Search

A rapid and simultaneous lateral flow immunochromatography (LFIC) method was established based on colloidal gold (CG) for testing three addictive drugs in milk tea, including methamphetamine (MET), ketamine (KET), and cocaine (COC).After optimizing the key parameters, such as the concentration of coated antigen, the combination position of the three analytes, and the immunoreaction time, the developed method could be used for the simultaneous identification of three target addictive drays in a milk tea sample with a lower concentration could be tested.The limits of detection of MET, KET, and COC were 1.60, 1.59, and 1.39 ng/mL, respectively.In milk tea samples, the mean recoveries of the three types of mixed reference analytes ranged from 90% to 109% with a relative standard deviation of less than 5%.Briefly, the multiple CG-LFIC method had the advantages of easy operation, fast analysis, high sensitivity, and good specificity.It can be an effective method for simultaneous screening addictive drugs in milk tea and provides promising applications in the bio-detection field. [ABSTRACT FROM AUTHOR]

Supplemental Digital Content is Available in the Text. Background: For patients with multiple sclerosis (MS), both structure and microvasculature alterations in the inner retina have been investigated in several studies. However, little is known about the alterations in the outer retina and choroid. Hence, this study aimed to assess the outer retinal and choroidal changes in patients with MS with no history of optic neuritis (ON). Methods: Patients with MS and healthy control participants were enrolled in this cross-sectional study. Quantitative analyses were performed using swept source optical coherence tomography and swept source optical coherence tomography angiography images to assess outer retina thickness (ORT) and choroid thickness (CT), vessel density (VD) of choriocapillaris, and choroidal vascularity index (CVI), which were then compared between the groups. Results: A total of 37 participants with MS (72 eyes) and 74 healthy control participants (148 eyes) were included in this study. Compared with healthy controls, patients with MS with no history of ON showed reduced VD of the choriocapillaris and CVI. There was no significant difference in ORT and CT between 2 groups. Meanwhile, in patients with MS, no correlation between OCTA parameters and expanded disability status scale score were found in this study. Conclusions: Our study indicates that patients with MS with no history of optical neuritis have reduced choriocapillaris vessel density and decreased choroidal vascularity index without detectable alteration in outer retina thickness and choroid thickness. The findings complement the outer retinal and choroidal component of MS, providing deeper insight into the pathophysiology of MS. [ABSTRACT FROM AUTHOR]

This paper presents a novel deep graph-based learning technique for speech emotion recognition which has been specifically tailored for energy efficient deployment within humanoid robots. Our methodology represents a fusion of scalable graph representations, rooted in the foundational principles of graph signal processing theories. By delving into the utilization of cycle or line graphs as fundamental constituents shaping a robust Graph Convolution Network (GCN)-based architecture, we propose an approach which allows the capture of relationships between speech signals to decode intricate emotional patterns and responses. Our methodology is validated and benchmarked against established databases such as IEMOCAP and MSP-IMPROV. Our model outperforms standard GCNs and prevalent deep graph architectures, demonstrating performance levels that align with state-of-the-art methodologies. Notably, our model achieves this feat while significantly reducing the number of learnable parameters, thereby increasing computational efficiency and bolstering its suitability for resource-constrained environments. This proposed energy-efficient graph-based hybrid learning methodology is applied towards multimodal emotion recognition within humanoid robots. Its capacity to deliver competitive performance while streamlining computational complexity and energy efficiency represents a novel approach in evolving emotion recognition systems, catering to diverse real-world applications where precision in emotion recognition within humanoid robots stands as a pivotal requisite. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have enjoyed much success in many applications, such as computer vision, automated medical diagnosis, autonomous systems, etc. Another application of DCNNs is for game strategies, where the deep neural network architecture can be used to directly represent and learn strategies from expert players on different sides. Many game states can be expressed not only as a matrix data structure suitable for DCNN training but also as a graph data structure. Most of the available DCNN methods ignore the territory characteristics of both sides' positions based on the game rules. Therefore, in this paper, we propose a hybrid approach to the graph neural network to extract the features of the model of game-playing strategies and fuse it into a DCNN. As a graph learning model, graph convolutional networks (GCNs) provide a scheme by which to extract the features in a graph structure, which can better extract the features in the relationship between the game-playing strategies. We validate the work and design a hybrid network to integrate GCNs and DCNNs in the game of Go and show that on the KGS Go dataset, the performance of the hybrid model outperforms the traditional DCNN model. The hybrid model demonstrates a good performance in extracting the game strategy of Go. [ABSTRACT FROM AUTHOR]

