Relationships of Ischemic Stroke Occurrence and Outcome with Gene Variants Encoding Enzymes of Tryptophan Metabolism

Combining metabolomics and network pharmacology to investigate the protective effect of Jiawei Xinglou Chengqi Granules in ischemic stroke

Jiawei Xinglou Chengqi Granule (JXCG) is an effective herbal medicine for the treatment of ischemic stroke (IS). JXCG has been shown to effectively ameliorate cerebral ischemic symptoms in clinical practice, but the underlying mechanisms are unclear. In this study, we investigated the mechanisms of action of JXCG in the treatment of IS by combining metabolomics with network pharmacology. The chemical composition of JXCG was analyzed using ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Ultra-high performance liquid chromatography-tandem time-of-flight mass spectrometry (UHPLC-Q-TOF MS) untargeted metabolomics were used to identify differential metabolites within metabolic pathways. Network pharmacology was applied to mine potential targets of JXCG in the treatment of IS. The identified key targets were validated by constructing an integrated network of metabolomics and network pharmacology and by molecular docking using Cytoscape. The effect of JXCG on IS was evaluated in vivo, and the predicted targets and pathways of JXCG in IS therapy were assessed using immunoblotting. Combining metabolomics and network pharmacology, we identified the therapeutic targets of JXCG for IS. Notably, JXCG lessened neuronal damage and reduced cerebral infarct size in rats with IS. Western blot analysis showed that JXCG upregulated PRKCH and downregulated PRKCE and PRKCQ proteins. Our combined network pharmacology and metabolomics findings showed that JXCG may have therapeutic potential in the treatment of IS by targeting multiple factors and pathways.

Research on the Graphical Model Structure Characteristic of Strong Exogeneity Based on Twin Network Method and Its Application in Causal Inference

Strong exogeneity is an important assumption in the study of causal inference, but it is difficult to identify according to its definition. The twin network method provides a graphical model tool for analyzing the variable relationship, involving the actual world and the hypothetical world, which facilitates the investigating of strong exogeneity. In this paper, the graphical model structure characteristic of strong exogeneity is investigated based on the twin network method. Compared with other derivation methods of graphical diagnosis, the method based on the twin network is more concise, clearer, and easier to understand. Under the condition of strong exogeneity, it is easy to estimate the probability of causation based on observational data. As an example, the application of graphical model structure characteristic of strong exogeneity in causal inference in the context of lung cancer simple sets (LUCAS) is illustrated.