Incidence of human cytomegalovirus in breast carcinoma tissues is associated with a higher expression of growth factor receptor-bound protein 2

Background: female mammary carcinoma is the second most common cancer incidence among women and the fifth most common leading cause of cancer death worldwide. Premenopausal young women are more frequently targeted by inflammatory breast cancer (IBC), which is the most lethal form of breast cancer. The human cytomegalovirus (HCMV) has been identified as one of the viral infection with a higher frequency in carcinoma tissues of IBC than in non-IBC. The adaptor protein growth factor receptor-bound protein 2 (Grb2), was found to be upregulated in HCMV-infected cells and play as crucial role in cancer progression. Objective: this study aimed to assess the expression level of Grb2 in carcinoma tissues of IBC and non-IBC with HCMV infection. Patients and Methods: overall, 135 female diagnosed with breast carcinoma were enrolled in this study. Using conventional and real time polymerase chain reaction (PCR), we determined the incidence of HCMV and assessed the expression level of Grb2 mRNA in the breast cancer tissue samples. Results: Grb2 mRNA was significantly upregulated in HCMV+ IBC higher than in HCMV+ non-IBC. According to the molecular subtype, Grb2 mRNA was significantly higher upregulated in breast carcinoma tissues of HCMV+ hormonal positive (HP) than in triple negative (TN) counterparts. Conclusion: HCMV infection is associated with a high expression of Grb2 mRNA in IBC and that HP HCMV+ mammary carcinoma tissues confer upregulated Grb2 mRNA, suggesting a potential role of HCMV infection in enhancing of Grb2 mRNA expression in breast cancer with HP

Prediction of Cholera Incidence by Using the Comparison of Four Models: Autoregressive Integrated Moving Average Model, Holt Model, Brown Model and Simple Regression Model.

Aims: The forecast is a topical subject, which aid in decision making and its performance. The aim of this study is to predict the disease between 1995 and 2010. Place and Duration of Study: The choice of the disease is of after its appearance in our survey in the region of Gharb. Time series were illustrated between 1988-1994. Regional cholera annual data reported from ministry of health of Morocco. Methods: The comparison of four models by the analysis of the series of cholera cases includes examining graphic series by using EVIEWS software, the consideration of the autocorrelation and partial autocorrelation functions, define the model that suits, estimate it, diagnose, the residue analysis and compare the four models to choose the best for use in the forecasting process. Except the stationary series, we used IBMSPSS V22 for the other steps. Results: Throughout this work, it is assumed that the underlying structure of the series follows an autoregressive integrated moving average (ARIMA) process. It is presumed that observations of the disease follow an autoregressive moving average process of order AR (1) and therefore ARIMA (1, 1, 0). The comparison of models of time series is extended away by using the statistics fit of the model: MAPE, BIC and R-squared, in addition to the sig. of the parameters and the analysis of residues by Ljung-Box and Durbinwatson statistic. The validation of the series is estimated by the calculation of the Mean Absolute Percentage Error (MAPE) and the signification of the parameter with P =0,05. Conclusion: Brown model is the model of choice for the prediction of cholera cases.