Background: Periplocae Cortex (PC), Acanthopanacis Cortex (AC), and Lycii Cortex (LC), as traditional Chinese medicines, are all dried root bark, presented in a roll, light and brittle, easy to break, have a fragrant scent, etc. Due to their similar appearances, it is tough to distinguish them, and they are often confused and adulterated in markets and clinical applications. To realize the identification and quality control of three herbs, in this paper, Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry Expression (UHPLC-QTOF-MS^E) combined with chemometric analysis was used to explore the different chemical compositions. Methods: LC, AC, and PC were analyzed by UHPLC-QTOF-MS^E, and the quantized MS data combined with Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used to explore the different chemical compositions with Variable Importance Projection (VIP) > 1.0. Further, the different chemical compositions were identified according to the chemical standard substances, related literature, and databases. Results: AC, PC, and LC can be obviously distinguished in PCA and PLS-DA analysis with the VIP of 2661 ions > 1.0. We preliminarily identified 17 differential chemical constituents in AC, PC, and LC with significant differences (p < 0.01) and VIP > 1.0; for example, Lycium B and Periploside H2 are LC and PC’s proprietary ingredients, respectively, and 2-Hydroxy-4-methoxybenzaldehyde, Periplocoside C, and 3,5-Di-O-caffeoylquinic acid are the shared components of the three herbs. Conclusions: UHPLC-QTOF-MS^E combined with chemometric analysis is conducive to exploring the differential chemical compositions of three herbs. Moreover, the proprietary ingredients, Lycium B (LC) and Periploside H2 (PC), are beneficial in strengthening the quality control of AC, PC, and LC. In addition, limits on the content of shared components can be set to enhance the quality control of LC, PC, and AC.

Ginseng Radix et Rhizoma (GRR), Panacis Quinquefolii Radix (PQR), Notoginseng radix et rhizoma (NRR) and Platycodonis Radix (PR) are often confused in the material market because of similar appearances and characteristics. Moreover, chemical identification methods tend to characterize the whole herb with regard to a single or a few components, which is an inaccurate representation and does not demonstrate the effective utilization of unknown components, and the result is unconvincing. In order to strengthen quality control, improve identification efficiency, and realize digital identification at the individual level of traditional Chinese medicine (TCM), we have put forward the “matrix characteristics” of TCM, combined with a UHPLC-QTOF-MS analysis to explore and realize the digital identification of GRR, PQR, NRR, and PR. The mass spectrometry was quantized to extract common data from different batches of the same TCMs as their matrix characteristics, and the matching credibility (M) was given by matching the “matrix characteristics” with unknown Chinese medicines. The results show that within a reasonable parameter threshold range, the M of four TCMs was higher than 92.00% compared with their own “matrix characteristics”, which was significantly higher than the M ranked second. Furthermore, the digital identification of four TCMs can be successfully realized based on the UHPLC-QTOF-MS analysis and “matrix characteristics”. This has important reference significance for developing the digital identification of GRR at an individual level based on UPLC-QTOF-MS and “matrix characteristics”.

In the regulatory market, it is not uncommon for ginseng radix et rhizoma (GR) to be adulterated with panacis quinquefolii radix (PR). Amid the digital transformation, this study puts forward a new method for the identification of GR adulterated with PR. Ultra-high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) was used to detect multiple batches of GR and PR to obtain mass spectrometry data. The common ions were isolated from multiple batches of GR and PR, serving as GR and PR’s “ion matrices”. Furthermore, GR and PR’s “ion matrices” were used to eliminate intersecting ion data to extract the top-100 ions as GR and PR “matrix identity cards” (MICs). Then, GR and PR’s MICs were employed as a reference for identification, yielding contrast credibility (CC) as feedback. The results indicated that leveraging the MICs of GR and PR enables efficient and precise digital identification of the two herbs: pure GR showed CC ≥ 95% when matched with GR MIC (≤2% with PR MIC), pure PR showed CC ≥ 93% with PR MIC (≤3% with GR MIC), and non-parametric analysis confirmed significant differences between groups (p < 0.01). Even in 5% PR-adulterated samples, CC ranged from 24% to 28% (avg. 25.8%) when matched with PR MIC, leading to a 26% adulteration detection threshold. Moreover, two adulterated batches were identified among ten GR blind samples, which was consistent with verification via PR-specific pseudo-ginsenoside F₁₁. This research is practically valuable for distinguishing between GR and PR, combating adulteration, and reinforcing GR quality management. It also informs the digital identification of GR via UPLC-QTOF-MS and “MICs”, contributing to the digital quality control of traditional Chinese medicines (TCMs).