Simple Summary: Porcine epidemic diarrhea virus (PEDV), porcine bocavirus (PBoV), and porcine rotavirus (PoRV) are associated with porcine viral diarrhea. The three viruses are often comorbid in mixed infection; therefore, it is necessary to develop a specific and rapid method for simultaneous detection. In this study, a multiple detection method, loop-mediated isothermal amplification (LAMP) combined with a lateral flow dipstick (LFD), was established to detect three pathogens in 125 field samples. The consistency between the real-time fluorescence quantitative PCR (rt-qPCR) and the triplex LAMP–LFD assay was over 99%. The assay has been proven to be highly specific and sensitive. Porcine epidemic diarrhea virus (PEDV), porcine bocavirus (PBoV), and porcine rotavirus (PoRV) are associated with porcine viral diarrhea. In this study, triplex loop-mediated isothermal amplification (LAMP) combined with a lateral flow dipstick (LFD) was established for the simultaneous detection of PEDV, PoRV, and PBoV. The PEDV-gp6, PoRV-vp6, and PBoV-vp1 genes were selected to design LAMP primers. The amplification could be carried out at 64 °C using a miniature metal bath within 30 min. The triplex LAMP–LFD assay exhibited no cross-reactions with other porcine pathogens. The limits of detection (LODs) of PEDV, PoRV, and PBoV were 2.40 × 101 copies/μL, 2.89 × 101 copies/μL, and 2.52 × 101 copies/μL, respectively. The consistency between rt-qPCR and the triplex LAMP–LFD was over 99% in field samples testing. In general, the triplex LAMP–LFD assay was suitable for the rapid and simultaneous detection of the three viruses in the field. [ABSTRACT FROM AUTHOR]

The five recognized zoonotic foodborne pathogens, namely, Listeria monocytogenes, Staphylococcus aureus, Streptococcus suis, Salmonella enterica and Escherichia coli O157:H7, pose a major threat to global health and social–economic development. These pathogenic bacteria can cause human and animal diseases through foodborne transmission and environmental contamination. Rapid and sensitive detection for pathogens is particularly important for the effective prevention of zoonotic infections. In this study, rapid and visual europium nanoparticle (EuNP)-based lateral flow strip biosensors (LFSBs) combined with recombinase polymerase amplification (RPA) were developed for the simultaneous quantitative detection of five foodborne pathogenic bacteria. Multiple T lines were designed in a single test strip for increasing the detection throughput. After optimizing the key parameters, the single-tube amplified reaction was completed within 15 min at 37 °C. The fluorescent strip reader recorded the intensity signals from the lateral flow strip and converted the data into a T/C value for quantification measurement. The sensitivity of the quintuple RPA-EuNP-LFSBs reached a level of 101 CFU/mL. It also exhibited good specificity and there was no cross-reaction with 20 non-target pathogens. In artificial contamination experiments, the recovery rate of the quintuple RPA-EuNP-LFSBs was 90.6–101.6%, and the results were consistent with those of the culture method. In summary, the ultrasensitive bacterial LFSBs described in this study have the potential for widespread application in resource-poor areas. The study also provides insights in respect to multiple detection in the field. [ABSTRACT FROM AUTHOR]