Ruixin Liu

Salvia miltiorrhiza: is a widely used Chinese medicinal herb whose quality is significantly influenced by geographical origin. Establishing reliable methods for origin identification is therefore crucial for quality assurance. In this study, 67 batches of Salvia miltiorrhiza samples from Shandong, Shanxi, Henan, and Sichuan provinces were analyzed using near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometric techniques. Six preprocessing methods were applied to optimize spectral data, and PLS-DA models were constructed based on the optimized results. To further improve model performance, uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and random forest (RF) were employed for variable selection. Discriminant models were then established using NIR, MIR, and fused (NIR + MIR) data, with performance evaluated by accuracy. Results showed that in NIR, the 2nd-RF-PLS-DA model achieved the best performance with 96.72% accuracy, while in MIR, the SG-UVE-PLS-DA model reached 98.33% accuracy. After integrating NIR and MIR data, the 2nd-UVE-PLS-DA model achieved 100% accuracy, demonstrating the strongest discriminative capability. These findings demonstrate that combining NIR and MIR spectroscopy with appropriate preprocessing and variable selection strategies fully exploits complementary spectral information, enabling the construction of rapid, reliable, and efficient discriminant models. This approach provides an effective tool for origin tracing of Salvia miltiorrhiza and serves as a methodological reference for advancing quality evaluation of other Chinese herbal medicines.

Acute lung injury (ALI) is a life‐threatening inflammatory disease of the respiratory system, characterised by high mortality rates and lack of effective clinical interventions. Emerging evidence suggests that traditional Chinese medicine (TCM) formulations may offer therapeutic benefits in managing inflammatory respiratory diseases. Jinbei decoction (JBD), a 12‐herb TCM preparation currently used for pulmonary fibrosis, has shown preliminary therapeutic potential in ALI; however, mechanistic studies remain limited. This study systematically evaluated JBD's therapeutic efficacy and elucidated its molecular mechanisms in LPS‐induced ALI. Survival analysis demonstrated that JBD significantly improved survival rates in a concentration‐dependent manner, while histopathological evaluation revealed a marked reduction in pulmonary tissue damage. These effects were further supported by significant decreases in circulating levels of major pro‐inflammatory cytokines, such as TNF‐α, IL‐6 and IL‐1β. Network pharmacology analysis identified 111 molecular targets associated with ALI pathogenesis influenced by JBD components, highlighting the regulatory effect on inflammatory signalling pathways in macrophages as the key intervening mechanism. Specifically, JBD suppressed LPS‐induced inflammatory responses by inhibiting ERK phosphorylation and blocking IKKα/β activation, thereby preventing NF‐κB‐dependent cytokine production in macrophages. Notably, astrapterocarpan was identified as the primary bioactive constituent of JBD through integrated network pharmacology and biochemical analyses. It was found to directly destabilise TRAF6 protein and to exhibit therapeutic efficacy comparable to that of dexamethasone in promoting histological recovery. In vivo experiments further confirmed that JBD significantly reduced TRAF6 expression in murine models, reinforcing the conclusion that its therapeutic effects are predominantly mediated by astrapterocarpan. Collectively, these findings suggest that JBD functions as an agent capable of regulating macrophage polarisation and mitigating cytokine storm through TRAF6‐dependent signalling pathways, thereby providing a mechanistic basis for its potential clinical application in inflammatory lung diseases.

Introduction: Ling-Gui-Zhu-Gan Decoction (LGZGD), one of the first batches of classical Chinese prescriptions formally recognized by the Chinese government, has a long-standing history of clinical application and significant potential for modern development. However, the chemical composition and content of different types of pharmaceutical preparations are not clear.Methods: This study aimed to develop an analytical approach integrating HPLC and UHPLC-Q-Orbitrap/MS to comprehensively characterize the chemical constituents of LGZGD across different preparation stages and to investigate its pharmacodynamic basis in the treatment of unstable angina pectoris (UA) using network pharmacology. The content and transfer rate of six index components were quantified using HPLC.Results: A total of 75 compounds were identified via UHPLC-Q-Orbitrap/MS, comprising 24 flavonoids, 25 organic acids, nine phenylpropanoids, eight terpenoids, five saponins, and four other compounds, based on precursor ion peaks and fragment ion spectra. Notably, five compounds—(-)-pterocarpin glucoside, γ-aminobutyric acid, calycosin, trimethyl citrate, and proline-phenylalanine—were absent following the drying of the concentrate. Using the LC-MS data as a foundation, network pharmacology and molecular docking analyses were conducted to elucidate the pharmacodynamic components responsible for LGZGD’s therapeutic effects on UA. This integrative analysis identified three key active compounds—naringenin, glycyrrhizin, and calycosin—and three core targets: TNF, EGFR, and PTGS2.Discussion: The analytical method established in this study effectively delineates the chemical profile and index component transfer dynamics of LGZGD preparation intermediates, providing essential data for the development of both liquid and solid dosage forms. The constructed “medicine-component-target-pathway-disease” network preliminarily reveals the multi-component, multi-target, and multi-pathway mechanisms by which LGZGD may exert therapeutic effects on UA. This work provides a scientific foundation for its clinical application, supporting rational drug use and formulation development.

