Simplified flow cytometry-based assay for rapid multi-cytokine profiling and machine-learning-assisted diagnosis of inflammatory diseases

一种简化的基于流式细胞术的检测方法,可用于快速多细胞因子分析和机器学习辅助的炎症性疾病诊断。

阅读:1

Abstract

INTRODUCTION: Multiple cytokines detection represents a more robust way to predict the disease progression than a single cytokine, and flow cytometry (FCM)-based assays are increasingly used worldwide for multiple cytokines profile. METHODS: Inspired by One-step concept of ELISA technology, here we reported the development of one-step FCM-based 12-plex cytokine assay to reduce operation and reaction times, in which all the reagents (including capture-antibody-modified beads and phycoerythrin-labeled detection antibodies) had mixed in the same reaction system and achieved similar performance to the conventional approach. Moreover, we used the lyophilization technique to remove the need for cold storage of reagents to further simplify the assay procedure. RESULTS: We leveraged our technology to test clinical serum samples from patients with COVID-19 or HBV infectious diseases, and established supervised or unsupervised machine learning models to predict the severity or viral load and get deeper insights into the diseases. DISCUSSION: Together, our results demonstrate a general and framework for convenient analysis of cytokine panel and have the potential to influence medical research and application in this field.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。