Background Fibroblast growth factor receptor 2 (FGFR2) and trinucleotide repeat-containing 9 (TNRC9) gene polymorphisms have been associated with some cancers. We aimed to assess the association of FGFR2 rs2981582 and TNRC9 rs12443621 polymorphisms with hepatocellular cancer risk. Methods One hundred patients with HCV-induced HCC, 100 patients with chronic HCV infection, and 100 controls were genotyped for FGFR2 rs2981582 and TNRC9 rs12443621 using allele-specific Real-Time PCR analysis. Results FGFR2 rs2981582 genotype TT was associated with increased risk of HCC when compared to controls (OR = 3.09, 95% CI = 1.24–7.68). However, it was significantly associated with a lower risk of HCC when using HCV patients as controls (OR =

Particulate matter (PM) emitted from diesel engine exhaust have been measured in terms of mass, using
99.98 % pure ethanol blended directly, without additives, with conventional diesel fuel (gas – oil),to
get 10 % , 15 %, 20 % ethanol emulsions . The resulting PM collected has been compared with those
from straight diesel. The engine used is a stationary single cylinder, variable compression ratio Ricardo
E6/US. This engine is fully instrumented and could run as a compression or spark ignition.
Observations showed that particulate matter (PM) emissions decrease with increasing oxygenate
content in the fuel, with some increase of fuel consumption, which is due to the lower heating value of
ethanol. The reduction in

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

In Present study, 25 clinical isolates of Proteus spp. of clinical samples, urine, wounds and burns collected from different hospitals in Baghdad city, all isolates were identified as Proteus mirabilis using different bacteriological media, biochemical assays and Vitek-2 system. It was found that 15 (60%) isolates were identifying as P. mirabilis. The susceptibility of P. mirabilis isolates to cefotaxime was 66.6 %, while to ceftazidime was 20%. Extended spectrum β-lactamses producing Proteus was 30.7 %. DNA of 5 isolates of P. mirabilis was extracted and detection for blaVEB-1 gene by using multiplex polymerase chain reaction (PCR). Results showed that the presence of this gene in all tested isolates, as an important indicator for increas