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Mi Y, Li X, Zeng X, Cai Y, Sun X, Yan Y, Jiang Y. Diagnosis of neuropsychiatric systemic lupus erythematosus by label-free serum microsphere-coupled SERS fingerprints with machine learning. Biosens Bioelectron 2024; 260:116414. [PMID: 38815463 DOI: 10.1016/j.bios.2024.116414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/08/2024] [Accepted: 05/20/2024] [Indexed: 06/01/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a powerful optical technique for non-invasive and label-free bioanalysis of liquid biopsy, facilitating to diagnosis of potential diseases. Neuropsychiatric systemic lupus erythematosus (NPSLE) is one of the subgroups of systemic lupus erythematosus (SLE) with serious manifestations for a high mortality rate. Unfortunately, lack of well-established gold standards results in the clinical diagnosis of NPSLE being a challenge so far. Here we develop a novel Raman fingerprinting machine learning (ML-) assisted diagnostic method. The microsphere-coupled SERS (McSERS) substrates are employed to acquire Raman spectra for analysis via convolutional neural network (CNN). The McSERS substrates demonstrate better performance to distinguish the Raman spectra from serums between SLE and NPSLE, attributed to the boosted signal-to-noise ratio of Raman intensities due to the multiple optical regulation in microspheres and AuNPs. Eight statistically-significant (p-value <0.05) Raman shifts are identified, for the first time, as the characteristic spectral markers. The classification model established by CNN algorithm demonstrates 95.0% in accuracy, 95.9% in sensitivity, and 93.5% in specificity for NPSLE diagnosis. The present work paves a new way achieving clinical label-free serum diagnosis of rheumatic diseases by enhanced Raman fingerprints with machine learning.
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Affiliation(s)
- Yanlin Mi
- School of Physics and Optoelectronic Engineering, Beijing University of Technology, Beijing, 100124, China
| | - Xue Li
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, 100044, China
| | - Xingyue Zeng
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, 100044, China
| | - Yuyang Cai
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Xiaolin Sun
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, 100044, China.
| | - Yinzhou Yan
- School of Physics and Optoelectronic Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Trans-scale Laser Manufacturing Technology (Beijing University of Technology), Ministry of Education, Beijing, 100124, China; Beijing Engineering Research Center of Laser Technology, Beijing University of Technology, Beijing, 100124, China.
| | - Yijian Jiang
- School of Physics and Optoelectronic Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Trans-scale Laser Manufacturing Technology (Beijing University of Technology), Ministry of Education, Beijing, 100124, China; Beijing Engineering Research Center of Laser Technology, Beijing University of Technology, Beijing, 100124, China
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Wu X, Shuai W, Chen C, Chen X, Luo C, Chen Y, Shi Y, Li Z, Lv X, Chen C, Meng X, Lei X, Wu L. Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms. Front Immunol 2023; 14:1328228. [PMID: 38162641 PMCID: PMC10754999 DOI: 10.3389/fimmu.2023.1328228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
Introduce Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and osteoarthritis (OA) are three rheumatic immune diseases with many common characteristics. If left untreated, they can lead to joint destruction and functional limitation, and in severe cases, they can cause lifelong disability and even death. Studies have shown that early diagnosis and treatment are key to improving patient outcomes. Therefore, a rapid and accurate method for rapid diagnosis of diseases has been established, which is of great clinical significance for realizing early diagnosis of diseases and improving patient prognosis. Methods This study was based on Fourier transform infrared spectroscopy (FTIR) combined with a deep learning model to achieve non-invasive, rapid, and accurate differentiation of AS, RA, OA, and healthy control group. In the experiment, 320 serum samples were collected, 80 in each group. AlexNet, ResNet, MSCNN, and MSResNet diagnostic models were established by using a machine learning algorithm. Result The range of spectral wave number measured by four sets of Fourier transform infrared spectroscopy is 700-4000 cm-1. Serum spectral characteristic peaks were mainly at 1641 cm-1(amide I), 1542 cm-1(amide II), 3280 cm-1(amide A), 1420 cm-1(proline and tryptophan), 1245 cm-1(amide III), 1078 cm-1(carbohydrate region). And 2940 cm-1 (mainly fatty acids and cholesterol). At the same time, AlexNet, ResNet, MSCNN, and MSResNet diagnostic models are established by using machine learning algorithms. The multi-scale MSResNet classification model combined with residual blocks can use convolution modules of different scales to extract different scale features and use resblocks to solve the problem of network degradation, reduce the interference of spectral measurement noise, and enhance the generalization ability of the network model. By comparing the experimental results of the other three models AlexNet, ResNet, and MSCNN, it is found that the MSResNet model has the best diagnostic performance and the accuracy rate is 0.87. Conclusion The results prove the feasibility of serum Fourier transform infrared spectroscopy combined with a deep learning algorithm to distinguish AS, RA, OA, and healthy control group, which can be used as an effective auxiliary diagnostic method for these rheumatic immune diseases.
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Affiliation(s)
- Xue Wu
- Department of Rheumatology and Immunology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Graduate School of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Wei Shuai
- College of Software, Xinjiang University, Urumqi, Xinjiang, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China
| | - Xiaomei Chen
- Department of Rheumatology and Immunology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Graduate School of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Cainan Luo
- Department of Rheumatology and Immunology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Graduate School of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Yi Chen
- Department of Rheumatology and Immunology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Graduate School of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Yamei Shi
- Department of Rheumatology and Immunology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Graduate School of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Zhengfang Li
- Department of Rheumatology and Immunology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Graduate School of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, Xinjiang, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, Xinjiang, China
| | - Xinyan Meng
- Department of Rheumatology and Immunology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Graduate School of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xin Lei
- Department of Rheumatology and Immunology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Graduate School of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Lijun Wu
- Department of Rheumatology and Immunology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Graduate School of Xinjiang Medical University, Urumqi, Xinjiang, China
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