Image-based motion artifact reduction on liver dynamic contrast enhanced MRI

被引:0
|
作者
Yu, Zhichao [1 ]
Lin, Qianyun [1 ]
Gong, Hexiang [1 ]
Li, Meijin [1 ]
Tang, Dianping [1 ]
机构
[1] Fuzhou Univ, MOE & Fujian Prov, Key Lab Analyt Sci Food Safety & Biol, Dept Chem, Fuzhou 350108, Peoples R China
来源
关键词
Self -powered photoelectrochemical; immunoassay; Artificial neural network; Micro-electro-mechanical system; Cardiac troponin I; Bi 2 O 2 S nanosheets; CONVERSION;
D O I
10.1016/j.bios.2022.115028
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
Considering the fact that acute myocardial infarction has shown a trend towards younger age and has become a major health problem, it is necessary to develop rapid screening devices to meet the needs of community health care. Herein, we developed an artificial neural network-assisted solar-powered photoelectrochemical (SP-PEC) sensing platform for rapid screening of cardiac troponin I (cTnI) protein in the prognosis of patients with acute myocardial infarction (AMI) by integrating a self-powered photoelectric signal output system with low-cost screen-printed paper electrodes functionalized with ultrathin Bi2O2S (BOS) nanosheets. An integrated solarpowered PEC immunoassay with micro-electro-mechanical system (MEMS) was constructed without an excitation light source. The quantification of cTnI protein was obtained by the electrical signal changes caused by the electro-oxidation process of H2O2, generated by the classical split immune reaction, on the electrode surface. The test electrodes were developed as dual working electrodes, one for target cTnI testing and the other for evaluating light intensity, to reduce the temporal inconsistency of sunlight. The photoelectrodes were discovered to exhibit satisfactory negative response to target concentrations in the dynamic range of 2.0 pg mL-1-10 ng mL-1 since being regressed in an improved artificial neural network (ANN) model using the pooled dataset of target signals affected by the light source. The difference of hot electron and hole transfer behavior in different thickness of nano-materials was determined by finite element analysis (FEA), which provided a theoretical basis for the development of efficient PEC sensors. This work presents a unique perspective for the design of a revolutionary low-cost bioassay platform by inventively illuminating the PEC biosensor's component process without the use of light.
引用
收藏
页数:9
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