ArticleBiosensors2025
A Portable Smartphone-Based 3D-Printed Biosensing Platform for Kidney Function Biomarker Quantification.
Article in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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.
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Who cites it
4 citing papers in PubMed.
- From Drops to Decisions: AI/ML-Driven Biofluidics for Clinical Diagnostics and Healthcare Intelligence.Analytical chemistry · 2026Review
- Smartphone-integrated portable microfluidic platform for liver biomarker quantification using deep learning.Scientific reports · 2025Article
- Smartphone-based biosensing: a review of optical imaging, microfluidic integration, and AI-enhanced analysis.Mikrochimica acta · 2025Review
- Microfluidics and molecular diagnostics in renal cell carcinoma: advances, challenges, and future directions.Frontiers in oncology · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detecting kidney function biomarkers is critical for the early diagnosis of kidney diseases and monitoring treatment efficacy. In this work, a portable, 3D-printed colorimetric sensor platform was developed to detect key kidney biomarkers: uric acid, creatinine, and albumin. The platform features a 3D-printed enclosure with integrated diffused LED lighting to ensure a controlled environment for image acquisition. A disposable 3D-printed flow cell holds samples, ensuring precision and minimizing contamination. The sensor relies on colorimetric analysis, where a reagent reacts with blood serum to produce a color shift proportional to the biomarker concentration. Using a smartphone, the color change is captured, and RGB values are normalized to calculate concentrations based on the Beer-Lambert Law. The system adapts to variations in smartphones, reagent brands, and lighting conditions through an adaptive calibration algorithm, ensuring flexibility and accuracy. The sensor demonstrated good linear detection ranges for uric acid (1-30 mg/dL), creatinine (0.1-20 mg/dL), and albumin (0.1-8 g/dL), with detection limits of 1.15 mg/dL, 0.15 mg/dL, and 0.11 g/dL, respectively. These results correlated well with commercial biochemistry analyzers. Additionally, an Android application was developed to handle image processing and database management, providing a user-friendly interface for real-time blood analysis. This portable, cost-effective platform shows significant potential for point-of-care diagnostics and remote health monitoring.
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Registered trials
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 OpenQuestion graph.