Two years after the coronavirus disease 2019(COVID-19)outbreak,an increasing number of patients continue to suffer from long COVID(LC),persistent symptoms,and/or delayed or long-term complications beyond the initial 4...Two years after the coronavirus disease 2019(COVID-19)outbreak,an increasing number of patients continue to suffer from long COVID(LC),persistent symptoms,and/or delayed or long-term complications beyond the initial 4 weeks from the onset of symptoms.Constant fatigue is one of the most common LC symptoms,leading to severely reduced quality of life among patients.Ginseng Radix et Rhizoma—known as the King of Herbs in traditional Chinese medicine—has shown clinical anti-fatigue effects.In this review,we summarize the underlying anti-fatigue mechanisms of Ginseng Radix et Rhizoma extracts and their bioactive compounds,with a special focus on anti-viral,immune remodeling,endocrine system regulation,and metabolism,suggesting that Ginseng Radix et Rhizoma is a potentially promising treatment for LC,especially regarding targeting fatigue.展开更多
Objective:To establish a deep-learning architecture based on faster region-based convolutional neural networks(Faster R-CNN)algorithm for detection and sorting of red ginseng(Ginseng Radix et Rhizoma Rubra)with intern...Objective:To establish a deep-learning architecture based on faster region-based convolutional neural networks(Faster R-CNN)algorithm for detection and sorting of red ginseng(Ginseng Radix et Rhizoma Rubra)with internal defects automatically on an online X-ray machine vision system.Methods:A Faster R-CNN based classifier was trained with around 20000 samples with mean average precision value(mAP)of 0.95.A traditional image processing method based on feedforward neural network(FNN)obtained a bad performance with the accuracy,recall and specificity of 69.0%,68.0%,and70.0%,respectively.Therefore,the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system.Results:An independent set of 2000 red ginsengs were used to validate the performance of the Faster RCNN based online sorting system in three parallel tests,achieving accuracy of 95.8%,95.2%and 96.2%,respectively.Conclusion:The overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.展开更多
基金funded by the Science&Technology Development Fund of the Tianjin Education Commission for Higher Education(2018KJ010)。
文摘Two years after the coronavirus disease 2019(COVID-19)outbreak,an increasing number of patients continue to suffer from long COVID(LC),persistent symptoms,and/or delayed or long-term complications beyond the initial 4 weeks from the onset of symptoms.Constant fatigue is one of the most common LC symptoms,leading to severely reduced quality of life among patients.Ginseng Radix et Rhizoma—known as the King of Herbs in traditional Chinese medicine—has shown clinical anti-fatigue effects.In this review,we summarize the underlying anti-fatigue mechanisms of Ginseng Radix et Rhizoma extracts and their bioactive compounds,with a special focus on anti-viral,immune remodeling,endocrine system regulation,and metabolism,suggesting that Ginseng Radix et Rhizoma is a potentially promising treatment for LC,especially regarding targeting fatigue.
基金funded by National Natural Science Foundation of China(Grant No.82074276)Projects of International Cooperation of Traditional Chinese Medicine(Grant No.06102040NF020928)+1 种基金National S&T Major Project of China(Grant No.2018ZX09201011)Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine.(No.ZYYCXTD-D-202002)。
文摘Objective:To establish a deep-learning architecture based on faster region-based convolutional neural networks(Faster R-CNN)algorithm for detection and sorting of red ginseng(Ginseng Radix et Rhizoma Rubra)with internal defects automatically on an online X-ray machine vision system.Methods:A Faster R-CNN based classifier was trained with around 20000 samples with mean average precision value(mAP)of 0.95.A traditional image processing method based on feedforward neural network(FNN)obtained a bad performance with the accuracy,recall and specificity of 69.0%,68.0%,and70.0%,respectively.Therefore,the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system.Results:An independent set of 2000 red ginsengs were used to validate the performance of the Faster RCNN based online sorting system in three parallel tests,achieving accuracy of 95.8%,95.2%and 96.2%,respectively.Conclusion:The overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.
