{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T17:16:49Z","timestamp":1788542209587,"version":"build-2803163510"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"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>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Syphilis is a sexually transmitted disease (STD) caused by\n                      <jats:italic>Treponema pallidum<\/jats:italic>\n                      (\n                      <jats:italic>TP<\/jats:italic>\n                      ) which could progress to neurosyphilis affecting the nervous system. However, the pathogenesis of neurosyphilis remain unknown. In addition, current diagnosis of neurosyphilis is mainly based on serological tests and cerebrospinal fluid (CSF) analysis, with limitations in terms of sensitivity and specificity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>From April 2021 to April 2023, 129 patients with syphilis were enrolled and divided into the non-neurosyphilis group (common syphilis, CS, n=19), asymptomatic neurosyphilis group (AN, n=77), symptomatic neurosyphilis group (NS, n=33) and a control group (n=15). Forty-eight cytokines\/chemokines in the CSF samples were measured and analysed in combination with clinical indices. Feature selections were further analysed for the using different machine learning model for the diagnosis and progression predicting of neurosyphilis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Machine learning-based feature selection identified a three-cytokine panel (RANTES, MIP-1\u03b2, and IL-3) that effectively distinguished syphilis patients from controls (AUC\u00a0=\u00a00.869, 95% CI: 0.821\u20130.917). For prediction of symptomatic neurosyphilis progression, IL-2R\u03b1 emerged as the optimal standalone biomarker, achieving an AUROC of 0.843 (95% CI: 0.784\u20130.902) for discriminating asymptomatic from symptomatic disease. Multivariable analysis confirmed IL-2R\u03b1 retained independent discriminatory power after adjusting for age (multivariable AUC\u00a0=\u00a00.876, 95% CI: 0.827\u20130.925). Age-stratified ROC analysis revealed IL-2R\u03b1 maintains robust diagnostic utility across all adult age groups, with optimal Youden-derived cut-offs of 21.82 U\/mL (sensitivity 100.0%, specificity 85.7%, AUC 0.939) for 35\u201345 years; 25.48 U\/mL (sensitivity 83.3%, specificity 88.9%, AUC 0.889) for 45\u201355 years; and 24.54 U\/mL (sensitivity 73.7%, specificity 84.6%, AUC 0.789) for &amp;gt;55 years. The combination of IL-2R\u03b1 and IP-10 significantly improved predictive accuracy within specific age strata, achieving AUCs of 0.949 (35\u201345 years), 1.000 (45\u201355 years), and 0.814 (&amp;gt;55 years) when distinguishing symptomatic from asymptomatic neurosyphilis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In conclusion, IL-2R\u03b1 and IP-10 represent promising predictive biomarkers for neurosyphilis progression, with their combined application achieving high diagnostic accuracy for distinguishing symptomatic from asymptomatic disease. These findings establish a foundation for CSF cytokine-guided risk stratification in syphilis patients, although prospective validation in larger, age-matched cohorts is warranted to optimize clinical cut-offs and confirm generalizability across diverse populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1677008","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T04:14:19Z","timestamp":1776658459000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Cytokines in cerebrospinal fluid combined with machine learning improve the diagnostic accuracy and predict the progression of neurosyphilis"],"prefix":"10.3389","volume":"17","author":[{"given":"Yu","family":"Zhao","sequence":"first","affiliation":[{"name":"Shenzhen Third People\u2019s Hospital, Second Hospital Affiliated to Southern University of Science and Technology","place":["Shenzhen, 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