral hemorrhage risk prediction and targeted therapy.

作者信息

Zhang Meng, Ning Jingyuan, Liu Jing, Sun Yingying, Xiao Ning, Xu Haochen, Chen Jingzhou

机构信息

State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China.

State Key Laboratory of Common Mechanism Research for Major Diseases & Department of Medical Genetics, Institute of Basic Medical Sciences & School of Basic Medicine Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China.

出版信息

Imeta. 2025 Apr 11;4(3):e70030. doi: 10.1002/imt2.70030. eCollection 2025 Jun.

DOI:10.1002/imt2.70030
PMID:40469509
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12130563/
Abstract

We employed bulk RNA-seq and scRNA-seq techniques to analyze the immune dysregulation in patients with intracerebral hemorrhage (ICH). The study revealed that excessive inflammatory responses, neutrophil activation, and T-cell dysfunction are the main characteristics of ICH. A multi-machine learning framework was utilized to construct a predictive model for ICH risk in hypertensive patients. Molecular docking and simulation results demonstrated that NXPE3 is a potential therapeutic target, and dihydroergotamine (DHE) exhibits a strong binding affinity to NXPE3. In vivo experiments indicated that DHE can reduce the incidence of spontaneous ICH and modulate the immune-inflammatory response. These results support DHE targeting NXPE3 as a potential therapeutic strategy for hypertension-related ICH.

摘要

我们采用批量RNA测序和单细胞RNA测序技术来分析脑出血(ICH)患者的免疫失调情况。研究表明,过度的炎症反应、中性粒细胞活化和T细胞功能障碍是ICH的主要特征。利用多机器学习框架构建了高血压患者ICH风险的预测模型。分子对接和模拟结果表明,NXPE3是一个潜在的治疗靶点,双氢麦角胺(DHE)对NXPE3表现出很强的结合亲和力。体内实验表明,DHE可降低自发性ICH的发生率并调节免疫炎症反应。这些结果支持将靶向NXPE3的DHE作为高血压相关性ICH的潜在治疗策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7382/12130563/5ab2677dd2a9/IMT2-4-e70030-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7382/12130563/da3b8c4361bd/IMT2-4-e70030-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7382/12130563/5ab2677dd2a9/IMT2-4-e70030-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7382/12130563/da3b8c4361bd/IMT2-4-e70030-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7382/12130563/5ab2677dd2a9/IMT2-4-e70030-g002.jpg

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Machine learning algorithms integrate bulk and single-cell RNA data to unveil oxidative stress following intracerebral hemorrhage.机器学习算法整合批量和单细胞 RNA 数据,揭示脑出血后的氧化应激。
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LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features.LORIS 使用常见的临床、病理和基因组特征,稳健地预测了接受免疫检查点阻断治疗的患者的预后。
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