免疫生物标志物的诊断价值在患有喘支气管炎和喘的儿童中
Ming Wu1, Danru Liu1, Fenhua Zhu1
1Department of Clinical Laboratory, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China.
Medicina (Kaunas, Lithuania)
|October 28, 2023
概括
这项研究确定了关键的免疫细胞差异,以创建一个机器学习模型,以区分儿童的喘支气管炎和喘. 这有助于早期诊断和监测疾病进展.
科学领域:
- 儿科免疫学 儿科免疫学
- 计算生物学是一种计算生物学.
- 呼吸系统药物 呼吸系统药物
背景情况:
- 喘支气管炎和喘是常见的儿科呼吸道疾病,症状重叠.
- 准确的差异诊断对于有效的治疗和管理至关重要.
- 当前的诊断方法可能并不总能提供快速或最终的差异化.
研究的目的:
- 评估免疫生物标志物的诊断价值,以区分儿童的喘支气管炎和喘.
- 开发和验证用于快速差异诊断的机器学习 (ML) 模型.
- 了解与每个疾病相关的免疫失调模式.
主要方法:
- 在外周血液中检测免疫生物标志物,使用流细胞计和免疫突变计.
- 应用随机森林和额外树木算法来进行特征选择和重要性排名.
- 使用Skit-learn开发和评估ML模型,使用K-fold交叉验证和Brier分数.
主要成果:
- 在患有喘支气管炎和喘的儿童中观察到幽默和细胞免疫反应的独特模式.
- CD8+ T 细胞和B 细胞成为主要的免疫生物标志物.
- 随机森林模型在学习和训练免疫生物标记数据集方面表现出很高的表现.
结论:
- 一个预测性ML模型已成功开发用于喘支气管炎和喘的早期差异诊断.
- 免疫生物标志物评估提供了有价值的临床见解,特别是对于在疾病之间过渡的儿童.
- 这种方法可以支持临床决策和个性化治疗策略.
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