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
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.
The colorimetric method, due to its rapid and low-cost characteristics, demonstrates a wide range of application prospects in on-site water quality testing. Current research on colorimetric detection using deep learning algorithms predominantly focuses on single-target classification. To address this limitation, we propose a multi-task water quality colorimetric detection method based on YOLOv8n, leveraging deep learning techniques to achieve a fully automated process of "image input and result output". Initially, we constructed a dataset that encompasses colorimetric sensor data under varying lighting conditions to enhance model generalization. Subsequently, to effectively improve detection accuracy while reducing model parameters and computational load, we implemented several improvements to the deep learning algorithm, including the MGFF (Multi-Scale Grouped Feature Fusion) module, the LSKA-SPPF (Large Separable Kernel Attention-Spatial Pyramid Pooling-Fast) module, and the GNDCDH (Group Norm Detail Convolution Detection Head). Experimental results demonstrate that the optimized deep learning algorithm excels in precision (96.4%), recall (96.2%), and mAP50 (98.3), significantly outperforming other mainstream models. Furthermore, compared to YOLOv8n, the parameter count and computational load were reduced by 25.8% and 25.6%, respectively. Additionally, precision improved by 2.8%, recall increased by 3.5%, mAP50 enhanced by 2%, and mAP95 rose by 1.9%. These results affirm the substantial potential of our proposed method for rapid on-site water quality detection, offering new technological insights for future water quality monitoring.
Double Ig IL-1R related molecule (DIGIRR), myeloid differentiation factor 88 (MyD88), interleukin-1 receptor-associated kinase-1 (IRAK-1), tumor necrosis factor receptor-associated factor 6 (TRAF6), and Interferon-c (IFNc), are all signaling molecules involved in Toll-like receptor (TLR)-dependent inflammation. In this study, Chinese sturgeon DIGIRR, MyD88, IRAK1, TRAF6, and IFNc expression levels in immune tissues (blood, spleen, liver, head kidney and middle kidney) were upregulated in response to Citrobacter freundii infection. DIGIRR were highest in the head kidney, MyD88, IRAK1 and TRAF6 were highest in the liver, and IFNc were highest in the blood, respectively. The expression levels of DIGIRR and other genes sturgeons challenged with Plesiomonas shigelloides were similar to those challenged with C. freundii, except that DIGIRR expression was highest in the liver. Surprisingly, the expression levels of the genes after Mycobacterium marinum challenge decreased significantly, DIGIRR, MyD88, IRAK1 were lowest in the liver, TRAF6 were lowest in the blood and IFNc were lowest in the head kidney, respectively. Taken together, the expression levels of DIGIRR and other genes in Chinese sturgeon were upregulated significantly after challenge with C. freundii and P. shigelloides, but decreased significantly by M. marinum. Although their reactions varied, these findings indicate that the DIGIRR signaling pathway in Chinese sturgeon is involved the inflammatory response to diverse bacterial infection.
This study aims to assess the impact of the nationwide Omicron outbreak in December 2022 on Chinese patients with plasma cell disorders (PCD), focusing on the clinical characteristics of PCD patients with COVID-19 and the risk factors contributing to adverse clinical courses (severity and hospitalization) and outcomes.
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