دورية أكاديمية
Diagnosis of neuropsychiatric systemic lupus erythematosus by label-free serum microsphere-coupled SERS fingerprints with machine learning.
| العنوان: | Diagnosis of neuropsychiatric systemic lupus erythematosus by label-free serum microsphere-coupled SERS fingerprints with machine learning. |
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| المؤلفون: | Mi Y; School of Physics and Optoelectronic Engineering, Beijing University of Technology, Beijing, 100124, China., Li X; Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, 100044, China., Zeng X; Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, 100044, China., Cai Y; Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China., Sun X; Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, 100044, China. Electronic address: sunxiaolin_sxl@126.com., Yan Y; 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. Electronic address: yyan@bjut.edu.cn., Jiang Y; 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. |
| المصدر: | Biosensors & bioelectronics [Biosens Bioelectron] 2024 Sep 15; Vol. 260, pp. 116414. Date of Electronic Publication: 2024 May 22. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Advanced Technology Country of Publication: England NLM ID: 9001289 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-4235 (Electronic) Linking ISSN: 09565663 NLM ISO Abbreviation: Biosens Bioelectron Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Oxford : Elsevier Advanced Technology Original Publication: [Barking, Essex, England] : Elsevier Applied Science, 1989- |
| مواضيع طبية MeSH: | Spectrum Analysis, Raman*/methods , Machine Learning* , Microspheres* , Lupus Vasculitis, Central Nervous System*/blood , Lupus Vasculitis, Central Nervous System*/diagnosis, Humans ; Biosensing Techniques/methods ; Metal Nanoparticles/chemistry ; Gold/chemistry ; Neural Networks, Computer ; Lupus Erythematosus, Systemic/blood ; Lupus Erythematosus, Systemic/diagnosis |
| مستخلص: | 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. Competing Interests: Declaration of competing interest The authors declare no competing financial interest. (Copyright © 2024 Elsevier B.V. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Convolutional neural network (CNN); Microsphere-coupled SERS (McSERS); Serum; Surface-enhanced Raman spectroscopy (SERS); Systemic lupus erythematosus |
| المشرفين على المادة: | 7440-57-5 (Gold) |
| تواريخ الأحداث: | Date Created: 20240530 Date Completed: 20240613 Latest Revision: 20240613 |
| رمز التحديث: | 20240614 |
| DOI: | 10.1016/j.bios.2024.116414 |
| PMID: | 38815463 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1873-4235 |
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| DOI: | 10.1016/j.bios.2024.116414 |