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Tian M, Han Y, Ma X, Liang W, Meng Z, Cao G, Luo Y, Zang H. Quality study of animal-derived traditional Chinese medicinal materials based on spectral technology: Calculus bovis as a case. PHYTOCHEMICAL ANALYSIS : PCA 2024. [PMID: 38649268 DOI: 10.1002/pca.3358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/15/2024] [Accepted: 03/24/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Calculus bovis (C. bovis) is a typical traditional Chinese medicine (TCM) derived from animals, which has a remarkable curative effect and high price. OBJECTIVES Rapid identification of C. bovis from different types was realized based on spectral technology, and a rapid quantitative analysis method for the main quality control indicator bilirubin was established. METHODS We conducted a supervised and unsupervised pattern recognition study on 44 batches of different types of C. bovis by five spectral pretreatment methods. Three variable selection methods were used to extract the essential information, and the partial least squares regression (PLSR) quantitative model of bilirubin by near-infrared (NIR) spectroscopy was constructed. RESULTS The partial least squares discriminant analysis (PLS-DA) model could achieve 100% accuracy in identifying different types of C. bovis. The R2 of the NIR quantitative model was 0.979, which is close to 1, and the root mean square error of calibration (RMSEC) was 2.3515, indicating the good prediction ability of the model. CONCLUSION The study was carried out to further improve the basic data of quality control of C. bovis and help the high-quality development of TCM derived from animals.
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Affiliation(s)
- Mengyin Tian
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, Shandong, China
- National Glycoengineering Research Center, Shandong University, Jinan, Shandong, China
| | - Ying Han
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, Shandong, China
- National Glycoengineering Research Center, Shandong University, Jinan, Shandong, China
| | - Xiaobo Ma
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, Shandong, China
- National Glycoengineering Research Center, Shandong University, Jinan, Shandong, China
| | - Wenyan Liang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, Shandong, China
- National Glycoengineering Research Center, Shandong University, Jinan, Shandong, China
| | - Zhaoqing Meng
- Shandong Hongjitang Pharmaceutical Group Co. Ltd., Jinan, China
| | - Guiyun Cao
- Shandong Hongjitang Pharmaceutical Group Co. Ltd., Jinan, China
| | - Yi Luo
- Shandong Hongjitang Pharmaceutical Group Co. Ltd., Jinan, China
| | - Hengchang Zang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, Shandong, China
- National Glycoengineering Research Center, Shandong University, Jinan, Shandong, China
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Liu CL, Jiang Y, Li HJ. Quality Consistency Evaluation of Traditional Chinese Medicines: Current Status and Future Perspectives. Crit Rev Anal Chem 2024:1-18. [PMID: 38252135 DOI: 10.1080/10408347.2024.2305267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Quality consistency evaluation of traditional Chinese medicines (TCMs) is a crucial factor that determines the safe and effective application in clinical settings. However, TCMs exhibit diverse, heterogeneous, complex, and flexible chemical compositions, as well as variability in preparation processes. These characteristics pose greater challenges in researching the consistency of TCMs compared to chemically synthesized and biological drugs. Therefore, it is paramount to develop effective strategies for evaluating the quality consistency of TCMs. From the starting point of quality properties, this review explores the strategy used to evaluate quality consistency in terms of chemistry-based strategy (chemical consistency) and the biology-based strategy (bioequivalence). Among them, the chemistry-based strategy is the mainstream, and biology-based strategy complements the chemistry-based strategy each other. Furthermore, the emerging chemistry-biology strategies (overall evaluation) is discussed, including individually combining strategy and integration strategy. Finally, this review provides insights into the challenges and future perspectives in this field. By highlighting current status and trends in TCMs quality consistency, this review aims to contribute to establishment of generally applicable chemistry-biology integrated evaluation strategy for TCMs. This will facilitate the advancement toward a higher stage of overall quality evaluation.
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Affiliation(s)
- Chun-Lu Liu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Yan Jiang
- College of Chemical Engineering, Nanjing Forestry University, Nanjing, China
| | - Hui-Jun Li
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
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Wang H, Yao Z, Luo R, Liu J, Wang Z, Zhang G. LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications. Gene 2023; 862:147246. [PMID: 36736509 DOI: 10.1016/j.gene.2023.147246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/22/2022] [Accepted: 01/27/2023] [Indexed: 02/04/2023]
Abstract
OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secrets of OMIC is like deciphering the biochemical code, but building data-driven models to mine the hidden phenotypic trait information has been a research hotspot. Transcriptome analysis is a popular biological technology for characterizing living systems' overall health, including cells and tissues. Individual transcript expression levels are known to be correlated with those of other transcripts. Nevertheless, most computational studies do not fully exploit these inter-feature correlations. Differential expression analyses, for example, assume that the expression levels of the transcripts are independent. Thus, we propose extracting these inter-feature correlations using the convolutional neural network (CNN) and transforming the transcriptomic features into a new space of convolutional transcriptomic (LaCOme) features. On most transcriptomic datasets in use, a series of comprehensive experiments have demonstrated that engineered LaCOme features outperform the original transcriptomic features in classification performances. Based on experimental results, OMIC data from biological samples could be further enriched using CNN to enhance computational analysis results. Also, feature rough screening can be used to extract valuable information from OMIC, regardless of the algorithm used to select features. It may always be better to create a novel feature than to keep the original. Furthermore, we investigated the feasibility of the feature construction method through cross-validation and independent verification, hoping to develop a more efficient and effective method.