Purpose: This study aims to identify the transdermal penetration components of Shufeitie ointment (SFTOT) and investigate the potential active components and mechanisms through which SFTOT exerts its effects on Chronic Obstructive Pulmonary Disease (COPD).Methods: An in vitro permeation test (IVPT) of SFTOT was conducted using a modified Franz diffusion cell method. Ultra-high-performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap/MS) was employed to analyze data from the transdermal receiving solution, enabling comprehensive identification of the components that permeate through the skin. To predict the potential mechanisms by which SFTOT may treat COPD, network pharmacology was used to construct a component-target-collaterals network. Additionally, molecular docking was applied to verify the interactions between the potential transdermal active components of SFTOT and the core targets.Results: Using UHPLC-Q-Orbitrap/MS, we identified 129 transdermal permeation components in SFTOT. Network pharmacology analysis revealed 222 common targets between SFTOT and COPD. The primary active components were predicted to be luteolin, kaempferol, quercetin, 7-O-methylluteolin, apigenin, ferulic acid, palmitic acid, inapinic acid, 6-shogaol, and myristic acid. These components were primarily enriched in the AGE-RAGE, TNF, PI3K-Akt, and MAPK signaling pathways. Protein–protein interaction (PPI) analysis identified TNF, ALB, AKT1, EGFR, and CASP3 as core targets. Molecular docking results showed that 72% of component-target interactions had a binding energy of < −5.0 kcal/mol, indicating strong binding activity. Among these, apigenin exhibited the lowest binding energy with EGFR and consistently lower binding energies with other core targets compared to the other components. This suggests that apigenin may play a key role in treatment.Conclusion: High-resolution liquid chromatography-mass spectrometry effectively identified the transdermal penetration components of SFTOT, providing a foundation for further screening of key active compounds. Our findings suggest that SFTOT may alleviate COPD by downregulating TNF, ALB, AKT1, EGFR, and CASP3 while inhibiting inflammatory mediator release through the AGE-RAGE, TNF, PI3K-Akt, and MAPK signaling pathways. These effects may help reduce COPD-related symptom clusters. Notably, apigenin appears to be a crucial bioactive component in the prevention and treatment of COPD.

Amomi fructus (AF) has been used for both medicinal and food purposes for centuries. However, issues such as source mixing, substandard quality, and product adulteration often affect its efficacy. This study used E-nose (EN) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) to determine and analyze the volatile organic compounds (VOCs) in AF and its counterfeit products. A total of 111 VOCs were detected by HS-GC-IMS, with 101 tentatively identified. Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) identified 47 VOCs as differential markers for distinguishing authentic AF from counterfeits (VIP value >1 and P < 0.05). Based on the E-nose sensor response value and the peak volumes of the 111 VOCs, the unguided Principal Component Analysis (PCA), guided Principal Component Analysis-Discriminant Analysis (PCA-DA), and Partial Least Squares-Discriminant Analysis (PLS-DA) models were established to differentiate AF by authenticity, origin, and provenance. The authenticity identification model achieved 100.00% accuracy after PCA analysis, while the origin identification model and the provenance identification model were 95.65% (HS-GC-IMS: PLS-DA) and 98.18% (HS-GC-IMS: PCA-DA/PLS-DA), respectively. Further data-level fusion of E-nose and HS-GC-IMS significantly improved the accuracy of the origin identification model to 97.96% (PLS-DA), outperforming single-source data modeling. In conclusion, the intelligent data fusion algorithm based on E-nose and HS-GC-IMS data effectively identifies the authenticity, origin, and provenance of AF, providing a rapid and accurate method for quality evaluation.

Introduction: We here describe a new method for distinguishing authentic Bletilla striata from similar decoctions (namely, Gastrodia elata, Polygonatum odoratum, and Bletilla ochracea schltr).Methods: Preliminary identification and analysis of four types of decoction pieces were conducted following the Chinese Pharmacopoeia and local standards. Intelligent sensory data were then collected using an electronic nose, an electronic tongue, and an electronic eye, and chromatography data were obtained via high-performance liquid chromatography (HPLC). Partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and back propagation neural network (BP-NN) models were built using each set of single-source data for authenticity identification (binary classification of B. striata vs. other samples) and for species determination (multi-class sample identification). Features were extracted from all datasets using an unsupervised approach [principal component analysis (PCA)] and a supervised approach (PLS-DA). Mid-level data fusion was then used to combine features from the four datasets and the effects of feature extraction methods on model performance were compared.Results and Discussion: Gas chromatography–ion mobility spectrometry (GC-IMS) showed significant differences in the types and abundances of volatile organic compounds between the four sample types. In authenticity determination, the PLS-DA and SVM models based on fused latent variables (LVs) performed the best, with 100% accuracy in both the calibration and validation sets. In species identification, the PLS-DA model built with fused principal components (PCs) or fused LVs had the best performance, with 100% accuracy in the calibration set and just one misclassification in the validation set. In the PLS-DA and SVM authenticity identification models, fused LVs performed better than fused PCs. Model analysis was used to identify PCs that strongly contributed to accurate sample classification, and a PC factor loading matrix was used to assess the correlation between PCs and the original variables. This study serves as a reference for future efforts to accurately evaluate the quality of Chinese medicine decoction pieces, promoting medicinal formulation safety.