Evidence map›Paper›PMID 40853910›Full record

ArticlePLoS neglected tropical diseases2025

Preliminary screening of urinary host protein biomarkers for Schistosomiasis haematobium: A proteome profiling study identifying candidate diagnostic targets in school-aged children.

Yiyun Liu, Saleh Juma, Qingkai Xue, Mingzhen He, Said Mohammed Ali, Khamis Seif Khamis, Mchanga Mohd Suleiman, Mayda Salim Hamad, Mgeni Abdalla Khamis, Hongxia Zhao and 3 more

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2025. 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. Trial
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.

Yiyun LiuNational Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu, China.
Saleh JumaNeglected Tropical Diseases Program, Ministry of Health, Chake-Chake, Pemba, United Republic of Tanzania.
Qingkai XueNational Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu, China.
Mingzhen HeChangzhou Center for Disease Control and Prevention, Changzhou, Jiangsu, China.
Said Mohammed AliPublic Health Laboratory-Ivo de Carneri, Chake-Chake, Pemba, United Republic of Tanzania.
Khamis Seif KhamisNeglected Tropical Diseases Program, Ministry of Health, Chake-Chake, Pemba, United Republic of Tanzania.
Mchanga Mohd SuleimanNeglected Tropical Diseases Program, Ministry of Health, Chake-Chake, Pemba, United Republic of Tanzania.
Mayda Salim HamadNeglected Tropical Diseases Program, Ministry of Health, Chake-Chake, Pemba, United Republic of Tanzania.
Mgeni Abdalla KhamisNeglected Tropical Diseases Program, Ministry of Health, Chake-Chake, Pemba, United Republic of Tanzania.
Hongxia ZhaoSchool of Medicine, Shanghai University, Shanghai, China.
Xin DongSchool of Medicine, Shanghai University, Shanghai, China.
Kun YangNational Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu, China.
Yuzheng HuangNational Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu, China.ORCID 0000-0002-8357-8806

Funding

Jiangsu Province Capability Improvement Project through Science, Technology, and EducationJiangsu Provincial Department of Science and TechnologyNational Key Research and Development Program of ChinaSpecial Foundation for Science and Technology of Jiangsu Province
6 · The paper itself

Abstract

Schistosomiasis is a major public health challenge and a globally neglected tropical disease. Schistosoma haematobium, the causative agent of urogenital schistosomiasis, is endemic in African countries; with school-aged children ages 7-15 years being the most vulnerable population. Current diagnostic methods rely on microscopy to identify parasite eggs in urine; which is labor-intensive, requires specialized skills, and often lacks sensitivity, especially in mild infections. To address these limitations, we explored host disease-related biomarkers as a promising avenue for advancing diagnosis and detection. We recruited 135 children ages 7-15 years from Zanzibar, a known transmission hotspot, and used data-independent acquisition (DIA) proteomics combined with machine learning to identify potential host protein biomarkers in urine samples from individuals infected with Schistosoma haematobium. Proteomic analysis identified 823 common host proteins in urine samples from the infected group. Machine learning algorithms highlighted candidate discriminative proteins; which were validated using enzyme-linked immunosorbent assays (ELISA). Machine learning emphasized SYNPO2, CD276, α2M, LCAT, and hnRNPM as the most discriminating biomarkers for Schistosoma haematobium infection. ELISA validation confirmed the differential expression trends of these proteins, while machine learning further validated LCAT and α2M, underscoring their diagnostic potential. Our study focused on host-derived proteins and identified key urinary protein biomarkers associated with Schistosoma haematobium infection, and offers new insights into host-parasite interactions and potential tools for non-invasive diagnostics. While validated in African pediatric populations from transmission hotspots, this host-protein approach inherently overcomes geographic limitations of parasite-based diagnostics; which is a critical advantage for surveillance in non-endemic regions where imported cases threaten gains toward elimination. These findings lay the groundwork for developing novel diagnostic approaches that could significantly improve the detection and surveillance of schistosomiasis, particularly in high-risk populations.

Indexed as

BiomarkersProteomeSchistosomiasis haematobiaAdolescentAnimalsChildEnzyme-Linked Immunosorbent AssayFemaleHumansMachine LearningMaleProteomicsSchistosoma haematobiumTanzaniaBiomarkersProteome

Identifiers

PMID40853910
PMCPMC12377595

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.