Ontology highlight
ABSTRACT:
SUBMITTER: Wu MY
PROVIDER: S-EPMC10959656 | biostudies-literature | 2024 Apr
REPOSITORIES: biostudies-literature
Wu Meng Yin MY Chen Lu L Liu Li Cheng LC Liu Ming Juan MJ Li Yan Feng YF Zheng He Yi HY Leng Ling L Zou Yi Jun YJ Chen Wei Jun WJ Li Jun J
iScience 20240301 4
The question of whether serofast status of syphilis patients indicates an ongoing low-grade <i>Treponema pallidum (T. pallidum)</i> infection remains unanswered. To address this, we developed a machine learning model to identify <i>T. pallidum</i> in cell-free DNA (cfDNA) using next-generation sequencing (NGS). Our findings showed that a TP_rate cut-off of 0.033 demonstrated superior diagnostic performance for syphilis, with a specificity of 92.3% and a sensitivity of 71.4% (AUROC = 0.92). This ...[more]