Background: The quality evaluation of traditional Chinese medicine (TCM) formulations is needed to guarantee the safety and efficacy. In our laboratory, we established interaction rules between chemical quality control and biological activity evaluations to study Yuanhu Zhitong tablets (YZTs). Moreover, a quality marker (Q-marker) has recently been proposed as a new concept in the quality control of TCM. However, no appropriate methods are available for the identification of Q-markers from the complex TCM systems.
Purpose: We aimed to use an integrative pharmacological (IP) approach to further identify Q-markers from YZTs through the integration of multidisciplinary knowledge. In addition, data mining was used to determine the correlation between multiple constituents of this TCM and its bioactivity to improve quality control.
Methods: The IP approach was used to identify the active constituents of YZTs and elucidate the molecular mechanisms by integrating chemical and biosynthetic analyses, drug metabolism, and network pharmacology. Data mining methods including grey relational analysis (GRA) and least squares support vector machine (LS-SVM) regression techniques, were used to establish the correlations among the constituents and efficacy, and dose efficacy in multiple dimensions.
Results: Seven constituents (tetrahydropalmatine, α-allocryptopine, protopine, corydaline, imperatorin, isoimperatorin, and byakangelicin) were identified as Q-markers of YZT using IP based on their high abundance, specific presence in the individual herbal constituents and the product, appropriate drug-like properties, and critical contribution to the bioactivity of the mixture of YZT constituents. Moreover, three Q-markers (protopine, α-allocryptopine, and corydaline) were highly correlated with the multiple bioactivities of the YZTs, as found using data mining. Finally, three constituents (tetrahydropalmatine, corydaline, and imperatorin) were chosen as minimum combinations that both distinguished the authentic components from false products and indicated the intensity of bioactivity to improve the quality control of YZTs.
Conclusions: Tetrahydropalmatine, imperatorin, and corydaline could be used as minimum combinations to effectively control the quality of YZTs.
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http://dx.doi.org/10.1016/j.phymed.2018.03.002 | DOI Listing |
Sci Rep
January 2025
Information Institute of the Ministry of Emergency Management of PR China, Beijing, 100029, People's Republic of China.
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Instituto de Patología Vegetal, Centro de Investigaciones Agropecuarias, Instituto Nacional de Tecnología Agropecuaria (IPAVE-CIAP-INTA), Camino 60 Cuadras Km 5,5, Córdoba X5020ICA, Argentina.
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January 2025
Department of Pharmacy, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Canakinumab, a humanized anti-IL-1β monoclonal antibody, is known for its ability to suppress IL-1β-mediated inflammation. However, continuous monitoring of its safety remains essential. Thus, we comprehensively evaluated the safety signals of canakinumab by data mining from FAERS.
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January 2025
Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China.
Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the finger-pricking glucometers in the market, a new sensor must first show that 95% of their glucose measurements have errors below 15% of these glucometers. Although recent innovative exploitations of the well-established Fourier-transform infrared (FTIR) spectroscopy have reached such FDA accuracy benchmarks, an FTIR spectrometer is too bulky. The advancements of quantum cascade lasers (QCLs) can lead to FTIR spectrometers of reduced size, but compact QCL-based noninvasive blood glucose sensors are not yet available.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data.
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