Gu-tong formula (GTF) has achieved good curative effects in the treatment of cancer-related pain. However, its potential mechanisms have not been explored. We used network pharmacology and molecular docking to investigate the molecular mechanism and the effective compounds of the prescription. Through the analysis and research in this paper, we obtained 74 effective compounds and 125 drug-disease intersection targets to construct a network, indicating that quercetin, kaempferol, and β-sitosterol were possibly the most important compounds in GTF. The key targets of GTF for cancer-related pain were Jun proto-oncogene (JUN), mitogen-activated protein kinase 1 (MAPK1), and RELA proto-oncogene (RELA). 2204 GO entries and 148 pathways were obtained by GO and KEGG enrichment, respectively, which proved that chemokine, MAPK, and transient receptor potential (TRP) channels can be regulated by GTF. The results of molecular docking showed that stigmasterol had strong binding activity with arginine vasopressin receptor 2 (AVPR2) and C-X3-C motif chemokine ligand 1 (CX3CL1) and cholesterol was more stable with p38 MAPK, prostaglandin-endoperoxide synthase 2 (PTGS2), and transient receptor potential vanilloid-1 (TRPV1). In conclusion, the therapeutic effect of GTF on cancer-related pain is based on the comprehensive pharmacological effect of multicomponent, multitarget, and multichannel pathways. This study provides a theoretical basis for further experimental research in the future.
Background Physical activity presents significant protection against death from cancer in the general population, so the global recommendations on physical activity for health are recommended by the WHO. While the recommendation is a guideline for general population, whether all cancer patients could get benefits from physical activity and whether the cancer patients who did not meet the requirement of the recommendation could get benefits from the physical activity, compared with the cancer patients with no physical activity, are unclear. Accordingly, we conducted a meta-analysis to identify whether the physical activity, even if low level of physical activity, could reduce the mortality of various cancer patients. Method We conducted a systematic search of PubMed, Embase, and Cochrane Library for published cohorts and case-control studies of cancer survivors with physical activity comparing with no physical activity and reported outcomes of mortality through October 15, 2018. Two investigators independently reviewed the included studies and extracted relevant data. The effect estimate of interest was the hazard ratios (HRs). Results There are 21811 participants in total in the nine studies, and 2386 cancer deaths in this meta-analysis. Among them, 1 was a case-control study and 8 were cohort studies. The meta-analysis results showed that physical activity was associated with a significantly reduced risk of mortality in cancer survivors, with a pooled HR and 95% CI of 0.66 (0.58∼0.73), reducing mortality by 34% and also suggested that low level of physical activity could reduce the mortality with an HR and 95% CI of 0.60 (0.50∼0.69). Conclusion The results of this meta-analysis demonstrated that postdiagnosis physical activity, no matter the level of physical activity, could significantly reduce the mortality by 34%, compared with the no physical activity. At the same time, the results also suggested that cancer survivors undergoing low level of physical activity had a 40% reduction in mortality, which means that the cancer patients with poor ECOG need to do physical activity as much as they can, even if the amount of physical activity was low.
In the 5G communication systems, polar code has been adapted as the control channel coding solution in the enhanced mobile broadband (eMBB) scenario. Although different decoding schemes, including belief propagation (BP) and successive cancellation (SC) based algorithms, have been proposed, the decoding complexity as well as the latency are still significant. To address this critical issue, several low-complexity schemes, e.g., the use of simplified decoding operation and stop the decoding operation in earlier stage, have been proposed recently. However, conventional early stopping strategies have to check a pre-defined metric in each iteration, and the associated decoding delay is significant. In this paper, to address this challenge, we proposed a low-complexity BP based decoding scheme, which contains the decodability detection stage and the early stopping prediction stage. The decodability detection stage can identify the codewords in the deep channel fading environment and eliminate the unnecessary decoding operations to reduce the decoding complexity, while the early stopping prediction stage can directly predict the required number of iterations rather than checking the metric in each iteration to avoid the associated decoding delay. Through the above two approaches, our proposed scheme is shown to achieve 71% decoding delay reduction and maintain the same decoding performance as traditional BP, G-matrix, MinLLR schemes.
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