Publications

A passive flow microreactor for urine creatinine test (2025)

Diagram showing a microfluidic device with reagent and sample inlets, a mixing channel, and an outlet. Graphs display normalized concentration and channel width' effects on flow and separation. Photograph of experimental setup includes a laptop, microfluidic chip, and syringe pump.

Description

Researchers have developed a rapid, point-of-care microfluidic device (uCR-Chip) that accurately measures urine creatinine levels within 7 minutes using a simple USB microscope. Designed to work alongside a previously developed urine albumin test, this new chip enables reliable CKD evaluation without lab-based testing. With high precision, a broad detection range, and low matrix interference, the uCR-Chip offers a promising, accessible solution for chronic kidney disease assessment and broader biomarker monitoring.

Authors: Dumitru Tomsa, Yang Liu, Amanda Stefanson, Xiaoou Ren, AbdulRazaq A. H., Sokoro, Paul Komenda, Navdeep Tangri, Rene P. Zahedi, Claudio Rigatto, and Francis Lin

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A passive flow microreactor for urine creatinine test (2024)

Diagram showing the filling process of a typical sphere-shaped ow in various timeframes, with velocity profiles illustrating differences in flow geometry.

Description

The uCR-Chip is a low-cost, passive flow microreactor developed to measure urine creatinine levels for point-of-care chronic kidney disease (CKD) screening. Using a USB microscope and colorimetric detection, the chip delivers accurate results within 7 minutes and meets clinical sensitivity standards. Validated against commercial tests, the uCR-Chip offers a reliable and portable solution, especially suited for remote or resource-limited settings, and serves as a foundation for broader disease biomarker detection.

Authors: Dumitru Tomsa, Yang Liu, Amanda Stefanson, Xiaoou Ren, AbdulRazaq A. H. Sokoro, Paul Komenda, Navdeep Tangri, Rene Zahedi, Claudio Rigatto, Francis Lin

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A Passive Mixing Microfluidic Urinary Albumin Chip for Chronic Kidney Disease Assessment (2018)

A scientific figure with five panels (a-e) showing experiments on concentration, signal, and relationships between variables. Panel a displays a series of color-coded test strips indicating different concentrations of a solution. Panel b shows a linear correlation between concentration and signal. Panel c compares the interaction over time between two platforms, Well-plate and UAL-Chip. Panel d depicts a high correlation between Well-plate results and UAL-Chip results. Panel e shows the correlation between UAL-Chip results and clinical results.

Description

The UAL-Chip is a low-cost, microfluidic device developed for point-of-care detection of urinary albumin, a key biomarker for early-stage chronic kidney disease (CKD). Using a passive mixing system and fluorescent assay, it delivers accurate, stable results within 5 minutes—comparable to lab-based tests but at a fraction of the cost. Validated with CKD patient samples, the UAL-Chip achieved a low detection limit (5.2 μg/mL) and high classification accuracy, making it a practical tool for early CKD screening, especially in remote or resource-limited settings.

Authors: Jiandong Wu, Dumitru Tomsa, Michael Zhang, Paul Komenda, Navdeep Tangri, Claudio Rigatto, and Francis Lin

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A Passive Mixing Microfluidic Urinary Albumin Chip for Chronic Kidney Disease Assessment (2018)

A collage of images and diagrams showing a USB microscope, LED array, and fiber optic probe, along with scientific data including a temperature gradient, laser diode assembly, a graph of intensity versus concentration, and an infrared image.

Description

This supplementary study supports the development of the UAL-Chip, a low-cost microfluidic device designed for point-of-care detection of urinary albumin, a key marker for chronic kidney disease (CKD). Clinical validation with 12 patient samples demonstrated strong correlation between UAL-Chip results and clinical gold standards for albuminuria and UACR thresholds. Additionally, two portable imaging prototypes—USB microscope and smartphone-based systems—were tested for feasibility, highlighting future potential for low-resource CKD screening outside traditional lab settings.

Authors: Jiandong Wu, Dumitru Tomsa, Michael Zhang, Paul Komenda, Navdeep Tangri, Claudio Rigatto, and Francis Lin

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Lab-on-chip technology for chronic disease diagnosis (2018)

Four panels display different aspects of LOC technology for CD diagnosis. Panel A shows a test card with three channels, a pink coin, and labels for negative control, DNA detection, and DNA mismatch detection. Panel B features a microfluidic chip with labeled inputs for waste buffer, washing buffer, blood specimen, and saliva sample, along with output waste and an image of the microchip. Panel C illustrates neutrophil capture in a microfluidic device with flow control and a 10-minute timer. Panel D displays a source well, a sphum gradient image, a diagram of a microfluidic channel with a cell line, and a flow gradient diagram.

Description

This 2018 npj Digital Medicine review explores the growing role of microfluidics in digital and personalized healthcare. It highlights how microfluidic technologies enable low-cost, miniaturized, and portable diagnostic tools that can be integrated with smartphones and connected platforms. These innovations are paving the way for scalable point-of-care testing, real-time health monitoring, and accessible chronic disease management—especially in remote or resource-limited settings. The paper emphasizes microfluidics as a foundational technology for the future of digital health.

Authors: Jiandong Wu, Meili Dong, Claudio Rigatto, Yong Liu, and Francis Lin

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Lab-on-a-Chip Platforms for Detection of Cardiovascular Disease and Cancer Biomarkers (2017)

Chart showing the distribution of diseases, with cardiovascular diseases at 31.3%, cancer at 16.8%, respiratory diseases at 6.9%, digestive and other NCDs at 12.1%, and other NCDs at 12.1%. Diagram illustrating shared risk factors for CVD and cancer, like diet, smoking, obesity, hypertension, and diabetes, with a bi-directional relationship. Image depicting plasma extraction from whole blood using a needle and a microfluidic device for diagnostics, labeled 'Suction chambers.'

Description

This 2017 review highlights the advancements in lab-on-a-chip (LOC) technologies for detecting biomarkers related to cardiovascular disease and cancer. It discusses how microfluidic platforms enable rapid, low-cost, and minimally invasive diagnostics using small sample volumes. The paper outlines key innovations in LOC integration, including point-of-care testing, multiplex detection, and real-time monitoring. These developments are accelerating early diagnosis and personalized treatment strategies, especially in resource-limited and decentralized healthcare settings.

Authors: Jiandong Wu, Meili Dong, Susy Santos, Claudio Rigatto, Yong Liu, and Francis Lin

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Rapid and Low-Cost CRP Measurement by Integrating a Paper-Based Microfluidic Immunoassay with Smartphone (CRP-Chip) (2017)

Diagram of CRP-chip assembly, operation, and analysis process. Panel A shows assembling steps with nitrocellulose paper, absorbent pad, and plasma separation membrane. Panel B details testing with sample application, imaging, and analysis for protein concentration. Panel C illustrates the nitrocellulose paper with printed channels and antibodies, backing pad, conjugate, and plasma separation membrane.

Description

This 2017 paper presents a microfluidic CRP-Chip designed for rapid, point-of-care detection of C-reactive protein (CRP), a key inflammation biomarker used in cardiovascular disease and infection assessment. The chip uses passive mixing and fluorescence-based detection to deliver accurate results within 4 minutes, using only a small volume of plasma. Validated with clinical samples, the CRP-Chip shows strong correlation with standard lab tests, offering a cost-effective, portable alternative for decentralized healthcare and timely clinical decision-making.

Authors: Meili Dong, Jiandong Wu, Zimin Ma, Hagit Peretz-Soroka, Michael Zhang, Paul Komenda, Navdeep Tangri, Yong Liu, Claudio Rigatto, and Francis Lin

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