Overview and limitations of database in global traditional medicines: A narrative review.

The study of traditional medicine has garnered significant interest, resulting in various research areas including chemical composition analysis, pharmacological research, clinical application, and quality control. The abundance of available data has made databases increasingly essential for researchers to manage the vast amount of information and explore new drugs. In this article we provide a comprehensive overview and summary of 182 databases that are relevant to traditional medicine research, including 73 databases for chemical component analysis, 70 for pharmacology research, and 39 for clinical application and quality control from published literature (2000-2023). The review categorizes the databases by functionality, offering detailed information on websites and capacities to facilitate easier access. Moreover, this article outlines the primary function of each database, supplemented by case studies to aid in database selection. A practical test was conducted on 68 frequently used databases using keywords and functionalities, resulting in the identification of highlighted databases. This review serves as a reference for traditional medicine researchers to choose appropriate databases and also provides insights and considerations for the function and content design of future databases.

Medcheck: a novel software for automated de-formulation of traditional Chinese medicine (TCM) prescriptions by liquid chromatography-mass spectrometry.

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Comprehensive HRMS Screening and Risk Assessments of Aristolochic Acid Analogues in Asari Radix et Rhizoma and Related Commercial Health Products.

Aristolochic acid analogues (AAAs) are well-known toxins. We performed the first comprehensive screening on AAAs in Asari Radix et Rhizoma (underground part of Asarum heterotropoides Schmidt), the only Aristolochiaceae plant widely used in clinical practice. LC-HRMS revealed 70 trace AAAs using polygonal mass defect filtering and precursor ion list strategies, 38 of which were newly discovered in A. heterotropoides. UHPLC-QTrap-MS/MS was then utilized for quantitative/semiquantitative analysis of 26 abundant compounds. Seventeen AAAs were detected from 91 batches of A. heterotropoides and 20 AAAs from 166 consumable products. For 141 Asari-containing proprietary products, aristolactam I and aristolactam II-glucoside exhibited the widest distribution, present in 98% products. AA IVa was the most abundant, detected in 91%. Notably, 60% of the products contained AA I (0.03-0.79 ppm). The safety was assessed using linear extrapolation, permitted daily exposure, cumulative amount, and the margin of exposure. It is recommended that AA I content be limited to 3 ppm.

MI-DenseCFNet: deep learning-based multimodal diagnosis models for Aureus and Aspergillus pneumonia.

OBJECTIVE: To build and merge a diagnostic model called multi-input DenseNet fused with clinical features (MI-DenseCFNet) for discriminating between Staphylococcus aureus pneumonia (SAP) and Aspergillus pneumonia (ASP) and to evaluate the significant correlation of each clinical feature in determining these two types of pneumonia using a random forest dichotomous diagnosis model. This will enhance diagnostic accuracy and efficiency in distinguishing between SAP and ASP. METHODS: In this study, 60 patients with clinically confirmed SAP and ASP, who were admitted to four large tertiary hospitals in Kunming, China, were included. Thoracic high-resolution CT lung windows of all patients were extracted from the picture archiving and communication system, and the corresponding clinical data of each patient were collected. RESULTS: The MI-DenseCFNet diagnosis model demonstrates an internal validation set with an area under the curve (AUC) of 0.92. Its external validation set demonstrates an AUC of 0.83. The model requires only 10.24s to generate a categorical diagnosis and produce results from 20 cases of data. Compared with high-, mid-, and low-ranking radiologists, the model achieves accuracies of 78% vs. 75% vs. 60% vs. 40%. Eleven significant clinical features were screened by the random forest dichotomous diagnosis model. CONCLUSION: The MI-DenseCFNet multimodal diagnosis model can effectively diagnose SAP and ASP, and its diagnostic performance significantly exceeds that of junior radiologists. The 11 important clinical features were screened in the constructed random forest dichotomous diagnostic model, providing a reference for clinicians. CLINICAL RELEVANCE STATEMENT: MI-DenseCFNet could provide diagnostic assistance for primary hospitals that do not have advanced radiologists, enabling patients with suspected infections like Staphylococcus aureus pneumonia or Aspergillus pneumonia to receive a quicker diagnosis and cut down on the abuse of antibiotics. KEY POINTS: • MI-DenseCFNet combines deep learning neural networks with crucial clinical features to discern between Staphylococcus aureus pneumonia and Aspergillus pneumonia. • The comprehensive group had an area under the curve of 0.92, surpassing the proficiency of junior radiologists. • This model can enhance a primary radiologist's diagnostic capacity.

