#AI reads Urine# Preliminary screening of urinary host protein biomarkers for Schistosomiasis haematobium

The study focused on school-aged children (aged 7–15 years) in Zanzibar, a region with a high prevalence of Schistosomiasis haematobium. Addressing the limitations of current diagnostic methods for Schistosomiasis haematobium— which rely on microscopic identification of parasite eggs in urine, are time-consuming, require specialized skills, and lack sufficient sensitivity in cases of mild infection— the research employed data-independent acquisition (DIA) proteomics combined with machine learning. From urine samples of 135 children, the study first identified 823 common host proteins and 269 differentially expressed proteins between the infected group and the healthy control group. Through machine learning, five key candidate discriminative biomarkers were screened out, namely SYNPO2, CD276, α2M, LCAT, and hnRNPM. Subsequent validation via enzyme-linked immunosorbent assay (ELISA) confirmed the differential expression trends of these proteins, and further validated the diagnostic potential of LCAT and α2M. Ultimately, this research lays the foundation for the development of a more simple, sensitive, and non-invasive diagnostic method for Schistosomiasis haematobium that does not require microscopy, holding promise for improving disease detection and monitoring in high-risk populations and alleviating the disease burden.

PLoS Negl Trop Dis. 2025 Aug 25;19(8):e0013429. doi: 10.1371/journal.pntd.0013429.

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/