Floating wheat is a classical herbal with potential efficacy in the treatment of hyperhidrosis. Aiming at revealing the main components and potential mechanisms of floating wheat, a comprehensive and unique phytopharmacology profile study was carried out. First, common wheat was used as a control to look for chemical markers of floating wheat. In the screening analysis, a total of 180 shared compounds were characterized in common wheat and floating wheat, respectively. The results showed that floating wheat and common wheat contain similar types of compounds. In addition, in non-targeted metabolomic analysis, when taking the contents of the constituents into account, it was found that there indeed existed quite a difference between floating wheat and common wheat and 17 potential biomarkers for floating wheat. Meanwhile, a total of seven components targeted for hyperhidrosis were screened out based on network pharmacology. Seven key differential components were screened, among which kaempferol, asiatic acid, sclareol, enoxolone, and secoisolariciresinol had higher degree values than the others. The analysis of interacting genes revealed three key genes, namely, MAP2K1, ESR1, and ESR2. The Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses showed that various signaling pathways were involved. Prolactin signaling, thyroid cancer, endocrine resistance, gonadotropin secretion, and estrogen signaling pathways were the main pathways of the intervention of floating wheat in excessive sweating, which was associated with the estrogenic response, hormone receptor binding, androgen metabolism, apoptosis, cancer, and many other biological processes. Molecular docking showed that the screened key components could form good bindings with the target proteins through intermolecular forces. This study reveals the active ingredients and potential molecular mechanism of floating wheat in the treatment of hyperhidrosis and provides a reference for subsequent basic research.
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http://dx.doi.org/10.3390/molecules29030553 | DOI Listing |
Front Plant Sci
June 2024
College of Electronic Engineering, South China Agricultural University, Guangzhou, China.
Introduction: The precise detection of weeds in the field is the premise of implementing weed management. However, the similar color, morphology, and occlusion between wheat and weeds pose a challenge to the detection of weeds. In this study, a CSCW-YOLOv7 based on an improved YOLOv7 architecture was proposed to identify five types of weeds in complex wheat fields.
View Article and Find Full Text PDFJ Sci Food Agric
August 2024
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Background: Quick and accurate detection of nutrient buds is essential for yield prediction and field management in tea plantations. However, the complexity of tea plantation environments and the similarity in color between nutrient buds and older leaves make the location of tea nutrient buds challenging.
Results: This research presents a lightweight and efficient detection model, T-YOLO, for the accurate detection of tea nutrient buds in unstructured environments.
Molecules
January 2024
College of Smart Health, Henan Polytechnic, Zhengzhou 450046, China.
Floating wheat is a classical herbal with potential efficacy in the treatment of hyperhidrosis. Aiming at revealing the main components and potential mechanisms of floating wheat, a comprehensive and unique phytopharmacology profile study was carried out. First, common wheat was used as a control to look for chemical markers of floating wheat.
View Article and Find Full Text PDFFront Plant Sci
December 2023
School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China.
Introduction: Efficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.
View Article and Find Full Text PDFPhysiol Plant
December 2023
Department of Plant Anatomy, Institute of Biology, Faculty of Science, ELTE Eötvös Loránd University, Budapest, Hungary.
High soil salinity is a global problem in agriculture that directly affects seed germination and the development of the seedlings sown deep in the soil. To study how salinity affected plastid ultrastructure, leaf segments of 11-day-old light- and dark-grown (etiolated) wheat (Triticum aestivum L. cv.
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