Recent studies have reported that the aerial parts of ginseng contain various saponins, which have anti-oxidative, anti-inflammatory, and anti-obesity properties similar to those of ginseng root. However, the leaf extracts of Korean ginseng have not yet been investigated. In this study, we demonstrate the anti-obesity effects of green leaf and dried leaf extracts (GL and DL, respectively) of ginseng in high-fat diet (HFD)-induced obese rats. The administration of GL and DL to HFD-induced obese rats significantly decreased body weight (by 96.5% and 96.7%, respectively), and epididymal and abdominal adipose tissue mass. Furthermore, DL inhibited the adipogenesis of 3T3-L1 adipocytes through regulation of the expression of key adipogenic regulators, such as peroxisome proliferator-activated receptor (PPAR)-γ and CCAAT/enhancer-binding protein (C/EBP)-α. In contrast, GL had little effect on the adipogenesis of 3T3-L1 adipocytes but greatly increased the protein expression of PPARγ compared with that in untreated cells. These results were not consistent with an anti-obesity effect in the animal model, which suggested that the anti-obesity effect of GL in vivo resulted from specific factors released by other organs, or from increased energy expenditure. To our knowledge, these findings are the first evidence for the anti-obesity effects of the leaf extracts of Korean ginseng in vivo.
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http://dx.doi.org/10.3390/nu9090999 | DOI Listing |
Front Plant Sci
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Department of Biological Sciences, College of Science, King Faisal University, Al-Ahsa, Saudi Arabia.
Introduction: is a medicinal plant that produces silymarin, which has been demonstrated to possess antiviral, anti-neurodegenerative, and anticancer activities. Silybin (A+B) are two major hepatoprotective flavonolignans produced predominantly in fruits. Several attempts have been made to increase the synthesis of silymarin, or its primary components, silybin (A+B).
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January 2025
College of Engineering, South China Agricultural University, Guangzhou, China.
Introduction: Accurate detection and recognition of tea bud images can drive advances in intelligent harvesting machinery for tea gardens and technology for tea bud pests and diseases. In order to realize the recognition and grading of tea buds in a complex multi-density tea garden environment.
Methods: This paper proposes an improved YOLOv7 object detection algorithm, called YOLOv7-DWS, which focuses on improving the accuracy of tea recognition.
Data Brief
February 2025
Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
In the field of agriculture, particularly within the context of machine learning applications, quality datasets are essential for advancing research and development. To address the challenges of identifying different mango leaf types and recognizing the diverse and unique characteristics of mango varieties in Bangladesh, a comprehensive and publicly accessible dataset titled "BDMANGO" has been created. This dataset includes images essential for research, featuring six mango varieties: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, and Himsagar, which were collected from different locations.
View Article and Find Full Text PDFFront Insect Sci
January 2025
Odisha University of Agriculture and Technology, Regional Research and Technology Transfer Station, Sambalpur, Odisha, India.
Extracts of plants have been used to manage various insect pests, but little information is available about how effective they are in reducing crop damage or how they affect crop yield and beneficial insects in rice. Extracts from leaves, leaves, leaves, leaves, cloves, and fruits, known to have insecticidal properties, were compared with two checks, viz., Azadirachtin 1% EC and standard insecticide Acephate 95 SG, for their efficacy against yellow stem borer (YSB), (Walk.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Volumetric modulated arc therapy (VMAT) is a popular radiotherapy technique in the clinic. As consisting of hundreds of control points in a VMAT plan it is more complex and time consuming than those conventional treatment modalities, such as intensity modulated radiation therapy. To improve the efficiency and accuracy of its quality assurance procedure, a novel automated anomaly detection method was proposed.
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