Jinyong Lin

Dr. Jinyong Lin


Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

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

Representative Publications

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11 November 2020 in Biomedical Optics Express
Verified
Journal Article
Closed access
PDF file
JLJinyong Lin
Jinyong Lin
ZHZongwei Huang
Zongwei Huang
XLXueliang Lin
Xueliang Lin
QWQiong Wu
Qiong Wu
KQKerun Quan
Kerun Quan
SFShangyuan Feng
Shangyuan Feng
The concept of being able to urinate in a cup and screen for colorectal cancer (CRC) is fascinating to the public at large. Here, a simple and label-free urine test based on surface-enhanced Raman spectroscopy (SERS) was employed for CRC detection. Significant spectral differences among normal, stages I-II, and stages III-IV CRC urines were observed. Using discriminant function analysis, the diagnostic sensitivities of 95.8%, 80.9%, and 84.3% for classification of normal, stages I-II, and stages III-IV CRC were achieved in training model, indicating the great promise of urine SERS as a rapid, convenient and noninvasive method for CRC staging detection.

8 August 2022 in Nanomaterials
Verified
Journal Article
Open access
PDF file
JLJinyong Lin
Jinyong Lin
YWYouliang Weng
Youliang Weng
XLXueliang Lin
Xueliang Lin
SQSufang Qiu
Sufang Qiu
ZHZufang Huang
Zufang Huang
SFShangyuan Feng
Shangyuan Feng
Early screening and precise staging are crucial for reducing mortality in patients with nasopharyngeal carcinoma (NPC). This study aimed to assess the performance of blood protein surface-enhanced Raman scattering (SERS) spectroscopy, combined with deep learning, for the precise detection of NPC. A highly efficient protein SERS analysis, based on a membrane purification technique and super-hydrophobic platform, was developed and applied to blood samples from 1164 subjects, including 225 healthy volunteers, 120 stage I, 249 stage II, 291 stage III, and 279 stage IV NPC patients. The proteins were rapidly purified from only 10 µL of blood plasma using the membrane purification technique. Then, the super-hydrophobic platform was prepared to pre-concentrate tiny amounts of proteins by forming a uniform deposition to provide repeatable SERS spectra. A total of 1164 high-quality protein SERS spectra were rapidly collected using a self-developed macro-Raman system. A convolutional neural network-based deep-learning algorithm was used to classify the spectra. An accuracy of 100% was achieved for distinguishing between the healthy and NPC groups, and accuracies of 96%, 96%, 100%, and 100% were found for the differential classification among the four NPC stages. This study demonstrated the great promise of SERS- and deep-learning-based blood protein testing for rapid, non-invasive, and precise screening and staging of NPC.


28 June 2024 in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
Verified
Journal Article
Closed access
WCWeiwei Chen
Weiwei Chen
SWShenghe Weng
Shenghe Weng
WZWeixiong Zhong
Weixiong Zhong
HHHao Huang
Hao Huang
GWGuoqiang Wei
Guoqiang Wei
YYYun Yu
Yun Yu