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相关概念视频

Structure and Function of Leukocytes01:21

Structure and Function of Leukocytes

An adult in good health typically has between 4,500 and 11,000 leukocytes, or white blood cells, per microliter of blood, which constitutes about 1% of the total blood volume. Unlike red blood cells, white blood cells contain a nucleus and other cellular organelles but do not have hemoglobin. Most white blood cells reside in connective tissues, particularly in lymphatic organs such as the lymph nodes, with only a small fraction present in circulating blood.
White blood cells protect the body...

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High Throughput Sequential ELISA for Validation of Biomarkers of Acute Graft-Versus-Host Disease
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在巨细胞动脉炎中血蛋白质组分析.

Kevin Y Cunningham1, Benjamin Hur2, Vinod K Gupta2

  • 1Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, Minnesota, USA.

Annals of the rheumatic diseases
|August 17, 2024
PubMed
概括

血蛋白质组签名可以区分巨细胞动脉炎 (GCA) 和对照细胞. 机器学习集成显示了发现多重生物标志物用于GCA诊断和管理的前景.

关键词:
巨细胞动脉炎的发生机器学习 机器学习血管炎是一种血管炎.

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科学领域:

  • 免疫学 免疫学 免疫学
  • 蛋白质组学是指蛋白质组学.
  • 血管炎症 血管炎症

背景情况:

  • 巨细胞动脉炎 (GCA) 是一种系统性血管炎,影响大动脉.
  • 准确的诊断和疾病活动的监测对于有效的GCA管理至关重要.
  • 确定可靠的GCA生物标志物仍然是一个未满足的临床需求.

研究的目的:

  • 为了识别血蛋白质组特征,将活性和非活性GCA与非疾病对照区分开来.
  • 在GCA中发现与疾病活动相关的蛋白质.
  • 评估血蛋白质组分析对GCA生物标志物发现的潜力.

主要方法:

  • 对30名GCA患者 (活跃和不活跃的疾病) 和30名对照者的前性纵向研究.
  • 基于高通量aptamer的蛋白质组学试验,分析了超过7000种蛋白质特征.
  • 机器学习模型 (随机森林) 应用于血蛋白质组数据.

主要成果:

  • 在活性GCA/对照和非活性GCA/对照之间分别发现了537和781种不同丰富的蛋白质.
  • 16与活跃的GCA疾病相关的蛋白质.
  • 机器学习模型准确地将GCA患者与对照患者区分开来 (准确率为95.0-98.3%).
  • 仅仅是血蛋白质在患者内有有限的能力来区分活性与非活性GCA.

结论:

  • 血蛋白质组签名为GCA诊断提供了潜在的可能性.
  • 机器学习与蛋白质组数据的整合显示了GCA中多重生物标志物发现的前景.
  • 需要进一步的研究来完善用于区分疾病状态的生物标志物.