Table_1_IL22RA1/JAK/STAT Signaling Acts As a Cancer Target Through Pan-Cancer Analysis.docx

Cytokines and cytokine receptors are important mediators in immunity and cancer development. Interleukin 22 (IL22) is one of the most important cytokines which has protumor effect. Given that common and specific roles of cytokines/receptors in multiple cancers, we conducted a pan-cancer study to investigate the role of IL22RA1 in cancer using The Cancer Genome Atlas (TCGA) database. Notably, we found IL22RA1 transcript was upregulated in 11 cancer types compared with their corresponding control. The mRNA expression level of IL22RA1 was highest in the pancreas among tumor tissues. The higher expression of IL22RA1 was associated with worse overall survival rate in patients. A total of 30 IL22RA1-correlated genes (e.g. IL17D, IL22RA2, IL20RB, IL10RA, IL10RB, TSLP and TYK2) are involved in the JAK/STAT pathway which promotes tumor progression. The upregulation of IL22RA1 in tumors was correlated with immune cell infiltration level. Higher expression of IL22RA2, IL20RB, IL10RA, IL10RB, TSLP, TYK2, STAT1 and STAT3 was associated with decreased overall survival rate in patients. IL22RA1 mutation was observed more in uterine cancer and melanoma compared with the other cancer types. Deactivation of IL22RA1 induced a lot of changes in gene expression. IL22RA1 mutants had upregulated DNA damage/repair genes in uterine cancer, whereas downregulated genes in the FoxO signaling pathway. In melanoma, mutation of IL22RA1 can upregulate the HIF signaling pathway but downregulate metabolic pathways. Our study suggests that IL22RA1/JAK/STAT signaling can be an important target for cancer treatment.</p

Passive smoking may be associated with bleeding of cerebral arteriovenous malformation in non-smoking women: a retrospective analysis

ABSTRACT Background Smoking has been considered to be a risk factor for cardiovascular disease, cancer, depression and other diseases in previous reports, and active smoking is considered to be a risk factor for hemorrhagic stroke. In addition, a retrospective study showed that male smokers were at increased risk of bleeding from arteriovenous malformation (AVM), compared with non-smokers. However, the effect of passive smoking on rupturing of cerebral AVM in non-smoking women has not been addressed. Objective This study aimed to assess the impact of tobacco exposure on AVM bleeding risk in non-smoking women. Methods A total of 393 non-smoking women diagnosed with AVM were included. They were divided into a bleeding group (205 women) and a non-bleeding group (188 women). We conducted univariate and multivariate analysis on these two groups. In univariate analysis, risk factors that might be related to AVM bleeding were analyzed. In multivariate analysis, the relationship between passive smoking and AVM rupture was analyzed by correcting confounding factors. Results Multivariate analysis showed that the proportion of passive smoking was statistically different between the bleeding group and the non-bleeding group (OR = 1.609; CI = 1.031-2.509; p = 0.036). Conclusion Passive smoking may increase the risk of AVM bleeding in non-smoking women. This increased risk may be related to the inflammatory response, vascular wall damage, hemodynamic disorders, changes in atherosclerosis and changes in gene expression caused by passive smoking.</div

Electric vehicle battery swap station location-routing problem with stochastic demands using hybrid variable neighborhood search

This is the experimental data of the manuscript entitled "Electric vehicle battery swap station location-routing problem with stochastic demands using hybrid variable neighborhood search". All data are saved in the file with txt format. </div

test

This is the experimental data of the manuscript entitled "Electric vehicle battery swap station location-routing problem with stochastic demands using hybrid variable neighborhood search". All data are saved in the file with txt format. <br

A fuzzy optimization model for the electric vehicle routing problem with time windows and recharging stations

This is the experimental data of the manuscript entitled "A fuzzy optimization model for the electric vehicle routing problem with time windows and recharging stations". All data are saved in the file with txt format

A novel location-routing problem in electric vehicle transportation with stochastic demands

This is the experimental data of the manuscript entitled "A novel location-routing problem in electric vehicle transportation with stochastic demands". All data are saved in the file with txt format. <br

A novel location-routing problem in electric vehicle transportation with stochastic demands

This is the experimental data of the manuscript entitled "A novel location-routing problem in electric vehicle transportation with stochastic demands". All data are saved in the file with txt format

Fuzzy optimization model for electric vehicle routing problem with time windows and recharging stations

This is the experimental data of the manuscript entitled "fuzzy optimization model for electric vehicle routing problem with time windows and recharging stations". All data are saved in the file with txt format

Fuzzy optimization model for electric vehicle routing problem with time windows and recharging stations

This is the experimental data of the manuscript entitled "Fuzzy optimization model for electric vehicle routing problem with time windows and recharging stations". All data are saved in the file with txt format

Collaborative Vehicle Routing Problem with Rough Location using Extended Ant Colony Optimization Algorithm

<p>This is the experimental data of the manuscript entitled "Collaborative Vehicle Routing Problem with Rough Location using Extended Ant Colony Optimization Algorithm". All data are generated through computer simulation and saved in the file with txt format. The data are consistent with the real-world case. For example, DATA_N_60_D_4_V_12 represents the case that contains 4 depots, 60 customers and 12 vehicles. The “DATA_N_77_D_3_V_9.txt” and “DATA_N_97_D_3_V_9.txt” are used in Section 5.2 as illustrative examples corresponding to figure 4 and figure 5, respectively. Other data are used in Section 5.3.</p