论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
与中枢神经系统潜伏梅毒感染相比,神经梅毒的脑脊液变化和临床特征:一项横断面研究
Authors Ge Y, Gou X, Dong X, Peng Y, Yang F
Received 14 May 2022
Accepted for publication 5 September 2022
Published 9 September 2022 Volume 2022:15 Pages 5377—5385
DOI https://doi.org/10.2147/IDR.S371446
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Suresh Antony
Purpose: At present, there is no gold standard or unified standard for the diagnosis of neurosyphilis, and the rate of misdiagnosis is high. The diagnosis of neurosyphilis is still challenging. This study compared the clinical indicators between neurosyphilis and latent syphilis infection in the central nervous system. The purpose of this study was to provide evidence for the differential diagnosis and prognosis of patients with neurosyphilis and latent syphilis infection of the central nervous system.
Methods: The clinical data of 59 patients with neurosyphilis and 30 patients with latent syphilis infection in the nervous system from 2008 to 2021 were analyzed. The cerebrospinal fluid and serum biochemical markers were evaluated for all patients.
Results: CSF-nucleated cells, CSF-TRUST, CSF-totalprotein and CSF-IgG (P < 0.001) were significantly different between neurosyphilis and latent syphilis infection in the central nervous system. CSF-TRUST titer was positively correlated with D-D concentration (r = 0.274, P < 0.05), sodion (r =0.251, P < 0.05), respectively. Glucose concentration is the most unreliable in the diagnosis of neurosyphilis (AUC=0.445, P =0.395), and TRUST combined with nucleated cells and total protein is the most accurate in the diagnosis of neurosyphilis (AUC=0.989, P < 0.001).
Conclusion: The combination of TRUST, nucleated cell count and totalprotein detection in CSF can distinguish the patients with neurosyphilis and latent syphilis infection in the central nervous system, which has a significant diagnostic value.
Keywords: neurosyphilis, latent syphilis infection, diagnosis, detection index