Some recent studies have suggested that the use of dipeptidyl peptidase-4 inhibitors (DPP4i) is associated with cancer development. However, some other studies suggest no such association. The aim of the present study was to evaluate the effect of DPP4i on the risk of developing cancers. The electronic databases PubMed, Medline, EMBASE, Web of Science and Cochrane Library and the clinical trial registry were searched for published and unpublished randomized clinical trials on humans. Eligible studies were RCTs conducted in patients with type 2 diabetes mellitus, comparing DPP4i with a placebo or other active drugs. A total of 72 trials with 35,768 and 33,319 patients enrolled for DPP4i and the comparison drugs, respectively. Overall, no significant associations were detected between the use of DPP4i and cancer development, in comparison with the use of other active drugs or placebo. The results were consistent across pre-defined subgroups stratified by type of DPP4i, type of cancer, drug for comparison, trial duration, or baseline characteristics. The results of this meta-analysis suggest that patients with type 2 diabetes treated with DPP4i do not have a higher risk of developing cancers than patients treated with a placebo or other drugs.
The differential expressed miRNAs and their predicted target genes that involved in Jak-STAT signaling pathway, MAPK signaling pathway and PPAR signaling pathway indicating their potential roles in pancreatic carcinogenesis and progress.
Pancreatic cancer (PC) has a high rate of mortality and a poorly understood mechanism of progression. Investigation of the molecular mechanism of PC and exploration of the specific markers for early diagnosis and specific targets of therapy are key points to prevent and treat PC effectively and to improve their prognosis. In our study, expression profiles experiment of para-carcinoma, carcinoma and relapse human PC was performed using Agilent human whole genomic oligonucleotide microarrays with 45 000 probes. Differentially expressed genes related with PC were screened and analysed further by Gene Ontology term analysis and Kyoto encyclopaedia of genes and genomes pathway analysis. Our results showed that there were 3853 differentially expressed genes associated with pancreatic carcinogenesis and relapse. In addition, our study found that PC was related to the Jak-STAT signalling pathway, PPAR signalling pathway and Calcium signalling pathway, indicating their potential roles in pancreatic carcinogenesis and progress.
The transformation and upgrading of the industrial manufacturing field brings important development opportunities for the promotion and deployment of the Industrial Internet of Things (IIoT). How to connect huge number of IIoT nodes to the current IP-based Internet for promoting the integration of industrialization and informationization is a key issue towards IIoT. Targeting at the insufficiency that existing algorithms only support a single channel and utilize limited number of time slots, which results in the deteriorated performance for solving the problem of resource allocation for nodes with different priorities. In this paper the self-similarity of the observation data of the IIoT nodes is evaluated to dynamically prioritize the nodes, and then Deep Q Network (DQN) algorithm is adopted to design an adaptive resource allocation method that takes into account the characteristics of diverse node priorities. Simulation results demonstrate that the proposed method can effectively improve network throughput and reduce data transmission delay in comparison with existing methods. INDEX TERMS Industrial Internet of Things, deep Q network, resource allocation, priority.
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