Background: The diagnosis of prostatic pathology may be of challenging , as some  difficult and suspected, atypical  cases may lack basal cell layer by routine H&E sections . Antibodies against 34BE12(HMW-CK) and p63 aid the diagnosis of such cases , to distinguish benign from  malignant prostatic lesions.

Objective: to identify basal cells in atypical prostatic lesions ,and distinguish benign from malignant prostatic lesions.

Type of the study:  A retro-spective  study.

Methods:  115cases of  paraffin embedded prostatic tissue blocks ,diagnosed as : 76 cases were benign prostatic hy

In recent years and decades, there is a great need for developing new alternative energy sources or renewable sustainable energy. On the other hand, new technology approaches are growing . towards benefits from the valuable nutrients in wastewater which are unrecoverable by traditional wastewater treatment processes.  In the current study, a novel integrated system of microbial fuel cell and anoxic bioreactor (MFC-ANB) was designed and constructed to investigate its potential for slaughterhouses wastewater treatment, nitrogen recovery, and power generation. The system consisted of a double-chamber tubular type MFC with biocathode inoculated with freshly collected activated sludge. The MFC-ANB system was continuously fed with real-fi

Allium ampeloprasum extract has the potential to facilitate wound healing due to its anti-inflammatory, antioxidant, and antibacterial properties. In this study, we aimed to investigate the potential wound-healing activity of the methanol extract derived from Allium ampeloprasum. Using 30 healthy male Wester rats; control group: 15 rats; normal saline applied locally instead of plant-extract. Experimental group: 15 rats received a daily local application of 200 mg/kg of the plant extract. Then, for each healing period of 0, 5, or 10 days, each group was divided into three-subgroups: A, B, and C, each with five rats. There was a significant difference in the percentage of wound contraction between the control and experimental groups (p 0.02)

ABSTRACT The antibacterial and antbiofilm activities of water extract of Calendula officinalis flowers against some of enteropathogenic bacteria was studied, also phytochemical screening and determination of antioxidant activity of the extract has been investigated. The results showed that the water extract of C. officinalis exhibited a good antibacterial activity against all pathogenic bacterial isolates (Salmonella, Shigella dysenteriae, Shigella flexneri, Shigella sonnei and E. coli) especially at concentration 100 µg/ml in contrast with the control cefotan antibiotic. S. sonnei was more sensitive to extract than other bacteria with highest inhibition zone (23 mm). The preliminary phytochemical tests results indicated the presence

Some maps of the chaotic firefly algorithm were selected to select variables for data on blood diseases and blood vessels obtained from Nasiriyah General Hospital where the data were tested and tracking the distribution of Gamma and it was concluded that a Chebyshevmap method is more efficient than a Sinusoidal map method through mean square error criterion.

Copper with different concentrations doped with zinc oxide nanoparticles were prepared from a mixture of zinc acetate and copper acetate with sodium hydroxide in aqueous solution. The structure of the prepared samples was done by X-ray diffraction, atomic force microscopy (AFM) and UV-VIS absorption spectrophotometer. Debye-Scherer formula was used to calculate the size of the prepared samples. The band gap of the nanoparticle ZnO was determined by using UV-VIS optical spectroscopy.

KE Sharquie, AA Noaimi, MS Al-Zoubaidi, Journal of Cosmetics, Dermatological Sciences and Applications, 2015 - Cited by 8

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback