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A critical review on effects of artificial sweeteners on gut microbiota and gastrointestinal health.
J Sci Food Agric
January 2025
Food Science and Technology Program, Department of Life Sciences, BNU-HKBU United International College, Zhuhai, China.
Artificial sweeteners have emerged as popular alternatives to traditional sweeteners, driven by the growing concern over sugar consumption and its associated rise in obesity and metabolic disorders. Despite their widespread use, the safety and health implications of artificial sweeteners remain a topic of debate, with conflicting evidence contributing to uncertainty about their long-term effects. This review synthesizes current scientific evidence regarding the impact of artificial sweeteners on gut microbiota and gastrointestinal health.
View Article and Find Full Text PDFRecognizing drivers' sleep onset by detecting slow eye movement using a parallel multimodal one-dimensional convolutional neural network.
Comput Methods Biomech Biomed Engin
January 2025
School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, Changzhou University, Changzhou, P.R. China.
Slow eye movements (SEMs) are a reliable physiological marker of drivers' sleep onset, often accompanied by EEG alpha wave attenuation. A parallel multimodal 1D convolutional neural network (PM-1D-CNN) model is proposed to classify SEMs. The model uses two parallel 1D-CNN blocks to extract features from EOG and EEG signals, which are then fused and fed into fully connected layers for classification.
View Article and Find Full Text PDFCan Artificial Intelligence and Machine Learning Transform Prediction and Treatment of Post-Transjugular Intrahepatic Portosystemic Shunt (TIPS) Overt Hepatic Encephalopathy?
Gastro Hep Adv
September 2024
Blacktown Clinical School, School of Medicine, Western Sydney University, Penrith, New South Wales, Australia.
Integrating AI·IoT-OAHPs with existing elderly care systems.
Digit Health
January 2025
School of Public Affairs, Zhejiang University, Hangzhou, China.
This letter addresses the integration of artificial intelligence and the Internet of Things-based older adult healthcare programs with existing community and institutional elderly care systems. It highlights the current disconnect leading to service duplication and resource inefficiencies, proposes multifaceted integration approaches, and underscores the importance of supportive policies. International examples are referenced to demonstrate successful models, emphasizing the need for coordinated care to enhance service delivery and optimize resource use.
View Article and Find Full Text PDFA Deep Learning Framework for Automated Classification and Archiving of Orthodontic Diagnostic Documents.
Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
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