BackgroundEstrogenic signals are suggested to have protection roles in the development of colorectal cancer (CRC). The G protein-coupled estrogen receptor (GPER) has been reported to mediate non-genomic effects of estrogen in hormone related cancers except CRC. Its expression and functions in CRC were investigated.MethodsThe expression of GPER and its associations with clinicopathological features were examined. The mechanisms were further investigated using cells, mouse xenograft models, and clinical human samples.ResultsGPER was significantly (p < 0.01) down regulated in CRC tissues compared with their matched adjacent normal tissues in our two cohorts and three independent investigations from Oncomine database. Patients whose tumors expressing less (n = 36) GPER showed significant (p < 0.01) poorer survival rate as compared with those with greater levels of GPER (n = 54). Promoter methylation and histone H3 deacetylation were involved in the down regulation of GPER in CRC cell lines and clinical tissues. Activation of GPER by its specific agonist G-1 inhibited proliferation, induced cell cycle arrest, mitochondrial-related apoptosis and endoplasmic reticulum (ER) stress of CRC cells. The upregulation of reactive oxygen species (ROS) induced sustained ERK1/2 activation participated in G-1 induced cell growth arrest. Further, G-1 can inhibit the phosphorylation, nuclear localization, and transcriptional activities of NF-κB via both canonical IKKα/ IκBα pathways and phosphorylation of GSK-3β. Xenograft model based on HCT-116 cells confirmed that G-1 can suppress the in vivo progression of CRC.ConclusionsEpigenetic down regulation of GPER acts as a tumor suppressor in colorectal cancer and its specific activation might be a potential approach for CRC treatment.Electronic supplementary materialThe online version of this article (doi:10.1186/s12943-017-0654-3) contains supplementary material, which is available to authorized users.
Objective Forecasting the seasonality and trend of pulmonary tuberculosis is important for the rational allocation of health resources; however, this foresting is often hampered by inappropriate prediction methods. In this study, we performed validation research by comparing the accuracy of the autoregressive integrated moving average (ARIMA) model and the back-propagation neural network (BPNN) model in a southeastern province of China. Methods We applied the data from 462,214 notified pulmonary tuberculosis cases registered from January 2005 to December 2015 in Jiangsu Province to modulate and construct the ARIMA and BPNN models. Cases registered in 2016 were used to assess the prediction accuracy of the models. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to evaluate the model fitting and forecasting effect. Results During 2005–2015, the annual pulmonary tuberculosis notification rate in Jiangsu Province was 56.35/100,000, ranging from 40.85/100,000 to 79.36/100,000. Through screening and comparison, the ARIMA (0, 1, 2) (0, 1, 1) 12 and BPNN (3-9-1) were defined as the optimal fitting models. In the fitting dataset, the RMSE, MAPE, MAE and MER were 0.3901, 6.0498, 0.2740 and 0.0608, respectively, for the ARIMA (0, 1, 2) (0, 1, 1) 12 model, 0.3236, 6.0113, 0.2508 and 0.0587, respectively, for the BPNN model. In the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.1758, 4.6041, 0.1368 and 0.0444, respectively, for the ARIMA (0, 1, 2) (0, 1, 1) 12 model, and 0.1382, 3.2172, 0.1018 and 0.0330, respectively, for the BPNN model. Conclusion Both the ARIMA and BPNN models can be used to predict the seasonality and trend of pulmonary tuberculosis in the Chinese population, but the BPNN model shows better performance. Applying statistical techniques by considering local characteristics may enable more accurate mathematical modeling.
Artesunate, a semi-synthetic derivative of arteminisin originally developed for the treatment of malaria, has recently been shown to possess antitumor properties. One of the cytotoxic effects of artesunate on cancer cells is mediated by induction of oxidative stress and DNA double-strand breaks (DSBs). We report here that in addition to inducing oxidative stress and DSBs, artesunate can also downregulate RAD51 and impair DSB repair in ovarian cancer cells. We observed that the formation of RAD51 foci and homologous recombination repair (HRR) were significantly reduced in artesunate-treated cells. As a consequence, artesunate and cisplatin synergistically induced DSBs and inhibited the clonogenic formation of ovarian cancer cells. Ectopic expression of RAD51 was able to rescue the increased chemosensitivity conferred by artesunate, confirming that the chemosensitizing effect of artesuante is at least partially mediated by the downregulation of RAD51. Our results indicated that artesunatecan compromise the repair of DSBs in ovarian cancer cells, and thus could be employed as a sensitizing agent in chemotherapy.
Objective: To investigate suitable forecasting models for tuberculosis (TB) in a Chinese population by comparing the predictive value of the autoregressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) hybrid model. Methods: We used the monthly incidence rate of TB in Lianyungang city from January 2007 through June 2016 to construct a fitting model, and we used the incidence rate from July 2016 to December 2016 to evaluate the forecasting accuracy. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to assess the performance of these models in fitting and forecasting the incidence of TB. Results: The ARIMA (10, 1, 0) (0, 1, 1) 12 model was selected from plausible ARIMA models, and the optimal spread value of the ARIMA-GRNN hybrid model was 0.23. For the fitting dataset, the RMSE, MAPE, MAE and MER were 0.5594, 11.5000, 0.4202 and 0.1132, respectively, for the ARIMA (10, 1, 0) (0, 1, 1) 12 model, and 0.5259, 11.2181, 0.3992 and 0.1075, respectively, for the ARIMA-GRNN hybrid model. For the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.2805, 8.8797, 0.2261 and 0.0851, respectively, for the ARIMA (10, 1, 0) (0, 1, 1) 12 model, and 0.2553, 5.7222, 0.1519 and 0.0571, respectively, for the ARIMA-GRNN hybrid model. Conclusions: The ARIMA-GRNN hybrid model was shown to be superior to the single ARIMA model in predicting the short-term TB incidence in the Chinese population, especially in fitting and forecasting the peak and trough incidence.
