標題: Intelligent classification of platelet aggregates by agonist type
作者: Zhou, Yuqi
Yasumoto, Atsushi
Lei, Cheng
Huang, Chun-Jung
Kobayashi, Hirofumi
Wu, Yunzhao
Yan, Sheng
Sun, Chia-Wei
Yatomi, Yutaka
Goda, Keisuke
光電工程學系
Department of Photonics
公開日期: 12-May-2020
摘要: Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.
URI: http://dx.doi.org/10.7554/eLife.52938
http://hdl.handle.net/11536/154562
ISSN: 2050-084X
DOI: 10.7554/eLife.52938
期刊: ELIFE
Volume: 9
起始頁: 0
結束頁: 0
Appears in Collections:Articles