{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T20:42:32Z","timestamp":1776199352650,"version":"3.50.1"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>This study aims to examine the impact of systemic lupus erythematosus (SLE) on various organs and tissues throughout the body. SLE is a chronic autoimmune disease that, if left untreated, can lead to irreversible damage to these organs. In severe cases, it can even be life-threatening. It has been demonstrated that prompt diagnosis and treatment are crucial for improving patient outcomes. However, applying spectral data in the classification and activity assessment of SLE reveals a high degree of spectral overlap and significant challenges in feature extraction. Consequently, this paper presents a rapid and accurate method for disease diagnosis and activity assessment, which has significant clinical implications for achieving early diagnosis of the disease and improving patient prognosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, a two-branch Bayesian network (DBayesNet) based on Raman spectroscopy was developed for the rapid identification of SLE. Serum Raman spectra samples were collected from 80 patients with SLE and 81 controls, including those with dry syndrome, undifferentiated connective tissue disease, aortitis, and healthy individuals. Following the pre-processing of the raw spectra, the serum Raman spectral data of SLE were classified using the deep learning model DBayes. DBayesNet is primarily composed of a two-branch structure, with features at different levels extracted by the Bayesian Convolution (BayConv) module, Attention module, and finally, feature fusion performed by Concate, which is performed by the Bayesian Linear Layer (BayLinear) output to obtain the result of the classification prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The two sets of Raman spectral data were measured in the spectral wave number interval from 500 to 2000 cm-1. The characteristic peaks of serum Raman spectra were observed to be primarily located at 1653 cm<jats:sup>-1<\/jats:sup> (amide I), 1432 cm<jats:sup>-1<\/jats:sup> (lipid), 1320 cm<jats:sup>-1<\/jats:sup> (protein), 1246 cm<jats:sup>-1<\/jats:sup> (amide III, proline), and 1048 cm<jats:sup>-1<\/jats:sup> (glycogen). The following peaks were identified: 1653 cm<jats:sup>-1<\/jats:sup> (amide), 1432 cm<jats:sup>-1<\/jats:sup> (lipid), 1320 cm<jats:sup>-1<\/jats:sup> (protein), 1246 cm<jats:sup>-1<\/jats:sup> (amide III, proline), and 1048 cm<jats:sup>-1<\/jats:sup> (glycogen). A comparison was made between the proposed DBayesNet classification model and traditional machine and deep learning algorithms, including KNN, SVM, RF, LDA, ANN, AlexNet, ResNet, LSTM, and ResNet. The results demonstrated that the DBayesNet model achieved an accuracy of 85.9%. The diagnostic performance of the model was evaluated using three metrics: precision (82.3%), sensitivity (91.6%), and specificity (80.0%). These values demonstrate the model\u2019s ability to accurately diagnose SLE patients. Additionally, the model\u2019s efficacy in classifying SLE disease activity was assessed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study demonstrates the feasibility of Raman spectroscopy combined with deep learning algorithms to differentiate between SLE and non-SLE. The model\u2019s potential for clinical applications and research value in early diagnosis and activity assessment of SLE is significant.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1467027","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T06:48:52Z","timestamp":1741589332000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Diagnosis and activity prediction of SLE based on serum Raman spectroscopy combined with a two-branch Bayesian network"],"prefix":"10.3389","volume":"16","author":[{"given":"Qianxi","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinya","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinrun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengfang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuoyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengsi","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.it.2005.09.001","article-title":"SLE: challenges and candidates in human disease","volume":"26","author":"Croker","year":"2005","journal-title":"Trends Immunol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)30237-5","article-title":"Management strategies and future directions for systemic lupus erythematosus in adults","volume":"393","author":"Durcan","year":"2019","journal-title":"Lancet"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3390\/jcm10020243","article-title":"The main challenges in systemic lupus erythematosus: where do we stand","volume":"10","author":"Piga","year":"2021","journal-title":"J Clin Med"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1136\/annrheumdis-2019-215089","article-title":"2019 update of the EULAR recommendations for the management of systemic lupus erythematosus","volume":"78","author":"Fanouriakis","year":"2019","journal-title":"Ann Rheum Dis"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.autrev.2015.02.008","article-title":"Assessment of disease activity in Systemic Lupus Erythematosus: Lights and shadows","volume":"14","author":"Ceccarelli","year":"2015","journal-title":"Autoimmun Rev"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1136\/annrheumdis-2018-214502","article-title":"Derivation