Dominguez, Rodrigo E

Urinary urea is a key biomarker for tracking protein oxidation and nitrogen balance. A persistent analytical limitation is that conventional total-nitrogen assays do not deconvolute urinary urea from urinary ammonia, and direct measurement still requires capital-intensive clinical analyzers. Here, we implement and independently validate two cost-effective methods that resolve urinary urea by pairing the presence and absence of urease: Method 1 is a urease-coupled potentiometric assay using an ammonium ion-selective electrode (ISE method); Method 2 is a microplate Berthelot–salicylate colorimetric assay employing a repurposed consumer-grade aquarium ammonia test kit (API®) (API method). Each method was validated against the Roche Cobas C311 clinical analyzer (ISE for urinary ammonia and API for urinary urea) in healthy adult volunteers (n = 10 each), and API–ISE were then compared head-to-head for urinary urea. ISE vs. Cobas yielded R2 = 0.967, slope 95% CI [0.867, 1.173], and Bland–Altman bias of −1.02 mM (95% LoA [−7.40, +5.36] mM). API vs. Cobas yielded R2 = 0.973, slope 95% CI [0.790, 1.039], and bias of −1.55 mM (95% LoA [−11.56, +8.46] mM). API–ISE urinary urea comparison confirmed mutual agreement (R2 = 0.9976, slope 95% CI [0.946, 1.024]; bias of +3.47 mM; 95% LoA [−14.60, +21.53] mM), with mean recoveries near 100% (API: 100.7 ± 2.9%; ISE: 100.6 ± 5.4%; 20–350 mM). Both methods agreed with the clinical analyzer, supporting their potential for decentralized, resource-limited testing; clinical and point-of-care validations are needed.

Acute kidney injury (AKI) affects thousands of hospitalized patients annually, yet early detection remains challenging as serum creatinine elevation lags behind clinical deterioration. Decreased urine output (UO) represents a key diagnostic criterion of AKI, sometimes manifesting hours before biochemical changes; however, current manual monitoring methods are labor-intensive and prone to error. Here, we developed and validated a simple, cost-effective automated urine flow meter using non-contact optical sensors, a peristaltic pump, and microcontroller-based automation for precise, real-time monitoring of urine output in clinical settings, named P-meter. Three successive prototypes (V1, V2, V3) were validated against gold-standard gravimetric measurements over 285 h of testing during animal experiments that required bladder catheterization. Iterative refinement addressed miniaturization challenges, fluid dynamics optimization, and sensor positioning to achieve progressively improved accuracy. The optimized V3 prototype demonstrated further enhanced volumetric precision, stability, and flow accuracy with near-unity linearity vs. reference method (R2 = 0.9889), minimal bias (mean error −0.1 mL), and 94.18% agreement within confidence limits (n = 86), outperforming the initial V1 prototype (R2 = 0.9971, mean error −1.69 mL, n = 207) and intermediate V2 design (R2 = 0.9941, mean error 3.63 mL, n = 390), primarily in terms of reduced bias and improved agreement. The P-meter offers accurate urine output monitoring at a lower cost than commercial systems, facilitating its use in early AKI detection and thereby improving patient outcomes.

Ketones, which are key biomarkers of fat oxidation, are relevant for metabolic health maintenance and disease development, making continuous monitoring essential. In this study, we introduce a novel colorimetric sensor designed for potential continuous acetone detection in biological fluids. The sensor features a polydimethylsiloxane (PDMS) shell that encapsulates a sensitive and specific liquid-core acetone-sensing probe. The microsphere sensors were characterized by evaluating their size, PDMS shell thickness, colorimetric response, and sensitivity under realistic conditions, including 100% relative humidity (RH) and CO2 interference. The microsphere size and sensor sensitivity can be controlled by modifying the fabrication parameters. Critically, the sensor showed high selectivity for acetone detection, with negligible interference from CO2 concentrations up to 4%. In addition, the sensor displayed good reproducibility (CV < 5%) and stability under realistic storage conditions (over two weeks at 4 °C). Finally, the accuracy of the microsphere sensor was validated against a gold standard gas chromatography-mass spectrometry (GC-MS) method using simulated and real breath samples from healthy individuals and type 1 diabetes patients. The correlation between the microsphere sensor and GC-MS produced a linear fit with a slope of 0.948 and an adjusted R-squared value of 0.954. Therefore, the liquid-core microsphere-based sensor is a promising platform for acetone body fluid analysis.