Bozhou University

Fritillaria spp. refers to the dried bulbs of multiple Fritillaria species (Liliaceae family), a well-known medicinal and edible homologous plant widely used in traditional herbal medicine and dietary supplements. Different Fritillaria spp. exhibit differences in chemical compositions, flavors, properties, and medicinal values, leading to a growing demand for quick authentication. This study was to explore the feasibility of rapid identification Fritillaria spp. using portable near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy coupled with machine learning methods. Classification models using K-nearest neighbors (KNN), random forest (RF), and support vector machine (SVM) were developed based on spectral data collected from four series of Fritillaria spp. The data was preprocessed by multiple data pretreatment and feature variable selection algorithms to improve its quality. Low-level, mid-level, and high-level data fusion strategies were applied to combine the spectral data from portable NIR, MIR, and Raman techniques. The results showed that data fusion significantly improved the classification of Fritillaria spp. Notably, high-level fusion based on the stacking strategy yielded the highest prediction accuracy at 90.41 %, with an F1 score of 0.906, a Kappa value of 0.861, and an AUC of 0.989. This study provides a rapid, accurate, and non-destructive method for identifying Fritillaria spp. samples, thereby enhancing analytical capabilities for future research related to this species.

Paeoniae Radix Alba is derived from the dried roots of Paeonia lactiflora Pall. (Ranunculaceae). Bozhou Paeoniae Radix Alba (BBS), primarily cultivated in Bozhou City of Anhui province, accounts for over 75 % of China's total Paeoniae Radix Alba yield. It traditionally harvested in summer and autumn. However, the spatial metabolomic variations across harvesting periods remain underexplored.

This study employed MALDI-MSI to analyze BBS harvested in March, June, September, and December. Differential metabolites were screened via multivariate statistical analysis, followed by KEGG pathway enrichment and activity trend analysis. Characteristic differential metabolites' spatial distributions and biosynthetic pathways were mapped. TOPSIS evaluated metabolite variations across harvesting periods.

MALDI-MSI analysis detected 1801 metabolites across 15 classes. Screening identified 608 differential metabolites primarily comprising terpenoid glycosides, phenolic acids, and tannins. K-means clustering delineated five metabolic trends. The results shown that BBS harvested in September demonstrate significantly enhanced enrichment and accelerated accumulation rates. KEGG pathway analysis revealed metabolite distribution among 68 pathways, showing highest enrichment in secondary metabolite biosynthesis. Key metabolic shifts included: September enrichment peaks for gallic acid, mudanpioside H, and sucrose; accelerated September accumulation for paeoniflorin, benzoylpaeoniflorin, and anthraquinone (maintaining high abundance through December); while 1,2,3,4,6-penta-O-galloyl-β-D-glucopyranose, quercetin, and N-acetyl-D-tryptophan showed high March-June abundance. Biosynthetic pathways were spatially resolved. Entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluation confirmed September as the optimal harvesting period.

MALDI-MSI effectively elucidated spatiotemporal metabolite distribution patterns in BBS. Temporal accumulation patterns were revealed across growth cycles. Spatial analysis clarified pathway-specific metabolite distributions in different tissues. This study provides scientific evidence for optimizing harvesting strategies. It enhances comprehensive utilization of medicinal resources. The findings support further development of season-specific BBS resources.

Sinomenine possesses potent suppression on the nuclear factor kappa-B (NF-κB) pathway. The phenylpropanoid moiety was introduced into sinomenine for building dual-structure sinomenine hybrid derivatives. The software AutoDock Vina was used to dock the sinomenine hybrid derivatives with 3 key proteins (1VKX, 4KIK and 6YMN) of NF-κB activation, and the virtually screened derivatives were tested via molecular dynamics simulation. Three series of derivatives were synthesized by Heck cross-coupling reaction, and evaluated for their suppressive effects on NF-κB in HEK 293 cells transfected with plasmid pNF-κB-luc. Some 1-acrylate sinomenine derivatives showed more active suppressive effects than the parent sinomenine. Derivatives 1-3 of sinomenine series were more active on NF-κB than other three series. Derivative 3 with 1-phenethyl acrylate was the most active one.