A total of 60 cotton swabs are collected from patients suffering from burn wound and surgical site infections admitted to Baghdad Teaching Hospital and Burn Specialist Hospital in Baghdad city during 9/2013 to 11/2013. All cotton swabs are cultured initially on blood agar and MacConkey agar and subjected for standard bacteriological procedures for bacteriological diagnosis. Twenty samples out of sixty are identified as Pseudomonas aeruginosa by conventional methods. The results of antibiotic susceptibility test illustrate that the antibiotics resistance rate of Pseudomonas aeruginosa isolates is as follows:100% (2020) for ceftriaxone, cefepime and carbencillin, 70% (14/20) for amikacin, 65%(13/20) for tobramycin, ceftazidim and gentamycin,

Background: Due to the variations in tooth anatomy and size among different populations, this study aimed to compare the mesiodistal width of primary second molars in Iraqi children with the mesiodistal width of stainless-steel crowns from different companies. Materials and Methods: This cross-sectional study was conducted on 220 intact maxillary and mandibular primary second molars selected from boys and girls’ Iraqi children aged 8-9 years collected from different primary schools in Baghdad city. The mesiodistal dimensions of the selected teeth and the available maxillary and mandibular stainless-steel crowns from three different companies were measured by using a 3-D scanner, and then the whole measurements were calculated usin

   The potential application of granules of brick waste (GBW) as a low-cost sorbent for removal of Ni⁺²ions from aqueous solutions has been studied. The properties of GBW were determined through several tests such as X-Ray diffraction (XRD), Energy dispersive X-ray (EDX), Scanning electron microscopy (SEM), and BET surface area. In batch tests, the influence of several operating parameters including contact time, initial concentration, agitation speed, and the dose of GBW was investigated. The best values of these parameters that provided maximum removal efficiency of nickel (39.4%) were 1.5 hr, 50 mg/L, 250 rpm, and 1.8 g/100mL, respectively. The adsorption data obtained by batch experiments subjected to the Three i

Leuciscidae species are the abundant and widely distributed fish species in Iraq's inland waters. They are complex species, and morphology makes them difficult to identify. Molecular analysis achieved and confirmed the morphological characters. Twenty specimens of Acanthobrama marmid were collected from two localities at Tigris River, in the middle of Iraq; 15 specimens from the Al-Zubaydia sub-district and five specimens from Al-Tharthar Lake. We used the mitochondrial DNA cytochrome b (cytb) gene to sequence the DNA of A. marmid. The following analysis are compared the sequences with those of other fish genera and species found in the Gene Bank. The barcoding result (DNA sequencing) in fishes found in the same family (Leuciscidae) showed

The removal of turbidity from produced water by chemical coagulation/flocculation method using locally available coagulants was investigated. Aluminum sulfate (alum) is selected as a primary coagulant, while calcium hydroxide (lime) is used as a coagulant aid. The performance of these coagulants was studied through jar test by comparing turbidity removal at different coagulant/ coagulants aid ratio, coagulant dose, water pH, and sedimentation time. In addition, an attempt has been made to examine the relationship between turbidity (NTU) and total suspended solids (mg/L) on the same samples of produced water. The best conditions for turbidity removal can be obtained at 75% alum+25% lime coagulant at coagulant dose of 80 m

The faujasite type Y zeolite catalyst was prepared from locally available kaolin. For prepared faujasite type NaY zeolite X-ray, FT-IR, BET pore volume and surface area, and silica/ alumina were determined. The Xray and FT-IR show the compatibility of prepared catalyst with the general structure of standard zeolite Y. BET test shows that the surface area and pore volume of prepared catalyst were 360 m2 /g and 0.39 cm3 /g respectively.
The prepared faujasite type NaY zeolite modified by exchanging sodium ion with ammonium ion using ammonium nitrate and then ammonium ion converted to hydrogen ion. The maximum sodium ion exchange with ammonium ion was 53.6%. The catalytic activity of prepared faujasite type NaY, NaNH4Y and NaHY zeolites

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

he effect of different cultural conditions on production of bioemulsifier from Serratia marcescens S10 was determined; different carbon and nitrogen sources were used such as: different oils include: edible (vegetable) oils (olive oil, sesame oil, sun flower oil and corn oil) and heavy oils (oil 150, oil 60, oil 40) as carbon sources and (NH4Cl, casein, (NH4)2SO4, peptone, tryptone, gelatin and yeast extract) as nitrogen sources were added to production media. Bioemulsifier was estimated by measuring the surface tension (S.T), emulsification activity (E.A) and emulsification index (E24%). The best results of bioemulsifier production from Serratia marcescens S10 were obtained at pH8 and incubated at 37ºC for 5days, using sesame oil

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de