Objective: To predict future trends in the incidence of malaria cases in the southeast of Iran as the most important area of malaria using Seasonal Autoregressive Integrated Moving Average (SARIMA) model, and to check the effect of meteorological variables on the disease incidence. Methods: SARIMA method was applied to fit a model on malaria incidence from April 2001 to March 2018 in Sistan and Baluchistan province in southeastern Iran. Climatic variables such as temperature, rainfall, rainy days, humidity, sunny hours and wind speed were also included in the multivariable model as covariates. Then, the best fitted model was adopted to predict the number of malaria cases for the next 12 months. Results: The best-fitted univariate model for the prediction of malaria in the southeast of Iran was SARIMA (1,0,0)(1,1,1)12 [Akaike Information Criterion (AIC)=307.4, validation root mean square error (RMSE)=0.43]. The occurrence of malaria in a given month was mostly related to the number of cases occurring in the previous 1 (p=1) and 12 (P=1) months. The inverse number of rainy days with 8-month lag ( =0.329 2) and temperature with 3-month lag ( =-0.002 6) were the best predictors that could improve the predictive performance of the univariate model. Finally, SARIMA (1,0,0)(1,1,1)12 including mean temperature with a 3-month lag (validation RMSE=0.414) was selected as the final multivariable model. Conclusions: The number of malaria cases in a given month can be predicted by the number of cases in the prior 1 and 12 months. The number of rainy days with an 8-month lag and temperature with a 3-month lag can improve the predictive power of the model.

Objective: To investigate the caspase-1 dependent inflammatory pathway activity and interleukin-1β (IL-1β) secretion in murine macrophage cell lines J774G8 infected with Leishmania major (L. major) using caspase-1 activity assay and ELISA. Methods: Novy-MacNeal-Nicolle biphasic medium was applied to produce promastigote form of L. major. Metacyclic promastigotes in the stationary phase were applied to infect macrophage. Caspase-1 activity and IL-1β secretion were assessed by the CPP32/caspase-1 fluorometric protease assay and ELISA IL-1β kits, respectively, with time intervals of 6, 18 and 30 h. Results: Our study showed an increase in caspase-1 activity and IL-1β secretion in infected samples compared to non-infected macrophages. The highest increase in IL-1β production was observed after 6 h of infection. Conclusions: These results arise that the activation of inflammasome pathway could be one of the innate immunity pathways against L. major.

Objective: To determine the potential antibacterial activity of ethyl acetate and aqueous extracts from Mentha longifolia L. (M. longifolia) and hydroalcoholic extract of Zataria multiflora Boiss. (Z. multiflora) against important human pathogens. Methods: Pseudomonas aeruginosa, Shigella dysenteriae, Klebsiella pneumoniae (K. pneumonia). Enterobacter cloacae, Salmonella typhi, Proteus mirabilis, Serratia marcescens, Bacillus cereus, Staphylococcus saprophyticus and Staphylococcus aureus were kinds of pathogenic bacteria to determine the antibacterial effect of aqueous and ethyl acetate extracts of M. longifolia and hydroalcoholic extract of Z. multiflora using broth microdiluation method. Results: The lowest minimum inhibitory concentration and minimum bactericidal concentration values for K. pneumonia and Pseudomonas aeruginosa (1.25 and 2.5 mg/mL) were observed by the hydroalcoholic extract of Z. multiflora and the lowest minimum inhibitory concentration and minimum bactericidal concentration values for K. pneumonia and Serratia marcescens (2.5 and 5 mg/mL) were observed by the aqueous extracts of M. longifolia. Conclusions: In conclusion, it seems that Z. multiflora and M. longifolia extracts could inhibit the growth of all of the mentioned bacteria.