and validation of the SLE Disease Activity Score (SLE-DAS): a new SLE continuous measure with high sensitivity for changes in disease activity","volume":"78","author":"Jesus","year":"2019","journal-title":"Ann Rheum Dis"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1038\/s41584-020-00522-w","article-title":"Understanding and interpreting antinuclear antibody tests in systemic rheumatic diseases","volume":"16","author":"Bossuyt","year":"2020","journal-title":"Nat Rev Rheumatol"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.1020891","article-title":"Progress in the application of body fluid and tissue level mRNAs-non-coding RNAs for the early diagnosis and prognostic evaluation of systemic lupus erythematosus","volume":"13","author":"Liang","year":"2022","journal-title":"Front Immunol"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.2c05359","article-title":"Raman spectroscopy for chemical biology research","volume":"144","author":"Dodo","year":"2022","journal-title":"J Am Chem Soc"},{"key":"B10","doi-asserted-by":"publisher","first-page":"5137","DOI":"10.1038\/s41598-023-29943-9","article-title":"Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sj\u00f6gren\u2019s syndrome","volume":"13","author":"Chen","year":"2023","journal-title":"Sci Rep"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1016\/j.pdpdt.2022.103057","article-title":"Raman spectroscopy combined with machine learning algorithms for rapid detection Primary Sj\u00f6gren\u2019s syndrome associated with interstitial lung disease","volume":"40","author":"Wu","year":"2022","journal-title":"Photodiagnosis Photodyn Ther"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2023.104762","article-title":"R-GDORUS technology: Effectively solving the Raman spectral data imbalance in medical diagnosis","volume":"235","author":"Chen","year":"2023","journal-title":"Chemom Intell Lab Syst"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.3390\/s23052827","article-title":"Spatially offset raman spectroscopy combined with attention-based LSTM for freshness evaluation of shrimp","volume":"23","author":"Liu","year":"2023","journal-title":"Sensors"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1021\/acs.analchem.4c00064","article-title":"Rapid and precise differentiation and authentication of agricultural products via deep learning-assisted multiplex SERS fingerprinting","volume":"96","author":"Wang","year":"2024","journal-title":"Anal Chem"},{"key":"B15","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1038\/s42003-025-07624-9","article-title":"Machine learning with label-free Raman microscopy to investigate ferroptosis in comparison with apoptosis and necroptosis","volume":"8","author":"Verduijn","year":"2025","journal-title":"Commun Biol"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2023.1328228","article-title":"Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms","volume":"14","author":"Wu","year":"2023","journal-title":"Front Immunol"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1016\/j.impact.2021.100296","article-title":"An antibody-free liver cancer screening approach based on nanoplasmonics biosensing chips via spectrum-based deep learning","volume":"21","author":"Cheng","year":"2021","journal-title":"NanoImpact"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.48048\/tis.2023.5425","article-title":"Biomarker based detection and staging of breast cancer from blood using raman spectroscopy and deep learning technique","volume":"20","author":"Vijayakumar Selvarani","year":"2023","journal-title":"Trends Sci"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1039\/d3ay02250a","article-title":"A deep learning method for multi-task intelligent detection of oral cancer based on optical fiber Raman spectroscopy","volume":"16","author":"Li","year":"2024","journal-title":"Anal Methods Adv Methods Appl"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102832","article-title":"DLGNet: A dual-branch lesion-aware network with the supervised Gaussian Mixture model for colon lesions classification in colonoscopy images","volume":"87","author":"Wang","year":"2023","journal-title":"Med Image Anal"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1016\/j.talanta.2023.125052","article-title":"DBAN: An improved dual branch attention network combined with serum Raman spectroscopy for diagnosis of diabetic kidney disease","volume":"266","author":"Chen","year":"2024","journal-title":"Talanta"},{"key":"B22","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1136\/annrheumdis-2016-210571","article-title":"2016 American College of Rheumatology\/European League Against Rheumatism classification criteria for primary Sj\u00f6gren\u2019s syndrome: A consensus and data-driven methodology involving three international patient cohorts","volume":"76","author":"Shiboski","year":"2017","journal-title":"Ann Rheum Dis"},{"key":"B23","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-33-4885-1_17","article-title":"Undifferentiated connective tissue disease, mixed connective tissue disease, and the overlap syndromes","volume-title":"Clinical Rheumatology","author":"Handa","year":"2021"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1136\/annrheumdis-2021-221795","article-title":"2022 American College of Rheumatology\/European Alliance of Associations for Rheumatology classification criteria for granulomatosis with polyangiitis","volume":"81","author":"Robson","year":"2022","journal-title":"Ann