This study aims to explore the anti-inflammatory pharmacological components and anti-inflammatory mechanisms of the alcohol extract of Saposhnikoviae Radix (SR). The components of the alcohol extract of SR were analyzed using the UPLC-MS/MS system. The anti-inflammatory efficacy of the alcohol extract and core components of SR was evaluated using the LPS-induced inflammation model of RAW264.7 cells. The anti-inflammatory mechanism of SR in a mouse model of rheumatoid arthritis was expounded by means of serum metabolomics, network pharmacology, and molecular docking. A total of 12 chromones and 13 coumarins were identified in the alcohol extract of SR. The alcohol extract of SR and its components all had good anti-inflammatory activities. In the mouse model of rheumatoid arthritis, the glycoside compounds of SR were transformed into aglycones, thereby exerting anti-inflammatory effects. Moreover, the alcohol extract of SR alleviated the inflammatory response by up-regulating the expression levels of metabolites such as phenylalanine and tyrosine. Network pharmacology and molecular docking results show that SR could exert an anti-inflammatory effect by regulating AGE-RAGE, PI3K-Akt, TNF, MAPK, and Toll-like signaling pathways. In this study, the anti-inflammatory efficacy and mechanisms of the alcohol extract of SR are explored, with the aim of providing a reference for subsequent research.

Drug-induced pseudoallergic reactions are rapid in onset and complex in mechanism, with current clinical safety assessments lacking precise molecular underpinnings. Cells were treated with pseudoallergen-positive compounds. Cell degranulation was verified by neutral red staining and ELISA, followed by high-throughput RNA sequencing. Key signaling pathways were elucidated through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. A pseudoallergic characteristic gene network was identified and constructed using WGCNA. Finally, two-sample MR analysis was performed utilizing GWAS data to evaluate the causal relationship between candidate genes and drug pseudoallergy, thereby identifying key genetic risk markers. The pseudoallergen-positive compounds significantly induced cell degranulation and extensive transcriptional regulation. Enrichment analyses indicated that MAPK, JAK-STAT, TNF, and PI3K-Akt signaling pathways play central roles in pseudoallergic reactions. WGCNA identified a pseudoallergic characteristic gene signature. Subsequent MR analysis identified EMP1 and KIF26A might be the key genetic risk markers associated with drug pseudoallergy. These two genes potentially increase the causal risk of drug-induced pseudoallergy by modulating membrane coating, vesicle formation, and related cellular transport processes. This study successfully established a mast cell characteristic gene signature for pseudoallergic reactions, elucidated the core transcriptional network, and identified key risk markers that may have a causal association with drug pseudoallergy.

Background: Saposhnikoviae radix (SR), a valuable Chinese medicinal plant with high medicinal and edible value, suffers from inconsistent quality caused by growth patterns, cultivation duration, and geographical origin. Methods: In this study, comprehensive volatile metabolite profiling using HS-GC-MS/MS combined with multivariate chemometrics was performed to characterize these quality-related differences. Results: In total, 7, 13, and 11 differential volatile compounds were identified for wild versus cultivated SR, cultivation duration, and geographical origin, respectively. By integrating the three comparisons, 11 recurrent differential compounds were selected for network pharmacology and molecular docking analyses. trans-3-Nonen-2-one, benzaldehyde, and Octanoic acid were predicted to interact with key targets, including TNF, IL1B, ALB, EGFR, and CASP3, which were mainly enriched in inflammation- and pain-related pathways, including the PI3K-Akt signaling pathway, apoptosis, and the HIF-1 signaling pathway. Molecular docking further supported favorable predicted interactions between the selected compounds and core targets. Conclusions: Overall, this study reveals volatile metabolite variation associated with major quality-related factors of SR and identifies candidate bioactive and quality-associated volatile compounds. The integration of volatile metabolite profiling, chemometrics, network pharmacology, and molecular docking provides a complementary strategy for the comprehensive quality evaluation of SR and offers a reliable reference for quality control research on other medicinal Apiaceae species.