Tetrastigma hemsleyanum (Tetrastigma hemsleyanum Diels et Gilg) is a valuable traditional Chinese medicine with various applications. In this study, we aimed to optimize the extraction process for the total extraction yield of five flavonoid components, namely kaempferol, quercetin, rutin, kaempferol-3-O-rutinoside, and astragalin from the Tetrastigma hemsleyanum root (THR), and explore its potential molecular mechanisms in treating oxidative diseases as well as antioxidant activity. To achieve these objectives, we employed the genetic algorithm-back propagation neural network (GA-BPNN), the Box-Behnken design (BBD) with 4-factors and 3-levels to establish the optimal ethanol extraction process for the total extraction yield of the 5 components. Using public databases, the “component core targets-disease core target genes” networks were built, as well as molecular docking. Furthermore, DPPH was used to examine the antioxidant activity of the extracts obtained from THR under the optimal extraction process. The experimental value of the total extraction yield of the 5 components achieved a maximum of 788.12 mg/kg when the ethanol concentration was 73%, the solid-liquid ratio was 26 g/mL, and the ultrasonic duration was 30 min, and the ultrasonic temperature was 76 °C. When docked with protein molecules such as 6Y8I, quercetin, and other components received moderate to high scores. When the total concentration of the 5 components was 3.033 μg/mL, the DPPH radical scavenging rate was 89.81%. Compared with the BBD method, the GA-BPNN method is more efficient and reliable for optimizing the extraction process of active ingredients in THR because of its good data-fitting ability.

Background and Objective Dan-Hong injection, which comprises extracts of Salvia miltiorrhiza and Carthamus tinctorius, promotes blood circulation and reduces blood stasis. Combination of S. miltiorrhiza and C. tinctorius is more effective in treating cerebral ischemia than S. miltiorrhiza alone. This study aimed to examine the pharmacokinetic characteristics of four active ingredients of S. miltiorrhiza and C. tinctorius, namely danshensu (DSS), hydroxysafflor yellow A (HSYA), and salvianolic acid A (SAA) and B (SAB) in normal and cerebral ischemia rats. Methods Normal and cerebral ischemia rats were injected via the tail vein with each active ingredient, and blood was collected through the jaw vein at different time points. The plasma concentration of the compatibility group was analyzed by high-performance liquid chromatography, and pharmacokinetic parameters were determined using Pharmacokinetic Kinetica 4.4 software. Results The pharmacokinetics of the four active ingredients in the normal and cerebral ischemia rats were consistent with a two-compartment model. The area under the concentration–time curve was higher in normal rats than in cerebral ischemia rats, with a highly significant difference for SAA (P < 0.01). Clearance rates were lower in normal rats than in cerebral ischemia rats, with DSS showing the most significant difference (P < 0.01). Furthermore, there were significant differences between normal and cerebral ischemia rats in the distribution phase-elimination half life for DSS, SAA, and HSYA, as well as in the apparent volume of distribution for the central compartment for DSS and HSYA (P < 0.01). The plasma concentrations of the four active ingredients were higher in normal rats than in cerebral ischemia rats. Conclusion Cerebral ischemia rats showed higher drug clearance rates and longer retention times than normal rats, which may be due to destruction of the blood–brain barrier during cerebral ischemia–reperfusion. The four active ingredients likely integrated and interacted with each other to affect target sites in the brain to protect against cerebral ischemic injury.

We extracted Sal B and TIIA from Salvia miltiorrhiza using enzymatic-assisted ethanol extraction. ACONN predicted optimal process conditions. Enzymolysis and alcohol extraction were used, optimizing conditions and evaluating antioxidant activity. ACONN analyzed data and ACO optimized conditions. Lab verification comprehensively evaluated the conditions. The correlation between Sal B, TIIA, and their antioxidant activities was examined. Weights of 0.5739 and 0.4260 evaluated Sal B and TIIA. ACONN had a 97.46% fitting degree. Optimized extraction conditions improved yield and quality, yielding a comprehensive evaluation value of 27.69 with 4.46% average errors. This approach enhances extraction and compound quality. Antioxidant activity strongly correlated with component yield, influenced by extraction conditions. ACONN-optimized extraction improved Sal B and TIIA yield and quality, with potential as natural antioxidants. Integrating machine learning and optimization algorithms in industrial extraction enhances efficiency and environmental preservation.

The contents of ellagic acid and kaempferol-3-O-rutinoside, the chief active components of raspberry, are considered the quality control indices of raspberry. This work employed the ant colony neural network (ACO-BPNN) to optimize their extraction processes, and the combination of network pharmacology and molecular docking technology to unveil the potential pharmacological effects of these components. Based on the single-factor test (ultrasonic time, ethanol concentration, ultrasonic temperature, and solid-liquid ratio), a factorial experiment with 4-factors and 3-levels was conducted in parallel for 3 times. The multi-factor analysis of variance results revealed high-order interactions among the factors. Then, the ACO-BPNN model was established to characterize the complex relationship of experimental data. After further verification, relative errors were all less than 8 %, implying the model's effectiveness and reliability. Moreover, with the network pharmacology, 66 key targets were screened out and mainly concentrated in PI3K-AKT, MAPK, and Ras signal pathways. Molecular docking revealed the binding sites between active components and key targets.

Background: Non-alcoholic steatohepatitis (NASH) commonly complicates coronary artery disease (CAD), yet the interaction mechanism remains unclear. Our research seeks to investigate the common mechanisms and key signature genes between CAD and NASH.Methods: RNA sequence information for CAD and NASH was screened from the GEO database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene analysis identified key genes, followed by functional enrichment analysis of these shared genes. Three machine learning methods—LASSO, random forest, and SVM-RFE—were used to identify signature genes. Gene set enrichment analysis (GSEA) was then performed to explore potential mechanisms associated with the signature genes. In addition, single-sample gene set enrichment analysis (ssGSEA) evaluated immune infiltration in CAD and NASH and its correlation with the signature genes.Results: WGCNA has revealed two key modules for CAD and NASH. The intersection of the CAD modules and their differential genes narrowed the key genes down to 2,808 shared genes. Finally, 44 shared genes were selected for both CAD and NASH. Kyoto Encyclopedia of Genes and Genomes analysis showed that these genes were primarily enriched in insulin resistance and inflammation pathways. Machine learning identified the signature genes BATF3, SOCS2, and GPER, all with ROC values above 0.7, validated in external datasets. GSEA revealed that these genes act through common mechanisms in CAD and NASH, regulating metabolic, inflammatory, and cardiovascular pathways. In addition, ssGSEA suggested their involvement in immune cell infiltration.Conclusion: BATF3, SOCS2, and GPER have emerged as promising gene candidates that may serve as biomarkers or potential therapeutic targets for CAD combined with NASH, linked to the regulation of metabolic, inflammatory, and cardiovascular pathways. We also identified insulin resistance and inflammation pathways as common mechanisms underlying both diseases.