Rheum Dis"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.5114\/reum.2024.141291","article-title":"Usefulness in daily practice of the Systemic Lupus Erythematosus Disease Activity Index 2000 scale and the Systemic Lupus Erythematosus Disease Activity Score index for assessing the activity of systemic lupus erythematosus","volume":"62","author":"Suszek","year":"2024","journal-title":"Reumatologia"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1038\/nprot.2015.024","article-title":"Obtaining information about protein secondary structures in aqueous solution using Fourier transform IR spectroscopy","volume":"10","author":"Yang","year":"2015","journal-title":"Nat Protoc"},{"key":"B27","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.saa.2016.06.042","article-title":"Fourier transform infrared spectroscopy and chemometrics for the characterization and discrimination of writing\/photocopier paper types: Application in forensic document examinations","volume":"170","author":"Kumar","year":"2017","journal-title":"Spectrochim Acta A Mol Biomol Spectrosc"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1038\/srep40448","article-title":"Demonstration of the protein involvement in cell electropermeabilization using confocal raman microspectroscopy","volume":"7","author":"Azan","year":"2017","journal-title":"Sci Rep"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1016\/j.saa.2022.121829","article-title":"Coupling and decoupling CH stretching vibration of methylene and methine in serine conformers","volume":"285","author":"Wang","year":"2023","journal-title":"Spectrochim Acta A Mol Biomol Spectrosc"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3390\/polym16081111","article-title":"Influences of polyphenols on the properties of crosslinked acellular fish swim bladders: experiments and molecular dynamic simulations","volume":"16","author":"Han","year":"2024","journal-title":"Polymers"},{"key":"B31","doi-asserted-by":"publisher","first-page":"1769","DOI":"10.1038\/s41467-020-15636-8","article-title":"Glycogen metabolism regulates macrophage-mediated acute inflammatory responses","volume":"11","author":"Ma","year":"2020","journal-title":"Nat Commun"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpcb.7b04999","article-title":"Tyrosine as a non-perturbing site-specific vibrational reporter for protein dynamics","volume":"121","author":"Chalyavi","year":"2017","journal-title":"J Phys Chem B"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1016\/j.molstruc.2020.128331","article-title":"Raman spectra of polyethylene glycols: Comparative experimental and DFT study","volume":"1217","author":"Kuzmin","year":"2020","journal-title":"J Mol Struct"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1016\/j.fmre.2024.04.008","article-title":"Mechanical communication and function regulation of immune cells","author":"Yang","year":"2024","journal-title":"Fundam Res"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1136\/annrheumdis-2016-209683","article-title":"Lupus high-density lipoprotein induces proinflammatory responses in macrophages by binding lectin-like oxidised low-density lipoprotein receptor 1 and failing to promote activating transcription factor 3 activity","volume":"76","author":"Smith","year":"2017","journal-title":"Ann Rheum Dis"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1016\/j.berh.2017.09.012","article-title":"Atherosclerosis in systemic lupus erythematosus","volume":"31","author":"Croca","year":"2017","journal-title":"Best Pract Res Clin Rheumatol"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1136\/ard-2023-225266","article-title":"The role of DNA in the pathogenesis of SLE: DNA as a molecular chameleon","volume":"83","author":"Pisetsky","year":"2024","journal-title":"Ann Rheum Dis"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.3390\/cells12162093","article-title":"Activated lymphocyte-derived DNA drives glucose metabolic adaptation for inducing macrophage inflammatory response in systemic lupus erythematosus","volume":"12","author":"Zhao","year":"2023","journal-title":"Cells"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1002\/VIW.20220038","article-title":"Advanced mass spectrometric and spectroscopic methods coupled with machine learning for in vitro diagnosis","volume":"4","author":"Chen","year":"2023","journal-title":"VIEW"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202304610","article-title":"Serum metabolic fingerprints characterize systemic lupus erythematosus","volume":"11","author":"Li","year":"2024","journal-title":"Adv Sci"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1002\/smll.202106412","article-title":"Hollow cobalt oxide\/carbon hybrids aid metabolic encoding for active systemic lupus erythematosus during pregnancy","volume":"18","author":"Wang","year":"2022","journal-title":"Small"}],"container-title":["Frontiers in Immunology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2025.1467027\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T06:48:56Z","timestamp":1741589336000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2025.1467027\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,10]]},"references-count":41,"alternative-id":["10.3389\/fimmu.2025.1467027"],"URL":"https:\/\/doi.org\/10.3389\/fimmu.2025.1467027","relation":{},"ISSN":["1664-3224"],"issn-type":[{"value":"1664-3224","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,10]]},"article-number":"1467027"}}