{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T18:28:08Z","timestamp":1792175288520,"version":"4.4.0"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T00:00:00Z","timestamp":1710720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG062987"],"award-info":[{"award-number":["R01AG062987"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>To improve the efficiency and patient coverage of the current healthcare system, user-friendly novel homecare devices are urgently needed. In this work, we developed a smartphone-based analyzing and reporting system (SBARS) for biomarker detection in lupus nephritis (LN). This system offers a cost-effective alternative to traditional, expensive large equipment in signal detection and quantification. This innovative approach involves using a portable and affordable microscopic reader to capture biomarker signals. Through smartphone-based image processing techniques, the intensity of each biomarker signal is analyzed. This system exhibited comparable performance to a commercial Genepix scanner in the detection of two potential novel biomarkers of LN, VISG4 and TNFRSF1b. Importantly, this smartphone-based analyzing and reporting system allows for discriminating LN patients with active renal disease from healthy controls with the area-under-the-curve (AUC) value = 0.9 for TNFRSF1b and 1.0 for VSIG4, respectively, indicating high predictive accuracy.<\/jats:p>","DOI":"10.3390\/bios14030147","type":"journal-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T06:58:39Z","timestamp":1710745119000},"page":"147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Advancing Point-of-Care Diagnosis: Digitalizing Combinatorial Biomarker Signals for Lupus Nephritis"],"prefix":"10.3390","volume":"14","author":[{"given":"Jiechang","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, University of Houston, Houston, TX 77024, USA"},{"name":"Department of Computer Science, University of Houston, Houston, TX 77024, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aygun","family":"Teymur","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Houston, Houston, TX 77024, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9037-1179","authenticated-orcid":false,"given":"Chenling","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Houston, Houston, TX 77024, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5722-9509","authenticated-orcid":false,"given":"Ramesh","family":"Saxena","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX 75390, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4406-1449","authenticated-orcid":false,"given":"Tianfu","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Houston, Houston, TX 77024, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,18]]},"reference":[{"key":"ref_1","first-page":"849684","article-title":"Lupus nephritis: An overview of recent findings","volume":"2012","year":"2012","journal-title":"Autoimmune Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1093\/rheumatology\/kev311","article-title":"The frequency and outcome of lupus nephritis: Results from an international inception cohort study","volume":"55","author":"Hanly","year":"2016","journal-title":"Rheumatology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"365","DOI":"10.36348\/sjmps.2023.v09i06.006","article-title":"A Study on Clinical and Laboratory Status of Active and Inactive LN Patients","volume":"9","author":"Hoque","year":"2023","journal-title":"Saudi J. Med Pharm. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1177\/0961203320932219","article-title":"Systemic lupus erythematosus, lupus nephritis and end-stage renal disease: A pragmatic review mapping disease severity and progression","volume":"29","author":"Mahajan","year":"2020","journal-title":"Lupus"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1053\/j.ackd.2019.08.015","article-title":"The kidney biopsy in systemic lupus erythematosus: A view of the past and a vision of the future","volume":"26","author":"Ayoub","year":"2019","journal-title":"Adv. Chronic Kidney Dis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1038\/nrd891","article-title":"Clinical proteomics: Translating benchside promise into bedside reality","volume":"1","author":"Petricoin","year":"2002","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1038\/nature01514","article-title":"Disease proteomics","volume":"422","author":"Hanash","year":"2003","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1146\/annurev.med.55.091902.105237","article-title":"Serum proteomics in cancer diagnosis and management","volume":"55","author":"Rosenblatt","year":"2004","journal-title":"Annu. Rev. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.talanta.2013.03.048","article-title":"Ion suppression; a critical review on causes, evaluation, prevention and applications","volume":"115","author":"Furey","year":"2013","journal-title":"Talanta"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1586\/erm.10.110","article-title":"Systematic antibody and antigen-based proteomic profiling with microarrays","volume":"11","author":"Ayoglu","year":"2011","journal-title":"Expert Rev. Mol. Diagn."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1002\/mas.20269","article-title":"Glycoprotein analysis using protein microarrays and mass spectrometry","volume":"29","author":"Patwa","year":"2010","journal-title":"Mass Spectrom. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1111\/j.1365-2249.2006.03251.x","article-title":"Protein array autoantibody profiles for insights into systemic lupus erythematosus and incomplete lupus syndromes","volume":"147","author":"Li","year":"2007","journal-title":"Clin. Exp. Immunol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1186\/s13075-015-0682-6","article-title":"Autoantigen microarrays reveal autoantibodies associated with proliferative nephritis and active disease in pediatric systemic lupus erythematosus","volume":"17","author":"Haddon","year":"2015","journal-title":"Arthritis Res. Ther."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1177\/0961203318773643","article-title":"Clinical application of protein biomarkers in lupus erythematosus and lupus nephritis","volume":"27","author":"Qi","year":"2018","journal-title":"Lupus"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1111\/joim.12820","article-title":"Smartphone-based clinical diagnostics: Towards democratization of evidence-based health care","volume":"285","author":"Neumann","year":"2019","journal-title":"J. Intern. