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通过多重电化学发光免疫阵列与强大的机器学习相结合,为急性心肌梗塞的早期和准确诊断提供三种生物标志物联合策略

Mingquan Guo¹, Dexin Du¹, Jue Wang¹

¹Key Laboratory of Precision and Intelligent Chemistry, iChEM (Collaborative Innovation Center of Chemistry for Energy Materials), Department of Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China.

Chemical & biomedical imaging

|October 30, 2024

概括

使用电化学发光 (ECL) 免疫阵列和机器学习的新三种生物标志物策略,准确诊断急性心肌梗塞 (AMI). 这种方法优于标准的心脏热素T (cTnT) 测试,特别是在早期诊断中,改善了患者的结果.

科学领域:

生物医学工程生物医学工程
心血管诊断心血管诊断服务
纳米技术纳米技术

背景情况:

急性心肌梗塞 (AMI) 是全球主要的死亡原因,需要快速诊断才能有效治疗.
目前的诊断标准,如心脏托罗邦素T (cTnT),在早期和准确的AMI检测方面存在局限性.
及时干预,最好是在症状出现后3小时内,可显著改善患者的康复并降低死亡率.

研究的目的:

开发和验证一种新的三种生物标志物诊断策略,用于早期和准确地检测AMI.
与现有方法相比,提高诊断准确度,特别是高灵敏性心脏托邦素T (hs-cTnT).
利用电化学发光 (ECL) 免疫阵列和机器学习等先进技术,以改善临床决策.

主要方法:

使用微芯片上的ciGold纳米组件开发的多重ECL免疫阵列,用于同时检测心脏素I,心脏类型脂肪酸结合蛋白和copeptin.
应用机器学习模型 (支持载体机器) 来根据来自260个血清样本的综合生物标记数据对患者进行分类.
对hs-cTnT的三种生物标志物策略进行比较分析,评估症状出现后不同时间点的表现.

主要成果:

三种生物标志物联合评估模型实现了AMI诊断的完美歧视 (100%的灵敏度和特异性).
这种新策略的准确性明显高于单独的cTnT,特别是在症状出现后的关键早期小时内.
虽然hs-cTnT在12小时内对20%以上的患者进行了错误分类,但关节生物标志物模型保持了完美的准确性,随着测试时间的减少,性能得到改善.

结论:

与hs-cTnT相比,拟议的三种生物标志物的联合战略为AMI的早期和准确诊断提供了一种优越的方法.
这种创新的诊断工具有可能显著降低AMI死亡率.
多重ECL免疫阵列和机器学习的集成为改善心血管诊断和优化医疗资源分配提供了一个强大的平台.

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