Peroxidase is a class of oxidation-reduction reaction enzyme that is useful for accelerating many oxidative reactions that protect cells from the harmful effects of free radicals. Peroxidase is found in many common sources like plants, animals and microbes and have extensive uses in numerous industries such as industrial, medical and food processing. In this study, P. aeruginosa was harvested to utilize and study its peroxidases. P. aeruginosa was isolated from a burn patient, and the isolate was verified as P. aeruginosa using staining techniques, biochemical assay, morphological, and a sensitivity test. The gram stain and biochemical test result show rod pink gram-negative bacteria, and ensure that the isolate was that of P. aeruginosa. Optimization for bacterial growth were done by used more than pH (5,7,9) and temperatures (32,35,37°C), and it was found that the best growth conditions were at pH 5.5, producing (4.5x108cells), and a temperature of 37°C, with (5.25x108cells) being produced. Intracellular enzymes were extracted by ultra-sonication that used frequencies of ultrasound 30 kHz for 20 min in 4 °C, and was centrifuged at 13000×g for 5min. The supernatant was then re-used as a crude enzymatic extract and the cell pellet was discarded. Purification of peroxidase was accomplished by using salt precipitation, dialysis, gel filtrations and ion exchange chromatographic techniques. The result shows that gel filtration has optimal specific activity and purification fold at (61 U/ml), purification fold 6 times and then the improvement enzyme was applied as H2O2 scavenging activity antioxidant by used three concentration of enzyme (10,40,60 µg/ml), and show higher scavenging activity at 60 µg/ml, which reached to 45% scavenging activity. The enzyme was also used as anticancer agent, which was verified by using three concentration of enzyme (10,15,20 µg/ml) which show a significant kill for Mcf-7cells at (15µg/ml), with cytotoxicity activity reaching (45%).
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The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi
... Show MoreThe purpose of this study was to investigate the difference in mandibular trauma caused by two mechanisms for the delivery of missile injuries: firearms and improvised explosive devices (IEDs). The data investigated included sex, age, mechanism of injury, and other clinical and radiographic manifestations. Seventy consecutive patients, predominantly male, with a mean age of 28.6 ± 14 years (range 2–60 years) were enrolled: 38 patients (54.3%) sustained mandibular fractures caused by bullet injuries and 32 patients (45.7%) had mandibular fractures caused by IED explosion injuries. The study revealed that the differences in most of the investigated variables were not statistically significant; the only significant differences were the inci
... Show MoreThis paper considers and proposes new estimators that depend on the sample and on prior information in the case that they either are equally or are not equally important in the model. The prior information is described as linear stochastic restrictions. We study the properties and the performances of these estimators compared to other common estimators using the mean squared error as a criterion for the goodness of fit. A numerical example and a simulation study are proposed to explain the performance of the estimators.