Risk of dyslipidemia and major adverse cardiac events with tofacitinib versus adalimumab in rheumatoid arthritis: a real-world cohort study from 7580 patients.

Objective: To compare the effects of tofacitinib and adalimumab on the risk of adverse lipidaemia outcomes in patients with newly diagnosed rheumatoid arthritis (RA). Methods: Data of adult patients newly diagnosed with RA who were treated with tofacitinib or adalimumab at least twice during a 3-year period from 1 January 2018 to 31 December 2020, were enrolled in the TriNetX US Collaborative Network. Patient demographics, comorbidities, medications, and laboratory data were matched by propensity score at baseline. Outcome measurements include incidental risk of dyslipidemia, major adverse cardiac events (MACE) and all-cause mortality. Results: A total of 7,580 newly diagnosed patients with RA (1998 receiving tofacitinib, 5,582 receiving adalimumab) were screened. After propensity score matching, the risk of dyslipidaemia outcomes were higher in the tofacitinib cohort, compared with adalimumab cohort (hazard ratio [HR] with 95% confidence interval [CI], 1.250 [1.076-1.453]). However, there is no statistically significant differences between two cohorts on MACE (HR, 0.995 [0.760-1.303]) and all-cause mortality (HR, 1.402 [0.887-2.215]). Conclusion: Tofacitinib use in patients with RA may increase the risk of dyslipidaemia to some extent compared to adalimumab. However, there is no differences on MACE and all-cause mortality.

A local cost simulation-based algorithm to solve distributed constraint optimization problems.

As an important incomplete algorithm for solving Distributed Constraint Optimization Problems (DCOPs), local search algorithms exhibit the advantages of flexibility, high efficiency and high fault tolerance. However, the significant historical values of agents that affect the local cost and global cost are never taken into in existing incomplete algorithms. In this article, a novel Local Cost Simulation-based Algorithm named LCS is presented to exploit the potential of historical values of agents to further enhance the exploration ability of the local search algorithm. In LCS, the Exponential Weighted Moving Average (EWMA) is introduced to simulate the local cost to generate the selection probability of each value. Moreover, populations are constructed for each agent to increase the times of being selected inferior solutions by population optimization and information exchange between populations. We theoretically analyze the feasibility of EWMA and the availability of solution quality improvement. In addition, based on our extensive empirical evaluations, we experimentally demonstrate that LCS outperforms state-of-the-art DCOP incomplete algorithms.

Exploration on Molecular Mechanism of Reversal Effect of Compound Danshen Tablets on Hepatic Fibrosis Based on Network Pharmacology.

Objective: To research the molecular mechanism of compound Danshen tablets in the treatment of hepatic fibrosis through network pharmacology. Methods: Traditional Chinese medicine systems pharmacology (TCMSP) and online Mendelian inheritance in man (OMIM) databases were searched for compound Danshen tablets' active ingredients o and hepatic fibrosis-related genes. The network enrichment of the targets of "herb-compound-target" was visualized and analyzed using Cytoscape software. Then, the screened target genes were used to construct a protein-protein interaction network. The DAVID enrichment database (the database for annotation, visualization, and integrated discovery) was adopted for GO (Gene Ontology) enrichment and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment of vital nodes. Results: The results yielded 234 targets of compound Danshen tablets; ten important targets (TNF, IL-10, TGF-ß1, EGF, CXCL16, CCL21, SERPINB5, SERPINA1, SOD2, and PPIG) for reversing hepatic fibrosis; and four core targets (TNF, IL-10, TGF-1, and EGF). In addition, KEGG enrichment analysis showed that compound Danshen tablets mainly involved FoxO and MAPK signaling pathways, as the key signaling pathways in the treatment of hepatic fibrosis. Conclusion: TNF, IL-10, TGF-1, and EGF and FOXO and MAPK signaling pathways play a key role in the pathogenesis of hepatic fibrosis. Based on the results of this study, the mechanism of action of compound Danshen tablets in the treatment of hepatic fibrosis may be associated with the regulation of FoxO and MAPK signaling pathways and inhibition of TNF, IL-10, TGF-1, and EGF.

3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads.

The existing deep convolutional neural networks (DCNNs) based methods have achieved significant progress regarding automatic glioma segmentation in magnetic resonance imaging (MRI) data. However, there are two main problems affecting the performance of traditional DCNNs constructed by simply stacking convolutional layers, namely, exploding/vanishing gradients and limitations to the feature computations. To address these challenges, we propose a novel framework to automatically segment brain tumors. First, a three-dimensional (3D) dense connectivity architecture is used to build the backbone for feature reuse. Second, we design a new feature pyramid module using 3D atrous convolutional layers and add this module to the end of the backbone to fuse multiscale contexts. Finally, a 3D deep supervision mechanism is equipped with the network to promote training. On the multimodal brain tumor image segmentation benchmark (BRATS) datasets, our method achieves Dice similarity coefficient values of 0.87, 0.72, and 0.70 on the BRATS 2013 Challenge, 0.84, 0.70, and 0.61 on the BRATS 2013 LeaderBoard, 0.83, 0.70, and 0.62 on the BRATS 2015 Testing, 0.8642, 0.7738, and 0.7525 on the BRATS 2018 Validation in terms of whole tumors, tumor cores, and enhancing cores, respectively. Compared to the published state-of-the-art methods, the proposed method achieves promising accuracy and fast processing, demonstrating good potential for clinical medicine.