A microfluidic immuno-biosensor detection system consisting of a microfluidic spectrum chip and a micro-spectrometer detection device is presented for the rapid point-of-care (POC) detection and quantification of high-sensitivity C-reactive protein (hs-CRP) in urine. The detection process utilizes a highly specific enzyme-linked immunosorbent assay (ELISA) method, in which capture antibodies and detection antibodies are pre-deposited on the substrate of the microchip and used to form an immune complex with the target antigen. Horseradish peroxidase (HRP) is added as a marker enzyme, followed by a colorimetric reaction using 3,3′,5,5′-tetramethylbenzidine (TMB). The absorbance values (a.u.) of the colorimetric reaction compounds are measured using a micro-spectrometer device and used to measure the corresponding hs-CRP concentration according to the pre-established calibration curve. It is shown that the hs-CRP concentration can be determined within 50 min. In addition, the system achieves recovery rates of 93.8–106.2% in blind water samples and 94.5–104.6% in artificial urine. The results showed that the CRP detection results of 41 urine samples from patients with chronic kidney disease (CKD) were highly consistent with the conventional homogeneous particle-enhanced turbidimetric immunoassay (PETIA) method’s detection results (R² = 0.9910). The experimental results showed its applicability in the detection of CRP in both urine and serum. Overall, the results indicate that the current microfluidic ELISA detection system provides an accurate and reliable method for monitoring the hs-CRP concentration in point-of-care applications.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that causes extensive damage to multiple organs and tissues and has no known cure. This study introduces a microfluidic detection platform that combines a microfluidic reaction chip with a micro-spectrometer to accurately detect the anti-cyclic citrullinated peptide antibody (anti-CCP Ab) biomarker, commonly associated with arthritis. The surface of the microfluidic reaction chip is functionalized using streptavidin to enable the subsequent immobilization of biotinylated-labeled cyclic citrullinated peptide (biotin–CCP) molecules through a streptavidin–biotin reaction. The modified chip is then exposed to anti-CCP Ab, second antibody conjugated with horseradish peroxidase (HRP) (2nd Ab-HRP), 3,3′,5,5′-tetramethylbenzidine (TMB), and a stop solution. Finally, the concentration of the anti-CCP Ab biomarker is determined by analyzing the optical density (OD) of the colorimetric reaction product at 450 nm using a micro-spectrometer. The detection platform demonstrated a strong correlation (R² = 0.9966) between OD and anti-CCP Ab concentration. This was based on seven control samples with anti-CCP Ab concentrations ranging from 0.625 to 100 ng/mL. Moreover, for 30 artificial serum samples with unknown anti-CCP Ab concentrations, the biosensor achieves a correlation coefficient of (R² = 0.9650). The proposed microfluidic detection platform offers a fast and effective method for accurately identifying and quantifying the anti-CCP Ab biomarker. Thus, it offers a valuable tool for the early diagnosis and monitoring of RA and its progression in point-of-care settings.

Electrokinetic instabilities in a shear thinning fluid start at a smaller electric field and an earlier location than in a Newtonian fluid but with a smaller wave amplitude and frequency.

Electrokinetic instabilities (EKIs) occur when a sufficiently large direct current electric field interacts with a fluid flow with conductivity gradients. While EKIs in Newtonian fluids have been extensively studied for microfluidic applications such as micromixing and sample stacking, the impact of fluid rheology has not been thoroughly explored. This work reports an experimental study on how fluid elasticity affects EKI in hyaluronic acid (HA) solutions that are essentially Boger fluids under experimental conditions. It is found that increasing HA concentration (i.e., more viscoelastic) raises the threshold electric field for the onset of EKI, leading to a higher critical electric Rayleigh number. This observation indicates the suppression effect of fluid elasticity on EKI, which validates a recent numerical prediction based on the Oldroyd-B model [Sasmal, “Fluid viscoelasticity suppresses chaotic convection and mixing due to electrokinetic instability,” Phys. Fluids 34, 082011 (2022)]. Increasing the fluid elasticity also weakens the magnitude of EKI waves but has an insignificant influence on the wave speed and frequency, in contrast to the increasing electroosmotic velocity in higher-concentration HA solutions.

A novel microfluidic ractopamine (RAC) detection platform consisting of a microfluidic RAC chip and a smart analysis device is proposed for the determination of RAC concentration in meat samples. This technology utilizes gold nanoparticles (AuNPs) modified with glutamic acid (GLU) and polyethyleneimine (PEI) to measure RAC concentration in food products. When RAC is present, AuNPs aggregate through hydrogen bonding, causing noticeable changes in their optical properties, which are detected using a self-built UV–visible micro-spectrophotometer. Within the range of 5 to 80 ppb, a linear relationship exists between the absorbance ratio (A_693nm/A_518nm) (Y) and RAC concentration (X), expressed as Y = 0.0054X + 0.4690, with a high coefficient of determination (R² = 0.9943). This method exhibits a detection limit of 1.0 ppb and achieves results within 3 min. The practical utility of this microfluidic assay is exemplified through the evaluation of RAC concentrations in 50 commercially available meat samples. The variance between concentrations measured using this platform and those determined via liquid chromatography–tandem mass spectrometry (LC-MS/MS) is less than 8.33%. These results underscore the viability of the microfluidic detection platform as a rapid and cost-effective solution for ensuring food safety and regulatory compliance within the livestock industry.

Nervous necrosis virus (NNV) continues to challenge aquaculture health management, where diagnosis commonly relies on tissue collection, laboratory instrumentation, and trained personnel. Such requirements make routine, non-destructive surveillance difficult in production environments. A microfluidic RT-RPA–LFD platform was therefore combined with machine-learning-assisted sampling-site selection for direct detection of NNV in culture water. Test-line signals were interpreted by RGB analysis, while sampling locations were selected using information on concentration dynamics within the tank. Primer and Mg²⁺ concentrations, reaction temperature, and readout time were optimized before analytical evaluation. The assay remained positive at 2.5 × 10⁻² copies/μL, the lowest nominal NNV concentration tested, whereas neither of the two non-target fish viruses generated a detectable signal. RGB analysis provided a C/T cutoff for binary interpretation. Applied to 30 blind-coded water samples, the cutoff yielded complete agreement with independently assigned reference labels, corresponding to 100% accuracy in this test set. To improve sampling representativeness under dynamic tank conditions, nitrite was used as a hydrodynamic tracer, and five time-series learning models were evaluated for concentration-field prediction. Among the evaluated architectures (LSTM, GRU, ConvLSTM, ConvGRU, and GAN), the GAN produced the smallest prediction error, with concentration deviation remaining below 2 ppm after 12 min. Sampling sites for biological validation were prioritized by combining model forecasts with observed aggregation behavior. In infected-grouper tank trials, NNV was detectable in water from day 4 onward, with infection independently confirmed by RT-qPCR analysis of kidney and spleen tissues. Together, these results establish a non-invasive, data-assisted surveillance framework that may support earlier intervention during emerging viral outbreaks in aquaculture systems.