Comprehensive multi-component analysis for authentication and differentiation of 6 Dendrobium species by 2D NMR-based metabolomic profiling

Identifying Anti-NSCLC Bioactive Compounds in Scutellaria via 2D NMR-Based Metabolomic Analysis of Pharmacologically Classified Crude Extracts

We presented a strategy utilizing 2D NMR-based metabolomic analysis of crude extracts, categorized by different pharmacological activities, to rapidly identify the primary bioactive components of TCM. It was applied to identify the potential bioactive components from Scutellaria crude extracts that exhibit anti-non-small cell lung cancer (anti-NSCLC) activity. Four Scutellaria species were chosen as the study subjects because of their close phylogenetic relationship, but their crude extracts exhibit significantly different anti-NSCLC activity. Cell proliferation assay was used to assess the anti-NSCLC activity of four species of Scutellaria. 1H-13C HSQC spectra were acquired for the chemical profiling of these crude extracts. Based on the pharmacological classification (PCA, OPLS-DA and univariate hypothesis test) were performed to identify the bioactive constituents in Scutellaria associated with the anti-NSCLC activity. As a result, three compounds, baicalein, wogonin and scutellarin were identified as bioactive compounds. The anti-NSCLC activity of the three potential active compounds were further confirmed via cell proliferation assay. The mechanism of the anti-NSCLC activity by these active constituents was further explored via flow cytometry and western blot analyses. This study demonstrated 2D NMR-based metabolomic analysis of pharmacologically classified crude extracts to be an efficient approach to the identification of active components of herbal medicine.

Identification of Dendrobium Using Laser-Induced Breakdown Spectroscopy in Combination with a Multivariate Algorithm Model

Dendrobium, a highly effective traditional Chinese medicinal herb, exhibits significant variations in efficacy and price among different varieties. Therefore, achieving an efficient classification of Dendrobium is crucial. However, most of the existing identification methods for Dendrobium make it difficult to simultaneously achieve both non-destructiveness and high efficiency, making it challenging to truly meet the needs of industrial production. In this study, we combined Laser-Induced Breakdown Spectroscopy (LIBS) with multivariate models to classify 10 varieties of Dendrobium. LIBS spectral data for each Dendrobium variety were collected from three circular medicinal blocks. During the data analysis phase, multivariate models to classify different Dendrobium varieties first preprocess the LIBS spectral data using Gaussian filtering and stacked correlation coefficient feature selection. Subsequently, the constructed fusion model is utilized for classification. The results demonstrate that the classification accuracy of 10 Dendrobium varieties reached 100%. Compared to Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), our method improved classification accuracy by 14%, 20%, and 20%, respectively. Additionally, it outperforms three models (SVM, RF, and KNN) with added Principal Component Analysis (PCA) by 10%, 10%, and 17%. This fully validates the excellent performance of our classification method. Finally, visualization analysis of the entire research process based on t-distributed Stochastic Neighbor Embedding (t-SNE) technology further enhances the interpretability of the model. This study, by combining LIBS and machine learning technologies, achieves efficient classification of Dendrobium, providing a feasible solution for the identification of Dendrobium and even traditional Chinese medicinal herbs.

Current Trends in Toxicity Assessment of Herbal Medicines: A Narrative Review

Even in modern times, the popularity level of medicinal plants and herbal medicines in therapy is still high. The World Health Organization estimates that 80% of the population in developing countries uses these types of remedies. Even though herbal medicine products are usually perceived as low risk, their potential health risks should be carefully assessed. Several factors can cause the toxicity of herbal medicine products: plant components or metabolites with a toxic potential, adulteration, environmental pollutants (heavy metals, pesticides), or contamination of microorganisms (toxigenic fungi). Their correct evaluation is essential for the patient’s safety. The toxicity assessment of herbal medicine combines in vitro and in vivo methods, but in the past decades, several new techniques emerged besides conventional methods. The use of omics has become a valuable research tool for prediction and toxicity evaluation, while DNA sequencing can be used successfully to detect contaminants and adulteration. The use of invertebrate models (Danio renio or Galleria mellonella) became popular due to the ethical issues associated with vertebrate models. The aim of the present article is to provide an overview of the current trends and methods used to investigate the toxic potential of herbal medicinal products and the challenges in this research field.

Combined metabolomics and proteomics to reveal beneficial mechanisms of Dendrobium fimbriatum against gastric mucosal injury

As a dietary and medicinal plant, Dendrobium fimbriatum (DF) is widely utilized in China for improving stomach disease for centuries. However, the underlying mechanisms against gastric mucosal injury have not been fully disclosed. Here, metabolomics and proteomics were integrated to clarify the in-depth molecular mechanisms using cyclophosphamide-induced gastric mucosal injury model in mice. As a result, three metabolic pathways, such as creatine metabolism, arginine and proline metabolism, and pyrimidine metabolism were hit contributing to DF protective benefits. Additionally, γ-L-glutamyl-putrescine, cytosine, and thymine might be the eligible biomarkers to reflect gastric mucosal injury tatus, and DF anti-gastric mucosal injury effects were mediated by the so-called target proteins such as Ckm, Arg1, Ctps2, Pycr3, and Cmpk2. This finding provided meaningful information for the molecular mechanisms of DF and also offered a promising strategy to clarify the therapeutic mechanisms of functional foods.