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Nowadays, kidney dysfunction is a common health issue due to the modernized lifestyle. Even though medications are commercially available to treat kidney diseases, early diagnosis is crucial and challenging. Clinically, measuring urine creatinine and pH has gained significant interest as a way to diagnose kidney diseases early. In the present work, we attempted to develop a low-cost, robust, accurate and naked-eye colorimetric method to determine both creatinine levels and pH variations in artificial urine samples using a simple 3D-printed hybrid microfluidic device. Creatinine was detected by the incorporation of the traditional Jaffe test onto the hybrid paper-PMMA microfluidic device and pH (4-8) was measured by a simple anthocyanin test. Notably, the tests were established without employing any sophisticated or costly instrument clusters. The developed 3D-printed microfluidic probe showed a limit of detection (LOD) of 0.04 mM for creatinine over a concentration range of 1-10 mM, with a regression coefficient ( R 2 ) of 0.995 in laboratory conditions. Interestingly, the experimental data obtained with artificial urine exhibited a wide linear range from 0.1 mM to 5 mM under different pH values ranging from 4 to 8 in the presence of matrices commonly found in urine samples other than proteins, indicating the potential use of this method in pre-clinical analysis. Since the wide linear range of urine creatinine in artificial urine samples falls well below the clinically relevant concentrations in humans (0.07-0.27 mM), the developed lab-on-chip device is further suitable for clinical evaluation with proper ethical clearance. This 3D-printed hybrid microfluidic colorimetry-based creatinine detection and pH indicator platform can be beneficial in the healthcare sector due to the on-site testing capability, cost-effectiveness, ease of use, robustness, and instrument-free approach.

This study introduces the development of a photothermal-based microfluidic paper analytical device (PT-µPAD) integrated with near-field communication (NFC) technology and smartphone readout for enzyme-free glucose quantification in human samples. With the properties of gold nanoparticles (AuNPs) both as a nanozyme and as a photothermal substrate, there is no need for costly reagents like enzymes or a readout instrumentation for the selective and sensitive detection of glucose. In PT-µPADs, AuNPs are etched by hydrogen peroxide (H 2 O 2 ) generated from glucose catalysis. Photothermal detection from the plasmonic heating of these AuNPs when illuminated by a 533nm LED light source is achieved by inserting the PT-µPAD sensor into a portable NFC platform suitable for smartphone readout. Temperature variation is directly proportional to the glucose concentration. After optimization, we acquired a linear range between 5.0 and 20.0 µmol L -1 ( R 2 = 0.9967) and a limit of detection (LOD) of 25.0 nmol L -1 for glucose. Additionally, while our sensor does not utilize any enzyme, it is remarkably selective to glucose with no effects from interferences. Recovery studies in various human control samples indicated a range of 99.73-102.66% with the highest RSD of 3.53%, making it highly accurate and precise. Moreover, our method is more sensitive than other methods relying on conventional µPADs for glucose sensing. By integrating the potential benefits of microfluidics, nanomaterials as nanozymes, and NFC technology for wireless readout, our sensor demonstrates great promise as an accessible, affordable, and shelf-stable device for glucose quantification. Moreover, this concept can be extended to detect other molecules of interest as a point-of-care (POC) diagnostics device.

We reported a colorimetric paper-based device by integrating the modified acid RNA-cleaving DNAzymes (MaRCD-EC1) for highly sensitive (detection limit = 10 2 CFU mL -1 ), and rapid (within 30 min) detection of E. coli without amplification. This device exhibited a clinical sensitivity of 100% and a specificity of 100% in identifying E. coli -associated urinary tract infections (UTIs) using the clinical urine samples.

Safe, accurate, and reliable analysis of urinary biomarkers is clinically important for early detection and monitoring of the progression of chronic kidney disease (CKD), as it has become one of the world's most prevalent non-communicable diseases. However, current technologies for measuring urinary biomarkers are either time-consuming and limited to well-equipped hospitals or lack the necessary sensitivity for quantitative analysis and post a health risk to frontline practitioners. Here we report a robust paper-based dual functional biosensor, which is integrated with the clinical urine sampling vial, for the simultaneous and quantitative analysis of pH and glucose in urine. The pH sensor was fabricated by electrochemically depositing IrOx onto a paper substrate using optimised parameters, which enabled an ultrahigh sensitivity of 71.58 mV pH -1 . Glucose oxidase (GOx) was used in combination with an electrochemically deposited Prussian blue layer for the detection of glucose, and its performance was enhanced by gold nanoparticles (AuNPs), chitosan, and graphite composites, achieving a sensitivity of 1.5 μA mM -1 . This dual function biosensor was validated using clinical urine samples, where a correlation coefficient of 0.96 for pH and 0.98 for glucose detection was achieved with commercial methods as references. More importantly, the urine sampling vial was kept sealed throughout the sample-to-result process, which minimised the health risk to frontline practitioners and simplified the diagnostic procedures. This diagnostic platform, therefore, holds high promise as a rapid, accurate, safe, and user-friendly point-of-care (POC) technology for the analysis of urinary biomarkers in frontline clinical settings.

A new analytical method for chronic kidney disease (CKD) detection utilizing paper spray mass spectrometry (PS-MS) combined with machine learning is presented. The analytical protocol is rapid and simple, based on metabolic profile alterations in urine. Anonymized raw urine samples were deposited (10 μL each) onto pointed PS-MS sample strips. Without waiting for the sample to dry, 75 μL of acetonitrile and high voltage were applied to the strips, using high resolution mass spectrometry measurement (15 s per sample) with polarity switching to detect a wide range of metabolites. Random forest machine learning was used to classify the resulting data. The diagnostic performance for the potential diagnosis of CKD was evaluated for accuracy, sensitivity, and specificity, achieving results >96% for the training data and >91% for validation and test data sets. Metabolites selected by the classification model as up- or down-regulated in healthy or CKD samples were tentatively identified and in agreement with previously reported literature. The potential utilization of this approach to discriminate albuminuria categories (normo, micro, and macroalbuminuria) was also demonstrated. This study indicates that PS-MS combined with machine learning has the potential to be used as a rapid and simple diagnostic tool for CKD.