Phytochemical Regulation of RNA in Treating Inflammatory Bowel Disease and Colon Cancer: Inspirations from Cell and Animal Studies

Texture Features of Computed Tomography Image under the Artificial Intelligence Algorithm and Its Predictive Value for Colorectal Liver Metastasis

The aim of this research was to investigate the predictive role of texture features in computed tomography (CT) images based on artificial intelligence (AI) algorithms for colorectal liver metastases (CRLM). A total of 150 patients with colorectal cancer who were admitted to the hospital were selected as the research objects and randomly divided into three groups with 50 cases in each group. The patients who were found to suffer from the CRLM in the initial examination were included in group A. Patients who were found with CRLM in the follow-up were assigned to group B (B1: metastasis within 0.5 years, 16 cases; B2: metastasis within 0.5–1.0 years, 17 cases; and B3: metastasis within 1.0–2.0 years, 17 cases). Patients without liver metastases during the initial examination and subsequent follow-up were designated as group C. Image textures were analyzed for patients in each group. The prediction accuracy, sensitivity, and specificity of CRLM in patients with six classifiers were calculated, based on which the receiver operator characteristic (ROC) curves were drawn. The results showed that the logistic regression (LR) classifier had the highest prediction accuracy, sensitivity, and specificity, showing the best prediction effect, followed by the linear discriminant (LD) classifier. The prediction accuracy, sensitivity, and specificity of the LR classifier were higher in group B1 and group B3, and the prediction effect was better than that in group B2. The texture features of CT images based on the AI algorithms showed a good prediction effect on CRLM and had a guiding significance for the early diagnosis and treatment of CRLM. In addition, the LR classifier showed the best prediction effect and high clinical value and can be popularized and applied.

Aucuboside Inhibits the Generation of Th17 Cells in Mice Colitis

Aucuboside is an iridoid glycoside extracted from traditional Chinese medicine such as Rehmannia glutinosa, possessing a wide range of biological activities, including antioxidant, anti-aging, anti-inflammatory, and anti-fibrotic effects. The effects of aucuboside on inflammatory bowel disease (IBD) have not been studied. Therefore, the effects of aucuboside on the generation of Foxp3+ regulatory T (Treg) cells and IL-17–producing T helper (Th17) cells in colitis were studied. A mouse colitis model was established by intracolonic administration of 2,4,6-trinitrobenzene sulfonic acid (TNBS) to mimic human IBD. The generation of Treg and Th17 cells was evaluated by flow cytometry. Aucuboside significantly alleviated colitis symptoms, including weight loss, high disease activity index, and inflammatory responses. The generation of Th17 cells in colitis was significantly inhibited by aucuboside and accompanied by the suppression of IL-17 expression. In Raw264.7 cells, the LPS-induced increase in IL-17 expression was also suppressed by aucuboside, which was significantly blocked by the RORγt inhibitor sr2211. In addition, the decrease in the proportion of Treg cells was also partially reversed by aucuboside, which may reflect the aucuboside-induced inhibition of Th17 cells. This previously unrecognized immunoregulatory function of aucuboside may have clinical applications in IBD.