, which is found widely distributed across the Asian region, functions as both an economic tree and a medicinal plant with a rich historical background. Previous investigations into its chemical composition and biological activity have predominantly centered on volatile components, leaving the study of non-volatile components relatively unexplored. In this study, we employed UPLC-HRMS technology to analyze the non-volatile components of branches and leaves, which successfully resulted in identifying 72 constituents. Comparative analysis between branches and leaves unveiled alkaloids, organic acids, and flavonoids as the major components. However, noteworthy differences in the distribution of these components between branches and leaves were observed, with only eight shared constituents, indicating substantial chemical variations in different parts of . Particularly, 24 compounds were identified for the first time from this plant. The assessment of antioxidant activity using four methods (ABTS, DPPH, FRAP, and CUPRAC) demonstrated remarkable antioxidant capabilities in both branches and leaves, with slightly higher efficacy observed in branches. This suggests that may act as a potential natural antioxidant with applications in health and therapeutic interventions. In conclusion, the chemical composition and antioxidant activity of provides a scientific foundation for its development and utilization in medicine and health products, offering promising avenues for the rational exploitation of resources in the future.
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http://dx.doi.org/10.3390/molecules29040788 | DOI Listing |
Medicine (Baltimore)
January 2025
The First Clinical College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China.
This study aimed to evaluate the causal effects of different immune cells on heart failure (HF) using Mendelian randomization (MR). Datasets for immune cell phenotypes and HF were obtained from European Bioinformatics Institute and FinnGen. Then, single nucleotide polymorphisms were screened according to the basic assumptions of MR.
View Article and Find Full Text PDFPlant Dis
January 2025
Microbiology, Campus Universitário s/n, Viçosa, Minas Gerais, Brazil, 36570-000;
The Ralstonia solanacearum Species Complex (RSSC) is the most significant plant pathogen group with a wide host range. It is genetically related but displays distinct biological features, such as restrictive geography occurrence. The RSSC comprises three species: Ralstonia pseudosolanacearum (phylotype I and III), Ralstonia solanacearum (phylotype IIA and IIB), and Ralstonia syzygii (phylotype IV) (Fegan and Prior 2005).
View Article and Find Full Text PDFBMC Plant Biol
January 2025
Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, 520521, China.
Background: Calmodulin-binding transcription activator (CAMTA) proteins play significant roles in signal transduction, growth and development, as well as abiotic stress responses, in plants. Understanding their involvement in the low-temperature stress response of teak is vital for revealing cold resistance mechanisms.
Results: Through bioinformatics analysis, the CAMTA gene family in teak was examined, and six CAMTA genes were identified in teak.
Biosystems
January 2025
University of Coimbra, ADAI, LAETA, Polo II, Rua Luis Reis Santos, Coimbra, 3030-788, Portugal. Electronic address:
Infodynamics is the study of how information behaves and changes within a system during its development. This study investigates the insights that informational analysis can provide regarding the ramifications predicted by constructal design. First, infodynamic neologisms informature, defined as a measure of the amount of information in indeterminate physical systems, and infotropy-contextualized informature representing the degree of transformation of indeterminate physical systems-are introduced.
View Article and Find Full Text PDFIn recent years, image processing technology has been increasingly studied on intelligent unmanned platforms, and the differences in the shooting environment during tobacco baking pose challenges to image processing algorithms. To address this problem, an ensemble multi-dimensional randomization network (EMRNet) for intelligent recognition of tobacco baking stage is proposed. The first is to obtain the tobacco leaf area during the baking process.
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