Systematic Review of S100 Proteins in IgA Vasculitis and Other Vasculitides
by Zofia Podraza·Updated 3mo ago
273.0 KB1files
Available on 1 platform
Sign in to view source links and access this dataset
Description
A 2025 systematic review synthesizes evidence from 54 human studies on S100 proteins in vasculitides. The review, authored by Zofia Podraza, focuses on the mechanistic and clinical roles of S100A8/9, S100A12, S100A4, and S100A10, evaluating their potential as biomarkers for disease activity and prognosis.
Use Cases
Analyze the association between S100A8/9 protein levels and neutrophil-driven inflammation in systemic vasculitides.
Evaluate the evidence for S100A12 as a biomarker for disease activity in IgA vasculitis (IgAV).
Compare the fragmentary findings on S100A4 and S100A10 across different forms of vasculitis.
Synthesize narrative data from studies included in the PRISMA-compliant review to identify gaps in IgAV-specific research.
Strengths
Synthesizes findings from 54 original human studies, providing a broad evidence base.
Focuses on four specific S100 proteins (S100A8/9, S100A12, S100A4, S100A10) with defined biological roles.
Follows a PRISMA-compliant systematic review methodology for transparent evidence synthesis.
Includes an independent risk-of-bias assessment for the included studies.
Limitations
The underlying data is narrative synthesis from studies; no primary tabular data with rows and columns is provided.
Findings for S100A4 and S100A10 are described as fragmentary and lack sufficient IgAV-specific data.
The dataset is a 273 KB document, limiting direct computational analysis without manual extraction.
Provenance
Source
Systematic search in PubMed, Embase, Scopus, and Web of Science.
Collection Method
PRISMA-compliant systematic review of original human studies.
Time Range
Literature search up to November 18, 2025.
Freshness
Literature search conducted up to November 18, 2025; document last updated March 25, 2026.
Data is contained within a narrative review document (DOCX format); users must manually extract information for analysis. License is CC BY 4.0.