Investigation of olfactory receptor family 51 subfamily j member 1 (OR51J1) gene susceptibility as a potential breast cancer-associated biomarker.

Among cancer treatment methods, targeted therapy using cancer-associated biomarkers has minimum side effects. Recently olfactory receptor (OR) family attracts the researcher's attention as a favorable biomarker of cancer. Here, a statistical approach using complete data from the human protein atlas database was used to evaluate the potential of OR51J1 gene as a cancer-associated biomarker. To confirm the findings of statistical analysis, the OR51J1 mRNA and protein expression levels in breast tumor and normal tissue were measured using quantitative Real Time PCR (qRT-PCR) and immunohistochemistry (IHC) techniques. The association with clinicopathological factors was analyzed. Statistical analysis revealed that OR51J1 has a high expression level in more than 20 types of cancer tissues without any expression in 44 normal tissues. In 15 cancer types, including breast cancer, expression score was more than 90%. The qRT-PCR analysis in breast cancer showed OR51J1 have significantly higher expression level in tumors than normal tissues (2.91 fold). The IHC results showed OR51J1 expression on other cellular subtypes than tumor and normal cells, including myoepithelium, fibroblast, and lymphocytes. OR51J1 protein expression in invasive cells, as well as its overall score, showed a significant correlation with ER and PR expression and breast cancer (BC) subtypes. Results revealed the potential of OR51J1 as a cancer-associated biomarker for the diagnosis of breast cancer at the mRNA level.

Virulence Genes Profile of Multidrug Resistant Pseudomonas aeruginosa Isolated from Iranian Children with UTIs.

Virulent and resistant strains Pseudomonas aeruginosa (P. aeruginosa) is one of the most important cause of UTIs in pediatrics. The present study was carried to investigate the frequency of virulence factors in the multi-drug resistant strains of P. aeruginosa isolated from pediatrics hospitalized due to the UTIs. One-hundred and forty three urine samples were collected from pediatric patients suffered from UTIs. Samples were cultured and those that were P. aeruginosa positive were analyzed for the presence of putative virulence genes. Seventy one out of 143 samples (49.65%) were positive for P. aeruginosa. Monthly, sex and age-dependent prevalence were seen for P. aeruginosa. Bacterial strains had the highest levels of resistance against ampicillin (95.77%), gentamicin (92.95%) and ciprofloxacin (81.69%). Of 71 P. aeruginosa isolates, 12 strains were resistant to more than 9 antibiotics (16.90%). The most commonly detected virulence factors in the cases of urethral infections were exoU and plcH while those of pyelonephritis and cystitis were were exoS and lasB. Our findings should raise awareness about antibiotic resistance in hospitalized pediatrics with UTIs in Iran. Clinicians should exercise caution in prescribing antibiotics, especially in cases of UTIs. Such information can help in identifying these virulence genes as useful diagnostic markers for clinical P. aeruginosa strains isolated from UTIs.

Application of zero-inflated poisson mixed models in prognostic factors of hepatitis C.

BACKGROUND AND OBJECTIVES: In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C. METHODS: The data was collected from a longitudinal study during 2005-2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared. RESULTS: The results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors. CONCLUSIONS: The results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.