Shuang Li

Dr. Shuang Li

Medical College, Tianjin University, Tianjin, China
orcid

Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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5 March 2020 in Sensors
Verified
Journal Article
Open access
PDF file
Jingjing LiuJingjing Liu
Jingjing Liu
NXNing Xu
Ning Xu
HMHong Men
Hong Men
Shuang LiShuang Li
Shuang Li
Yanli LuYanli Lu
Yanli Lu
Qingjun LiuQingjun Liu
Qingjun Liu
Cortisol is commonly used as a significant biomarker of psychological or physical stress. With the accelerated pace of life, non-invasive cortisol detection at the point of care (POC) is in high demand for personal health monitoring. In this paper, an ultrasensitive immunosensor using gold nanoparticles/molybdenum disulfide/gold nanoparticles (AuNPs/MoS2/AuNPs) as transducer was explored for non-invasive salivary cortisol monitoring at POC with the miniaturized differential pulse voltammetry (DPV) system based on a smartphone. Covalent binding of cortisol antibody (CORT-Ab) onto the AuNPs/MoS2/AuNPs transducer was achieved through the self-assembled monolayer of specially designed polyethylene glycol (PEG, SH-PEG-COOH). Non-specific binding was avoided by passivating the surface with ethanolamine. The miniaturized portable DPV system was utilized for human salivary cortisol detection. A series current response of different cortisol concentrations decreased and exhibited a linear range of 0.5–200 nM, the detection limit of 0.11 nM, and high sensitivity of 30 μA M−1 with a regression coefficient of 0.9947. Cortisol was also distinguished successfully from the other substances in saliva. The recovery ratio of spiked human salivary cortisol and the variation of salivary cortisol level during one day indicated the practicability of the immunosensor based on the portable system. The results demonstrated the excellent performance of the smartphone-based immunosensor system and its great potential application for non-invasive human salivary cortisol detection at POC.

5 July 2024 in ACS Sensors
Verified
Journal Article
Closed access
ZLZhuo Liu
Zhuo Liu
HLHe Lv
He Lv
YZYuchi Zhang
Yuchi Zhang
JHJian Wang He
Jian Wang He
LHLe Han
Le Han
YXYan Xu
Yan Xu

29 June 2021 in ACS Applied Bio Materials
Verified
Journal Article
Closed access
Shuang LiShuang Li
Shuang Li
CHChang Hu
Chang Hu
CCChong Chen
Chong Chen
JZJiawei Zhang
Jiawei Zhang
YBYongchang Bai
Yongchang Bai
DMDong Ming
Dong Ming

13 August 2023 in Biosensors
Verified
Journal Article
Open access
PDF file
JFJie Fu
Jie Fu
QGQiya Gao
Qiya Gao
Shuang LiShuang Li
Shuang Li
With the popularization of intelligent sensing and the improvement of modern medical technology, intelligent medical sensing technology has emerged as the times require. This technology combines basic disciplines such as physics, mathematics, and materials with modern technologies such as semiconductors, integrated circuits, and artificial intelligence, and has become one of the most promising in the medical field. The core of intelligent medical sensor technology is to make existing medical sensors intelligent, portable, and wearable with full consideration of ergonomics and sensor power consumption issues in order to conform to the current trends in cloud medicine, personalized medicine, and health monitoring. With the development of automation and intelligence in measurement and control systems, it is required that sensors have high accuracy, reliability, and stability, as well as certain data processing capabilities, self-checking, self-calibration, and self-compensation, while traditional medical sensors cannot meet such requirements. In addition, to manufacture high-performance sensors, it is also difficult to improve the material process alone, and it is necessary to combine computer technology with sensor technology to make up for its performance shortcomings. Intelligent medical sensing technology combines medical sensors with microprocessors to produce powerful intelligent medical sensors. Based on the original sensor functions, intelligent medical sensors also have functions such as self-compensation, self-calibration, self-diagnosis, numerical processing, two-way communication, information storage, and digital output. This review focuses on the application of intelligent medical sensing technology in biomedical sensing detection from three aspects: physical sensor, chemical sensor, and biosensor.

3 January 2022 in Journal of Alloys and Compounds
Verified
Journal Article
Closed access
JWJingyi Wang
Jingyi Wang
LLLiangwen Liu
Liangwen Liu
MYMei Yang
Mei Yang
XWXin Wu
Xin Wu
Shuang LiShuang Li
Shuang Li
HZHao Zhang
Hao Zhang

1 January 2024 in Interdisciplinary Medicine
Verified
Journal Article
Open access
QGQiya Gao
Qiya Gao
Shuang LiShuang Li
Shuang Li
Intelligent point‐of‐care testing (iPOCT) is a rapidly advancing technology that combines molecular detection and bio‐sensing techniques to achieve instant and accurate analysis through advanced sensors and intelligent algorithms. Molecular detection technologies, such as nucleic acid isothermal amplification, biochip and microfluidics, enable rapid analysis of the chemical composition and concentration of samples, thereby determining their presence. Bio‐sensing technology utilizes the recognition properties of biomolecules in conjunction with sensors to detect specific biomolecules or biological processes. iPOCT technology holds broad prospects for application in the field of healthcare and is expected to enhance detection sensitivity, accuracy, and intelligence through further development. In this review, we delineate the developmental journey of iPOCT, followed by a discussion on the principles of molecular detection and bio‐sensing technology, as well as the design of iPOCT devices for health monitoring and disease diagnosis. Furthermore, we showcased illustrative examples of iPOCT applications in disease diagnosis, highlighting the integration of iPOCT technology with wearable devices and smartphones for attaining more precise and personalized diagnostic results.