# A biomarker panel based smart mini-array system for the homecare of autoimmune kidney diseases

> **NIH NIH R01** · UNIVERSITY OF HOUSTON · 2020 · $434,027

## Abstract

Project summary
Systemic lupus erythematosus (SLE) is a complex autoimmune disease predominantly affecting
women. Lupus nephritis (LN), is a leading cause of high mortality. Timely treatment of LN flares
with anti-inflammatory and immunosuppressant drugs is critical, but flares are not currently
diagnosed in a point-of-care or home setting; the gold standard for LN flare diagnosis is renal
biopsy. At-home or Point-of-care (POC) detection of LN flares would improve treatment
responsiveness access and facilitate SLE clinical research. We propose 3 specific aims: AIM 1:
To develop multiplex quantitative Biomarker Mini-array (BMA) for urinary SLE flare markers.
AIM 2: To develop a reliable, convenient Biomarker Mini-array for LN flare identification by
smartphone based analysis and reporting system (SBARS). AIM 3: To evaluate the clinical
performance of the BMA-SBARS tests in disease settings, using urine samples from cross-
sectional and longitudinal cohorts of LN patients. The primary innovation of the proposed work
lies in its development of a prototype practical POC/self-testing urine test for LN flares. The
technology itself will have broad impact in self-management of chronic diseases at home,
especially for aged patients.

## Key facts

- **NIH application ID:** 9900712
- **Project number:** 5R01AG062987-02
- **Recipient organization:** UNIVERSITY OF HOUSTON
- **Principal Investigator:** Tianfu Wu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $434,027
- **Award type:** 5
- **Project period:** 2019-04-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9900712

## Citation

> US National Institutes of Health, RePORTER application 9900712, A biomarker panel based smart mini-array system for the homecare of autoimmune kidney diseases (5R01AG062987-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9900712. Licensed CC0.

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