Evidence map›Paper›PMID 42088502›Full record

ArticleFrontiers in immunology2026

Cytokines in cerebrospinal fluid combined with machine learning improve the diagnostic accuracy and predict the progression of neurosyphilis.

Yu Zhao, Guohao Fan, Qi Sheng, Canghai Ma, Gaolei Dong, Qizheng Wang, Xiaoma Liu, Xi Xie, Jinhu Liang, Yun Peng and 3 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Yu Zhao *Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Guohao Fan *Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Qi Sheng *Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Canghai Ma *Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Gaolei DongShenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Qizheng WangShenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Xiaoma LiuShenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Xi XieShenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Jinhu LiangShenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Yun PengShenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Shijun WangShenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Yang YangShenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Fuxiang WangShenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Syphilis is a sexually transmitted disease (STD) caused by Methods: 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. Results: Machine learning-based feature selection identified a three-cytokine panel (RANTES, MIP-1β, and IL-3) that effectively distinguished syphilis patients from controls (AUC = 0.869, 95% CI: 0.821-0.917). For prediction of symptomatic neurosyphilis progression, IL-2Rα emerged as the optimal standalone biomarker, achieving an AUROC of 0.843 (95% CI: 0.784-0.902) for discriminating asymptomatic from symptomatic disease. Multivariable analysis confirmed IL-2Rα retained independent discriminatory power after adjusting for age (multivariable AUC = 0.876, 95% CI: 0.827-0.925). Age-stratified ROC analysis revealed IL-2Rα 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 35-45 years; 25.48 U/mL (sensitivity 83.3%, specificity 88.9%, AUC 0.889) for 45-55 years; and 24.54 U/mL (sensitivity 73.7%, specificity 84.6%, AUC 0.789) for >55 years. The combination of IL-2Rα and IP-10 significantly improved predictive accuracy within specific age strata, achieving AUCs of 0.949 (35-45 years), 1.000 (45-55 years), and 0.814 (>55 years) when distinguishing symptomatic from asymptomatic neurosyphilis. Conclusion: In conclusion, IL-2Rα 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.

Indexed as

CytokinesMachine LearningNeurosyphilisAdultBiomarkersDisease ProgressionFemaleHumansMaleMiddle AgedPredictive Learning ModelsSensitivity and SpecificityTreponema pallidumBiomarkersCytokinescerebrospinal fluiddiagnosticIL-2RαIP-10machine learningneurosyphilis

Identifiers

PMID42088502
PMCPMC13135949

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.