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Affiliation(s)
- Hongyu Wang
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China; College of Software, Jilin University, Changchun, Jilin 130012, China
| | - Zhaomin Yao
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China
| | - Renli Luo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China
| | - Jiahao Liu
- School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China
| | - Zhiguo Wang
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China.
| | - Guoxu Zhang
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China.
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Effect of Lactobacillus plantarum and Lactobacillus acidophilus fermentation on antioxidant activity and metabolomic profiles of loquat juice. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Unraveling the mystery of efficacy in Chinese medicine formula: New approaches and technologies for research on pharmacodynamic substances. ARAB J CHEM 2022; 15:104302. [PMID: 36189434 PMCID: PMC9514000 DOI: 10.1016/j.arabjc.2022.104302] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 09/21/2022] [Indexed: 12/25/2022] Open
Abstract
Traditional Chinese medicine (TCM) is the key to unlock treasures of Chinese civilization. TCM and its compound play a beneficial role in medical activities to cure diseases, especially in major public health events such as novel coronavirus epidemics across the globe. The chemical composition in Chinese medicine formula is complex and diverse, but their effective substances resemble "mystery boxes". Revealing their active ingredients and their mechanisms of action has become focal point and difficulty of research for herbalists. Although the existing research methods are numerous and constantly updated iteratively, there is remain a lack of prospective reviews. Hence, this paper provides a comprehensive account of existing new approaches and technologies based on previous studies with an in vitro to in vivo perspective. In addition, the bottlenecks of studies on Chinese medicine formula effective substances are also revealed. Especially, we look ahead to new perspectives, technologies and applications for its future development. This work reviews based on new perspectives to open horizons for the future research. Consequently, herbal compounding pharmaceutical substances study should carry on the essence of TCM while pursuing innovations in the field.
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Key Words
- 2D, Two Dimensional
- 3D, Three Dimensional
- ADME, Absorption, Distribution, Metabolism, and Excretion
- AFA DESI-MSI, Air flow-assisted desorption electrospray ionization mass spectrometry imaging
- AI, Artificial Intelligence
- Active ingredient
- CDE, Center for Drug Evaluation
- COX-2, Cyclooxygenase 2
- Chemical components
- Chinese medicine formula
- Compound
- Disease Targets
- GC-MS, Gas chromatography-mass spectrometry
- HPLC, High Performance Liquid Chromatography
- HR-MS, High Resolution Mass Spectrometry
- HTS, High Throughput Screening
- HUA, hyperuricemia
- ICPMS, inductively coupled plasma mass spectrometry
- MALDI MS, Matrix for surface-assisted laser desorption/ionization mass spectrometry
- MD, Microdialysis
- MI, Molecular imprinting
- MSI, Mass spectrometry imaging
- Mass Spectrometry
- NL/PR, Neutral loss/precursor ion
- NMPA, National Medical Products Administration
- OPLS-DA, Orthogonal partial least squares discriminant analysis
- PD, Pharmacodynamic
- PK, Pharmacokinetic
- Q-TOF/MS, Quadrupole time-of-flight mass spectrometry
- QSAR, Quantitative structure-activity relationship
- QqQ-MS, Triple quadruple mass spectrometry
- R-strategy, Reduce strategy
- TCM, Traditional Chinese medicine
- UF, Affinity ultrafiltration
- UPLC, Ultra Performance Liquid Chromatography
- XO, Xanthine oxidase
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Li X, Liang C, Su R, Wang X, Yao Y, Ding H, Zhou G, Luo Z, Zhang H, Li Y. An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile. Front Chem 2022; 10:1005843. [DOI: 10.3389/fchem.2022.1005843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022] Open
Abstract
Animal bile is an important component of natural medicine and is widely used in clinical treatment. However, it is easy to cause mixed applications during processing, resulting in uneven quality, which seriously affects and harms the interests and health of consumers. Bile acids are the major bioactive constituents of bile and contain a variety of isomeric constituents. Although the components are structurally similar, they exhibit different pharmacological activities. Identifying the characteristics of each animal bile is particularly important for processing and reuse. It is necessary to establish an accurate analysis method to distinguish different types of animal bile. We evaluated the biological activity of key feature markers from various animal bile samples. In this study, a strategy combining metabolomics and machine learning was used to compare the bile of three different animals, and four key markers were screened. Quantitative analysis of the key markers showed that the levels of Glycochenodeoxycholic acid (GCDCA) and Taurodeoxycholic acid (TDCA) were highest in pig bile; Glycocholic acid (GCA) and Cholic acid (CA) were the most abundant in bovine and sheep bile, respectively. In addition, four key feature markers significantly inhibited the production of NO in LPS-stimulated RAW264.7 macrophage cells. These findings will contribute to the targeted development of bile in various animals and provide a basis for its rational application.
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Meng FB, Zhou L, Li JJ, Li YC, Wang M, Zou LH, Liu DY, Chen WJ. The combined effect of protein hydrolysis and Lactobacillus plantarum fermentation on antioxidant activity and metabolomic profiles of quinoa beverage. Food Res Int 2022; 157:111416. [DOI: 10.1016/j.foodres.2022.111416] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/18/2022] [Accepted: 05/23/2022] [Indexed: 12/25/2022]
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