Diastereomers of Steroidal Alkaloids with Cytotoxic Activities against Non-Small-Cell Lung Cancer from the Bulbs of Fritillaria sinica.

Eleven new steroidal alkaloids, along with nine known related compounds, were isolated from the bulbs of Fritillaria sinica. Seven pairs of diastereomers were identified, including six and four 20-deoxy cevanine-type steroidal alkaloid diastereomers with molecular weights of 413 and 415, respectively. Structures were elucidated based on spectroscopic data analysis, chemical derivatization, and single-crystal X-ray diffraction analysis. Compounds 5, 9, 11, 12, 16, and 20 exhibited significant in vitro cytotoxic activity against non-small-cell lung cancer with CC50 values from 6.8 ± 3.9 to 12 ± 5 µM.

Species discrimination of multiple botanical origins of Fritillaria species based on infrared spectroscopy, thin layer chromatography-image analysis and untargeted metabolomics.

BACKGROUND: Fritillaria Bulbus (FB), a precious medicinal herb renowned for its heat-clearing, lung-moistening, cough-relieving and phlegm-eliminating effects. In pursuit of profits, unscrupulous merchants have engaged in the substitution or adulteration of valuable varieties with cheaper alternatives. It is, therefore, urgent to develop effective technical approaches to identify FBs from adulterants. METHODS: This paper employed infrared spectroscopy (IR), thin layer chromatography-image analysis (TLC-IA), and untargeted metabolomics techniques to discriminate ten species of FBs. RESULTS: Five species of FBs were successfully differentiated using mid-infrared spectroscopy. Furthermore, the power of TLC-IA technology allowed the differentiation of five species of FBs and two origins of FCBs (Fritillariae Cirrhosae Bulbus). Remarkably, through the application of untargeted metabolomics technique, the precise discrimination of five species of FBs, as well as three origins of FCBs were accomplished. Moreover, a comprehensive identification of 101 markers that reliably distinguished diverse FBs was achieved through the employment of untargeted metabolomics technique. CONCLUSION: The investigation presented powerful means of detection for assuring the quality control of Fritillaria herbs.

MATLAB language assisted data acquisition and processing in liquid chromatography Orbitrap mass spectrometry: Application to the identification and differentiation of Radix Bupleuri from its adulterants.

Comprehensive and rapid analysis of secondary metabolites like saponins remains challenging. This study aimed to establish a semi-automated workflow for filtration, identification, and characterization of saikosaponins in six Bupleurum species. Radix Bupleuri, a high-sales herbal medicine, is often adulterated, restricting its quality control and applications. Two authentic Radix Bupleuri species and four major adulterants were analyzed through UHPLC-LTQ-Orbitrap-MS for targeted saikosaponin analysis. To reveal trace saikosaponins and obtain quality fragment data, a MATLAB-based process automatically enumerating "sugar chain + aglycone + side chain" combinations and deduplicating generated a predicted saikosaponin database covering all possible saikosaponins as a precursor ion list for comprehensive targeted acquisition. To focus on informative ions and reduce MS analysis workload, we utilized MATLAB to automatically filtrate the false positive ions by MS1 and MS2 spectrometry. The newly established MATLAB-assisted data acquisition approach exhibited 50 % improvement in characterization of targeted saikosaponins. Furthermore, positive and negative ionization workflows were designed for accurate saikosaponins characterization based on fragmentation rules. In total, 707 saikosaponins were characterized, including over 500 potential new compounds and previously unreported C29 aglycones. We identified 25 saikosaponins present in both authentic species but absent in adulterants as potential markers. This unprecedented comprehensive multi-origin species differentiation demonstrates the promise of MATLAB-assisted acquisition and processing to advance saponin identification and standardize the Radix Bupleuri market.

