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Tripterygium glycosides tablets (TGT) are the commonly used preparation for rheumatoid arthritis (RA). However, the changes in TGT on RA are still unclear at the metabolic level. This study aimed to reveal the biological processes of TGT in collagen-induced arthritis (CIA) rats through integrated metabolomics and network analysis. First, the CIA model in rats was established, and the CIA rats were given three doses of TGT. Then, the endogenous metabolites in the serum from normal rats, CIA rats, and CIA rats treated with varying doses of TGT were detected by UHPLC-QTOF-MS/MS. Next, univariate and multivariate statistical analyses were performed to find the differential metabolites. Finally, differential metabolites, metabolic pathways, and hub genes were analyzed integrally to reveal the biological processes of TGT in CIA rats. The paw diameter, arthritis score, immunoglobulin G (IgG) concentration, CT image, and histological assay showed that TGT had evident therapeutic effects on CIA rats. Untargeted metabolomics revealed that TGT could ameliorate the down-regulation of lipid levels in CIA rats. Four key differential metabolites were found including LysoP(18:0), LysoPA(20:4), LysoPA(18:2), and PS(O-20:0/17:1). The glycerophospholipid metabolic pathway was perturbed in treating CIA with TGT. A total of 24 genes, including PLD1, LPCAT4, AGPAT1, and PLA2G4A, were found to be the hub genes of TGT in CIA rats. In conclusion, the integrated analysis provided a novel and holistic perspective on the biological processes of TGT in CIA rats, which could give helpful guidance for further TGT on RA. Future studies based on human samples are necessary.

Background: Despite the progress in early diagnosis and treatment, prognosis of pancreatic adenocarcinoma (PAAD) is still poor. Basic leucine zipper and W2 domain-containing protein 1 (BZW1) and protein 2 (BZW2) are attached to the basic leucine zipper (bZIP) superfamily. Recently, BZW1 was identified as an important role in glycolysis of PAAD. However, the comprehensive reports about BZW1/2 in PAAD are not sufficient.Methods: RNA-seq data in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were retrospectively analyzed. We explored the expression of BZW1/2 in PAAD tissues and the associations between BZW1/2 and prognosis. In addition, the potential roles of BZW1/2 in tumor microenvironment (TME) of PAAD were analyzed. Finally, clinicopathological data of 49 patients with PAAD in our institution were collected. Immunohistochemistry was used to determine the expression of BZW1/2 in PAAD samples.Results: BZW1 and BZW2 were upregulated in PAAD tissues compared to normal tissues (p < 0.05). The expression of BZW1/2 were not significantly correlated with gender, grade and stage of PAAD (p > 0.05). High expression of BZW2 was an independent predictor for poor prognosis of PAAD (HR 1.834, 95%CI 1.303–2.581, p = 0.001). And a nomogram to predict overall survival (OS) of PAAD was established with a C-index of 0.685. BZW1 and BZW2 expression were positively associated with T cell mediated immune response to tumor cell and Th2 cells in xCell database. Tumor Immune Single-Cell Hub (TISCH) analyses indicated that BZW1 and BZW2 were mainly expressed in B cells and malignant cells. External cohort furtherly validated that high expression of BZW1 and BZW2 were predictors for poor prognosis of PAAD.Conclusion: We found that BZW1 and BZW2 are highly expressed in malignant cells and B cells in the TME of PAAD. BZW2 is an independent predictor for OS of PAAD. BZW1 and BZW2 expression are positively associated with T cell mediated immune response to tumor cell and Th2 cells in PAAD.

Accurate land-use/-cover change (LUCC) simulation is of great significance to issues closely related to regional planning and policy-making. Many models have been committed to conducting LUCC simulations for better decision-making. However, LUCC is a nonlinear spatiotemporal process with complex links and feedback as well as latent dependencies in both spatial and temporal neighborhoods. They are challenging to be integrally utilized using existing models that employ conventional statistical or machine learning methods, inevitably leading to inaccurate LUCC simulations. Aiming to handle this problem, this paper innovatively proposed a hybrid spatiotemporal convolution-based cellular automata model (ST-CA) by coupling nonlinear spatiotemporal dependency learning and CA-based spatial allocation. A three-dimensional convolutional neural network (3D-CNN) was introduced in the model to assimilate both the nonlinear driving mechanism and spatiotemporal dependencies. It contributes to generating more elaborate development potentials to increase simulation accuracy. To evaluate the model performance, an LUCC simulation was applied on a national scale in China by ST-CA. Four traditional CA models, namely, logistic regression (LR)-CA, random forest (RF)-CA, full-connected neural network (FCN)-CA, and convolutional neural network (CNN)-CA, were also developed for accuracy comparisons. The results demonstrate that the simulation by ST-CA reached an FoM of 18.42%, which outperformed the other models with accuracy increases of 11.65%, 13.11%, 7.01%, and 2.29%, respectively. The proposed model incorporating 3D-CNN effectively captured the nonlinear spatiotemporal properties in the LUCC process, which is promising for more accurate LUCC simulations.