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A miniaturized colorimetric-photoacoustic dual-mode device for point-of-care testing of ascorbic acid and ascorbate oxidase.
Anal Chim Acta
March 2025
The Radiology Department of Shanxi Provincial People's Hospital Affiliated to Shanxi Medical University, Taiyuan, 030001, China. Electronic address:
Background: Real-time and rapid detection of ingredients in food has important significance for food safety. However, traditional detection methods not only require bulky and costly instruments but also are often based on single-mode analysis, limiting their accuracy and applications in point-of-care testing. Herein, an integrated and miniaturized dual-mode device based on colorimetric and photoacoustic (PA) principles is developed, using Au@Ag nanoparticles (Au@AgNPs) as signal probe and ascorbic acid (AA) and ascorbate oxidase (AAO) as analytes.
View Article and Find Full Text PDFDetection of biogenic amines using a ylidenemalononitrile enamine-based fluorescence probe: Applications in food quality control.
Spectrochim Acta A Mol Biomol Spectrosc
January 2025
Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210046 China. Electronic address:
Biogenic amines (BAs) are a class of nitrogen-containing natural organic compounds. Elevated levels of BAs are a reliable indicator of food spoilage and pose a significant risk to human health. Thus, the development of real-time sensors for BAs monitoring is crucial.
View Article and Find Full Text PDFSimplified self-supervised learning for hybrid propagation graph-based recommendation.
Neural Netw
January 2025
School of Big Data & Software Engineering, Chongqing University, Chongqing, 401331, China. Electronic address:
Recent progress in Graph Convolutional Networks (GCNs) has facilitated their extensive application in recommendation, yielding notable performance gains. Nevertheless, existing GCN-based recommendation approaches are confronted with several challenges: (1) how to effectively leverage multi-order graph connectivity to derive meaningful node embeddings; (2) faced with sparse raw data, how to augment supervision signals without relying on auxiliary information; (3) given that GCNs necessitate the aggregation of neighborhood nodes, and the sparsity of these nodes can exacerbate the impact of noise data, how to mitigate the noise problem inherent in the raw data. For tackling aforementioned challenges, we devise a new hybrid propagation GCN-based method named S3HGN, incorporating a simplified self-supervised learning paradigm for recommendation.
View Article and Find Full Text PDFThe TOXIN knowledge graph: supporting animal-free risk assessment of cosmetics.
Database (Oxford)
January 2025
Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium.
The European Union's ban on animal testing for cosmetic products and their ingredients, combined with the lack of validated animal-free methods, poses challenges in evaluating their potential repeated-dose organ toxicity. To address this, innovative strategies like Next-Generation Risk Assessment (NGRA) are being explored, integrating historical animal data with new mechanistic insights from non-animal New Approach Methodologies (NAMs). This paper introduces the TOXIN knowledge graph (TOXIN KG), a tool designed to retrieve toxicological information on cosmetic ingredients, with a focus on liver-related data.
View Article and Find Full Text PDFIn the field of image processing, optical neural networks offer advantages such as high speed, high throughput, and low energy consumption. However, most existing coherent optical neural networks (CONN) rely on coherent light sources to establish transmission models. The use of laser inputs and electro-optic modulation devices at the front end of these neural networks diminishes their computational capability and energy efficiency, thereby limiting their practical applications in object detection tasks.
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