The effect of metformin on senescence of T lymphocytes.

BACKGROUND: Immunosenescence occurs as people age, leading to an increased incidence of age-related diseases. The number of senescent T cells also rises with age. T cell senescence and immune response dysfunction can result in a decline in immune function, especially in anti-tumor immune responses. Metformin has been shown to have various beneficial effects on health, such as lowering blood sugar levels, reducing the risk of cancer development, and slowing down the aging process. However, the immunomodulatory effects of metformin on senescent T cells still need to be investigated. METHODS: PBMCs isolation from different age population (n = 88); Flow Cytometry is applied to determine the phenotypic characterization of senescent T lymphocytes; intracellular staining is applied to determine the function of senescent T cells; Enzyme-Linked Immunosorbent Assay (ELISA) is employed to test the telomerase concentration. The RNA-seq analysis of gene expression associated with T cell senescence. RESULTS: The middle-aged group had the highest proportion of senescent T cells. We found that metformin could decrease the number of CD8 + senescent T cells. Metformin affects the secretion of SASP, inhibiting the secretion of IFN-γ in CD8 + senescent T cells. Furthermore, metformin treatment restrained the production of the proinflammatory cytokine IL-6 in lymphocytes. Metformin had minimal effects on Granzyme B secretion in senescent T cells, but it promoted the production of TNF-α in senescent T cells. Additionally, metformin increased the concentration of telomerase and the frequency of undifferentiated T cells. The results of RNA-seq showed that metformin promoted the expression of genes related to stemness and telomerase activity, while inhibiting the expression of DNA damage-associated genes. CONCLUSION: Our findings reveal that metformin could inhibit T cell senescence in terms of cell number, effector function, telomerase content and gene expression in middle-aged individuals, which may serve as a promising approach for preventing age-related diseases in this population.

Undescribed steroidal alkaloids from the bulbs of Fritillaria sinica.

Eight undescribed steroidal alkaloid derivatives, including three cevanine-type isosteroidal alkaloids (two N-oxide glycosides and one D-ring aromatization) (1-3), one verazine-type steroidal alkaloid derivative (4), three solanidine-type steroidal alkaloid glycosides (5-7), and one veratramine-type analogue (8), along with three known compounds (9-11) were isolated from the bulbs of Fritillaria sinica. Their structures were elucidated by comprehensive analysis of spectroscopic data, acidic hydrolysis, and X-ray crystal diffractions. In the in vitro bioassay, the anti-cancer effect, anti-oxidation and anti-inflammatory activities for the isolates were evaluated at a concentration of 10 µM.

Systematical characterization of gypenosides in Gynostemma pentaphyllum and the chemical composition variation of different origins.

Gynostemma pentaphyllum (Thunb.) Makino is an herbaceous plant of Cucurbitaceae family, which has been widely used as an herbal tea and traditional Chinese medicine. Since its saponins are similar to ginsenosides and have a wide range of activities, it has attracted wide interest. However, there are still a large number of unknown saponins that have not been isolated, especially some trace gypenosides. In the present study, a HILIC × RP offline two-dimensional liquid separation combined with a multimode data acquisition was developed for the systematical characterization of gypenosides. On top of the negative mode information, considering that saponins are prone to in-source fragmentations in positive ion mode, a precursor ion list data acquisition method was used for the targeted acquisition of multistage positive data. Reference herbal drug was taken as a golden sample to probe the chemical composition of G. pentaphyllum. The mixed sample of commercially available samples were also analyzed in parallel. Furthermore, the chemical compositions of commercially available samples from different sources were compared. In total, 1108 saponins were characterized, among which 588 were accurately characterized, with 574 identified in the reference herbal drug and 700 in the mixed commercially available samples. The commercially available samples showed great composition variation. These findings clarified the material basis and provided clues for quality control of G. pentaphyllum.