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"16240","DOI":"10.1021\/acs.analchem.1c04126","article-title":"A smartphone optical device for point-of-care testing of glucose and cholesterol using Ag NPs\/UiO-66-NH2-based ratiometric fluorescent probe","volume":"93","author":"Guo","year":"2021","journal-title":"Anal. Chem."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"44778","DOI":"10.1038\/srep44778","article-title":"A smartphone-based diagnostic platform for rapid detection of Zika, chikungunya, and dengue viruses","volume":"7","author":"Priye","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"231","DOI":"10.7150\/thno.14023","article-title":"Smartphone-based fluorescent diagnostic system for highly pathogenic H5N1 viruses","volume":"6","author":"Yeo","year":"2016","journal-title":"Theranostics"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ludwig, S.K., Tokarski, C., Lang, S.N., van Ginkel, L.A., Zhu, H., Ozcan, A., and Nielen, M.W. (2015). Calling biomarkers in milk using a protein microarray on your smartphone. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0134360"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, G., Li, Y., Tang, C., Lin, F., Wu, T., and Bao, J. (2022). Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus. Chemosensors, 10.","DOI":"10.3390\/chemosensors10080330"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3302","DOI":"10.1039\/D0LC00547A","article-title":"A modular microarray imaging system for highly specific COVID-19 antibody testing","volume":"20","author":"Hedde","year":"2020","journal-title":"Lab A Chip"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4361","DOI":"10.1039\/D1AY01209F","article-title":"Smartphone-based colorimetric detection system for portable health tracking","volume":"13","author":"Balbach","year":"2021","journal-title":"Anal. Methods"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"022001","DOI":"10.1088\/2050-6120\/ab7365","article-title":"Photobleaching of organic fluorophores: Quantitative characterization, mechanisms, protection","volume":"8","author":"Demchenko","year":"2020","journal-title":"Methods Appl. Fluoresc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"808839","DOI":"10.3389\/fimmu.2021.808839","article-title":"Emerging molecular markers towards potential diagnostic panels for lupus","volume":"12","author":"Tan","year":"2022","journal-title":"Front. Immunol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1002\/art.42545","article-title":"V-Set Immunoglobulin Domain\u2013Containing Protein 4 as a Novel Serum Biomarker of Lupus Nephritis and Renal Pathology Activity","volume":"75","author":"Tang","year":"2023","journal-title":"Arthritis Rheumatol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1002\/art.40830","article-title":"Identification of novel autoantibodies associated with psoriatic arthritis","volume":"71","author":"Yuan","year":"2019","journal-title":"Arthritis Rheumatol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Roy, P., Dutta, S., Dey, N., Dey, G., Chakraborty, S., and Ray, R. (2014, January 10\u201311). Adaptive thresholding: A comparative study. Proceedings of the 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kanyakumari, India.","DOI":"10.1109\/ICCICCT.2014.6993140"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1080\/01621459.1972.10481251","article-title":"Significance testing of the Spearman rank correlation coefficient","volume":"67","author":"Zar","year":"1972","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1038\/s41590-020-0677-6","article-title":"Autoimmunity and organ damage in systemic lupus erythematosus","volume":"21","author":"Tsokos","year":"2020","journal-title":"Nat. Immunol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Renaudineau, Y., Brooks, W., and Belliere, J. (2023). Lupus nephritis risk factors and biomarkers: An update. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms241914526"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1002\/art.39623","article-title":"Development of biomarker models to predict outcomes in lupus nephritis","volume":"68","author":"Wolf","year":"2016","journal-title":"Arthritis Rheumatol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.gpb.2015.09.001","article-title":"Autoantigen microarray for high-throughput autoantibody profiling in systemic lupus erythematosus","volume":"13","author":"Zhu","year":"2015","journal-title":"Genom. Proteom. Bioinform."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, M., Zhang, Y., Liu, T., Yang, Y., and Yang, P. (2022, January 17\u201321). Multi-task Learning with Adaptive Global Temporal Structure for Predicting Alzheimer\u2019s Disease Progression. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA.","DOI":"10.1145\/3511808.3557406"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1586\/14737159.2016.1142877","article-title":"Advances in addressing technical challenges of point-of-care diagnostics in resource-limited settings","volume":"16","author":"Wang","year":"2016","journal-title":"Expert Rev. Mol. Diagn."},{"key":"ref_36","unstructured":"Kolberg, J., Penny, L., Todd, J., and Urdea, M. (2009). An Assessment of the Technological Issues and Options for Point-of-Care Diagnostic Tests in Resource-Limited Settings, Halteres Associates."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2139","DOI":"10.1002\/pmic.201000460","article-title":"Proteomic technologies for the identification of disease biomarkers in serum: Advances and challenges ahead","volume":"11","author":"Ray","year":"2011","journal-title":"Proteomics"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.talanta.2017.03.091","article-title":"Depleting high-abundant and enriching low-abundant proteins in human serum: An evaluation of sample preparation methods using magnetic nanoparticle, chemical depletion and immunoaffinity techniques","volume":"170","author":"Raimundo","year":"2017","journal-title":"Talanta"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Salvi, M., Acharya, U.R., Molinari, F., and Meiburger, K.M. (2021). The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Comput. Biol. Med., 128.","DOI":"10.1016\/j.compbiomed.2020.104129"}],"container-title":["Biosensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-6374\/14\/3\/147\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:15:22Z","timestamp":1760105722000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-6374\/14\/3\/147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,18]]},"references-count":39,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["bios14030147"],"URL":"https:\/\/doi.org\/10.3390\/bios14030147","relation":{},"ISSN":["2079-6374"],"issn-type":[{"value":"2079-6374","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,18]]}}}