Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Yingyan Gu"'
Publikováno v:
Cell Division, Vol 19, Iss 1, Pp 1-10 (2024)
Abstract Background The precise mechanisms underlying preeclampsia (PE) pathogenesis remain unclear. Mesenchymal stem cells (MSCs) are involved in the pathology of PE. The aim of our study was to identify the effects of protein phosphatase 2 regulato
Externí odkaz:
https://doaj.org/article/53a4ef15b8c94f5b887665010af89a5c
Autor:
Xiaowei Cao, Shengjie Ge, Weiwei Hua, Xinyu Zhou, Wenbo Lu, Yingyan Gu, Zhiyue Li, Yayun Qian
Publikováno v:
Journal of Nanobiotechnology, Vol 20, Iss 1, Pp 1-13 (2022)
Abstract Circulating tumour DNA (ctDNA) has emerged as an ideal biomarker for the early diagnosis and prognosis of gastric cancer (GC). In this work, a pump-free, high-throughput microfluidic chip coupled with catalytic hairpin assembly (CHA) and hyb
Externí odkaz:
https://doaj.org/article/04c22cbad721443283b259f342e36985
Publikováno v:
Analytical and Bioanalytical Chemistry. 414:7659-7673
Publikováno v:
Materials Chemistry Frontiers. 6:1331-1343
A novel SERS biosensor based on cascade signal amplification of CHA-HCR for ultrasensitive detection of HPV-E7 and OPN was developed in this work.
Publikováno v:
New Journal of Chemistry. 46:20629-20642
In this work, a novel surface-enhanced Raman scattering and lateral flow assay (SERS–LFA) biosensor with multiple channels based on an aptamer has been proposed.
Publikováno v:
Journal of materials chemistry. B. 10(32)
Circulating tumor DNA (ctDNA) is an ideal biomarker for cancer diagnosis based on liquid biopsy, so there is an urgent need for developing an efficient, rapid, and ultrasensitive detection method to meet clinical needs. In this paper, a novel surface
Publikováno v:
SSRN Electronic Journal.
Autor:
Yuexing Gu, Dawei Cao, Yu Mao, Shengjie Ge, Zhiyue Li, Yingyan Gu, Yue Sun, Li Li, Xiaowei Cao
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
Sensors and Actuators B: Chemical. 375:132894
Publikováno v:
Computers in Biology and Medicine. 148:105693
In this paper, we propose a novel U-Net with fully connected residual blocks (FCRB U-Net) for the fetal cerebellum Ultrasound image segmentation task. FCRB U-Net, an improved convolutional neural network (CNN) based on U-Net, replaces the double conv