An amperometric micro glucose biosensor based on the hybrid bio-organic–inorganic composite sensitive membrane by which electrostatic absorption of glucose oxidase (GOD) in polyaniline (PAN) film on iridium oxide (Ir2O3 film electrode has been fabricated. The microelectrode exhibited a linear relationship with the concentration of glucose in the range of 0.01∼14 mM with a detection limit of 2.4×10−6 M. Iridium oxide electrode exhibits obviously electro-catalytic activity towards reduction of hydrogen peroxide (H2O2 at a low reductive potential. Ascorbic acid, uric acid and other oxidizable compounds coexisted (with glucose) in the sample did not cause interference to the determination. The selectivity, response time, reproducibility and lifetime of this micro biosensor are discussed. It has been used as a detector of flow-injection analysis (FIA) for glucose assay in blood serum directly.
The rising demand for point-of-care testing (POCT) in disease diagnosis has made LFIA sensors based on dendritic metal thin film (HD-nanometal) and background fluorescence technology essential for rapid and accurate disease marker detection, thanks to their integrated design, high sensitivity, and cost-effectiveness. However, their unique 3D nanostructures cause significant fluorescence variation, challenging traditional image processing methods in segmenting weak fluorescence regions. This paper develops a deep learning method to efficiently segment target regions in HD-nanometal LFIA sensor images, improving quantitative detection accuracy. We propose an improved UNet++ network with attention and residual modules, accurately segmenting varying fluorescence intensities, especially weak ones. We evaluated the method using IoU and Dice coefficients, comparing it with UNet, Deeplabv3, and UNet++. We used an HD-nanoCu-Ni LFIA sensor for cardiac troponin I (cTnI) as a case study to validate the method's practicality. The proposed method achieved a 96.3% IoU, outperforming other networks. The
Flow measurement immunochromatography has been widely used in the field of medical testing due to its advantages of simplicity, speed, convenience and low cost. The combination of machine vision image processing technology and test strip technology has become a research hotspot for rapid quantitative detection in recent years. This paper reviews the research progress of flow measurement immunochromatography to improve the accuracy in the past five years, evaluates the advantages and disadvantages of flow measurement immunochromatography detection from the perspectives of test strip labelling methods, image segmentation process and deep learning algorithms, and analyses its future development direction.
The traditional lateral flow immunoassay (LFIA) detection method suffers from issues such as unstable detection results and low quantitative accuracy. In this study, we propose a novel multi-test line lateral flow immunoassay quantitative detection method using smartphone-based SAA immunoassay strips. Following the utilization of image processing techniques to extract and analyze the pigments on the immunoassay strips, quantitative analysis of the detection results was conducted. Experimental setups with controlled lighting conditions in a dark box were designed to capture samples using smartphones with different specifications for analysis. The algorithm's sensitivity and robustness were validated by introducing noise to the samples, and the detection performance on immunoassay strips using different algorithms was determined. The experimental results demonstrate that the proposed lateral flow immunoassay quantitative detection method based on image processing techniques achieves an accuracy rate of 94.23% on 260 samples, which is comparable to the traditional methods but with higher stability and lower algorithm complexity.
Abstract Luminescent cadmium(II) (8‐hydroxyquinoline) chloride (CdqCl) complex nanowires are synthesized via a sonochemical solution route. The results of X‐ray photoelectron spectroscopy, energy dispersive X‐ray analysis, infrared spectroscopy, elemental analysis (EA), and atomic absorption spectroscopy demonstrate that the chemical composition of the product is Cd(C 9 H 6 NO)Cl. Transmission electron microscopy and scanning electron microscopy images show that the CdqCl product is wire‐like in structure, with a diameter of approximately 50 nm and an approximate length of 2–4 µm. The morphology and composition of the product can be transformed from Cdq 2 micrometer‐scaled flakes to CdqCl nanowires by increasing the ratio of CdCl 2 /q. A new fluorescent sensing strategy for detecting H 2 O 2 and glucose is developed and is based on the combination of the luminescent nanowires and the biocatalytic growth of Au nanoparticles. The quenching effects of Au nanoparticles and ${\rm AuCl}_4^ - $ on the fluorescence of CdqCl nanowires are investigated. The dominant factor for the fluorescence quenching of CdqCl nanowires is that the Stern–Volmer quenching constant of Au nanoparticles is larger than that of ${\rm AuCl}_4^ - $ .
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