BackgroundSyphilis has made a dramatic resurgence in China during the past two decades and become the third most prevalent notifiable infectious disease in China. Female sex workers (FSWs) have become one of key populations for the epidemic. In order to investigate syphilis infection among different tiers of FSWs, a cross-sectional study was conducted in 8 sites in China.MethodsSerum specimens (n = 7,118) were collected to test for syphilis and questionnaire interviews were conducted to obtain socio-demographic and behavioral information among FSWs recruited from different types of venues. FSWs were categorized into three tiers (high-, middle- and low-tier FSWs) based on the venues where they solicited clients. Serum specimens were screened with enzyme-linked immunosorbent assay (ELISA) for treponemal antibody followed by confirmation with non-treponemal toluidine red unheated serum test (TRUST) for positive ELISA specimens to determine syphilis infection. A logistic regression model was used to determine factors associated with syphilis infection.ResultsOverall syphilis prevalence was 5.0% (95%CI, 4.5-5.5%). Low-tier FSWs had the highest prevalence (9.7%; 95%CI, 8.3-11.1%), followed by middle-tier (4.3%; 95%CI, 3.6-5.0%, P < 0.001) and high-tier FSWs (2.2%; 95%CI, 1.6-2.9%, P < 0.001). Factors independently associated with syphilis infection included older age, lower education level, geographic location, lower tier of typology, and injection drug use.ConclusionsThis multi-site survey showed a high prevalence of syphilis infection among FSWs and substantial disparities in syphilis prevalence by the tier of FSWs. The difference in syphilis prevalence is substantial between different tiers of FSWs, with the highest rate among low-tier FSWs. Thus, current surveillance and intervention activities, which have low coverage in low-tier FSWs in China, should be further examined.
BackgroundDrug-resistant tuberculosis (TB) has emerged as a major challenge toward TB control and prevention. In Lianyungang city, the extent and trend of drug resistant TB is not well known. The objective of the survey was to assess drug resistance pattern of MTB and risk factors for drug resistant TB, including multidrug resistance tuberculosis (MDR-TB) in this area.MethodsWe performed drug susceptibility testing on Mycobacterium tuberculosis (MTB) isolates with first- and second-line anti-tuberculosis drugs of 1012 culture positive TB cases by using the proportion method, who were consecutively enrolled from January 2011 to December 2012 in Lianyungang city, China. The patterns of drug resistance in MTB were investigated and multiple logistic regression analysis was performed to assess the risk factors for drug resistant TB.ResultsAmong the 1012 strains tested, 308 (30.4%) strains were resistant to at least one first-line drug; the prevalence of MDR-TB was 88 (8.7%), 5 (0.5%) strains were found to be extensively drug-resistant tuberculosis (XDR-TB). Female gender was a risk factor for MDR-TB (adjusted odds ratio (aOR) 1.763, 95% CI (1.060-2.934). The aged 28–54 years was significantly associated with the risk of MDR-TB with an aOR: 2.224, 95% CI (1.158-4.273) when compared with those 65 years or older. Patients with previous treatment history had a more than 7-fold increased risk of MDR-TB, compared with those never previously treated.ConclusionsThe burden of drug resistant TB cases is sizeable, which highlights an urgent need to reinforce control, detection and treatment strategies for drug resistant TB.
Many cancer drugs exert their therapeutic effect by inducing oxidative stress in the cancer cells. Oxidative stress compromises cell survival by inflicting lesions in macromolecules like DNA. Cancer cells rely on enhanced antioxidant metabolism and increased DNA repair function to survive oxidative assault. PARP1, a protein that senses DNA-strand breaks and orchestrates their repair, has an important role in the repair of oxidative DNA damage. Berberine, an alkaloid compound present in many herbal plants, is capable of inducing oxidative DNA damage and downregulating homologous recombination repair (HRR) in cancer cells. In this study, we demonstrated that berberine and PARP inhibitor niraparib have a synthetic lethal effect on ovarian cancer cells. Oxidative DNA damage was greatly induced by berberine in ovarian cancer cells. In addition, the level of RAD51 and the capacity of HRR were also reduced by berberine. Correspondingly, PARP became hyperactivated in response to berberine treatment. Cancer cells treated with berberine and niraparib in combination exhibited greatly increased apoptosis and remarkably reduced tumor growth in vivo. Together, the results indicate that by inducing oxidative DNA damage and downregulating HRR in cancer cells berberine is able to further sensitize cancer cells to PARP inhibition. Our findings demonstrate a potential therapeutic value of combined application of berberine and PARP inhibitors in ovarian cancer treatment.
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