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Herbs, such as Aconitum carmichaeli Debeaux (ACD), have long been used as therapies, but it is difficult to identify which organs of the human body are affected by the various compounds. In this study, we predicted the organ where the drug predominantly acts using bioinformatics and verified it using transcriptomics. We constructed a computer-aided brain system network (BSN) and intestinal system network (ISN). We predicted the action points of ACD using network pharmacology (NP) analysis and predicted the dockable proteins acting in the BSN and ISN using statistical-based docking analysis. The predicted results were verified using ACD-induced transcriptome analysis. The predicted results showed that both the NP and docking analyses predominantly acted on the BSN and showed better hit rates in the hub nodes. In addition, we confirmed through verification experiments that the SW1783 cell line had more than 10 times more differentially expressed genes than the HT29 cell line and that the dominant acting organ is the brain, using network dimension spanning analysis. In conclusion, we found that ACD preferentially acts in the brain rather than in the intestine, and this multi-bioinformatics-based approach is expected to be used in future studies of drug efficacy and side effects. [ABSTRACT FROM AUTHOR]

Atopic dermatitis (AD) is chronic allergic contact dermatitis with immune dysregulation. Veronica persica has pharmacological activity that prevents asthmatic inflammation by ameliorating inflammatory cell activation. However, the potential effects of the ethanol extract of V. persica (EEVP) on AD remain elusive. This study evaluated the activity and underlying molecular pathway of EEVP in two AD models: dinitrochlorobenzene (DNCB)-induced mice and interferon (IFN)-γ/tumor necrosis factor (TNF)-α-stimulated human HaCaT keratinocytes. EEVP attenuated the DNCB-induced increase in serum immunoglobulin E and histamine levels, mast cell counts in toluidine-blue-stained dorsal skin, inflammatory cytokine (IFN-γ, interleukin [IL]-4, IL-5, and IL-13) levels in cultured splenocytes, and the mRNA expression of IL6, IL13, IL31 receptor, CCR-3, and TNFα in dorsal tissue. Additionally, EEVP inhibited the IFN-γ/TNF-α-induced mRNA expression of IL6, IL13, and CXCL10 in HaCaT cells. Furthermore, EEVP restored the IFN-γ/TNF-α-induced downregulation of heme oxygenase (HO)-1 in HaCaT cells by inducing nuclear factor erythroid 2-related factor 2 (Nrf2) expression. A molecular docking analysis demonstrated that EEVP components have a strong affinity to the Kelch-like ECH-associated protein 1 Kelch domain. In summary, EEVP inhibits inflammatory AD by attenuating immune cell activation and inducing the Nrf2/HO-1 signaling pathway in skin keratinocytes. [ABSTRACT FROM AUTHOR]

While pattern identification (PI) is an essential process in traditional medicine (TM), it is difficult to objectify since it relies heavily on implicit knowledge. Therefore, this study aimed to propose a machine learning (ML)-based analysis tool to evaluate the clinical decision-making process of PI in terms of explicit and implicit knowledge, and to observe the actual process by which this knowledge affects the choice of diagnosis and treatment in individual TM doctors. Clinical data for the development of the analysis tool were collected using a questionnaire administered to allergic rhinitis (AR) patients and the diagnosis and prescription results of TM doctors based on the completed AR questionnaires. Explicit knowledge and implicit knowledge were defined based on the doctors' explicit scoring and feature evaluations of ML models, respectively. There were many differences between the explicit and implicit importance scores in this study. Implicit importance is more closely related to explicit importance in prescription than in diagnosis. The analysis results for eight doctors showed that our tool could successfully identify explicit and implicit knowledge in the PI process. This is the first study to evaluate the actual process by which explicit and implicit knowledge affect the choice of individual TM doctors and to identify assessment tools for the definition of the decision-making process in diagnosing PI and prescribing herbal treatments by TM clinicians. The assessment tool suggested in this study could be broadly used for the standardization of precision medicine, including TM therapeutics. [ABSTRACT FROM AUTHOR]