The present study was carried to evaluate antibacterial activity of Acetone, Alcholic (cold and hot) and Aqueaus(water) extracts of Citrus aurantifoliaseeds,against growth of some bacteria isolated from burns infections(Pseudomonas aeruginosa,Escherichia coli, Klebsiellapneumonia,Staphylococcusaureus)fromKindy HospitalIn Baghdad from March to June 2012.Antibiotic Sensitivity was done for all isolated bacteria used in this study.Results showed variation in antibacterial activity of different extracts against all tested bacteria by well diffusion technique in agar and measuring the diameter of inhibition zone, at concentration 250Mg-ml. Acetone extract had the greatest inhibitory effect followed by hot alcoholci extract, and then cold alcoholic extract,while the aqueous extract slightly inhibited bacteria. Minimum inhibitory concentration(MIC)were determined for all extracts against studying bacteria and found(12.5-50)mg-ml for acetone and alcoholic extracts, MIC for aqueous extract was 50mg-ml forPseudomonasaeruginosa and Escherichia coli,while was no effect onKlebsiellapneumonia and Staphylococcusaureus. Minimum Bacterial Concentration(MBC)were determined and was found25-50mg-ml for acetone extract,hot water was25mg-ml, cold alcoholic extract was 50mg-ml forPseudomonasaeruginosa,Escherichia coli andKlebsiella pneumonia but showed no effect on Staphylococcus aureus, aqueous extract showed negative effect on alltested bacteria. The antimicrobial activity of hot alcoholic extract of seeds was investigated practically (in vivo) by treating burns mices infected with tested bacteria(Pseudomonas aeruginosa,Escherichia coli and Klebsiellapneumonia),the results revealed good recovery at short time comparing with antibiotic(Flamazine) used at the same time.
Abstract The results of isolation, morphological and microscopic diagnosis, Chromic Agar, Vitik technology and Bact Alert showed that the diagnosis of fungi isolated from blood samples of end-stage renal patients who did not undergo dialysis and those who underwent dialysis was 60 samples for each type. The total number of fungal isolates isolated from people who did not undergo dialysis was 26 pathogenic fungal isolates, with a percentage frequency of 43.33%. In this study, 4 genera of pathogenic fungi were identified: Candida spp, Rhodotorula spp, Cryptococcus spp. and Aspergillus spp. The number of Candida isolates reached 13 isolates, with a frequency of 50%. The results also showed that the diagnosed species from the genus Rhodotorula
... Show MoreBackground: Mycoplasma pneumoniae (M. pneumoniae) is an important respiratory bacterial pathogen, especially among children. It causes acute upper and lower respiratory infections.Objective: This study was aimed to measure anti- M. pneumoniae antibodies among hospitalized children who were admitted to hospital diagnosed with acute respiratory tract infections.Method: Automated ELISA technique was performed to detect anti- M. pneumoniae antibodies (IgM and IgG antibodies) in serum from 108 children less than 5 years old. The children were admitted to the Pediatric Teaching Hospital in Suleimani city/Kurdistan Region/Iraq because of acute respiratory tract infections. A questionnaire was designed to collect demographic and clinical data fr
... Show MoreSystemic lupus erythematosus (SLE) is one of the autoimmune disorders, generated by a production of specific autoantibodies against self-antigens before the occurrence of clinical symptoms. The etiology of disease is still unknown, although there have been several infectious agents that have been associated with SLE development, especially in genetically predisposed individuals. Herpes simplex virus-I and -II (HSV-I and -II) and Toxoplasma gondiiare two infectious agents that have been suggested to be involved in SLE etiology. Accordingly, the present study assessed anti- HSV-I and -II and anti-T. gondii IgG and IgM antibodies by enzyme linked immunosorbent assay in sera of 64 SLE female patients and 32 healthy control women. The patients w
... Show MoreDBN Dr. Liqaa Habeb, International Journal of Multidisciplinary Reseach, 2015
Methods of speech recognition have been the subject of several studies over the past decade. Speech recognition has been one of the most exciting areas of the signal processing. Mixed transform is a useful tool for speech signal processing; it is developed for its abilities of improvement in feature extraction. Speech recognition includes three important stages, preprocessing, feature extraction, and classification. Recognition accuracy is so affected by the features extraction stage; therefore different models of mixed transform for feature extraction were proposed. The properties of the recorded isolated word will be 1-D, which achieve the conversion of each 1-D word into a 2-D form. The second step of the word recognizer requires, the
... Show MoreBackground: Toxoplasmosis is a very common infection caused by the obligate intracellular protozoan parasite. This parasite is called Toxoplasma gondii widely distributed around the world . Toxoplasma gondii can be vertically transmitted to the fetus during pregnancy and may cause wide range of clinical manifestations in the offspring.
Objective: To determine seroprevalence Immunoglobulin G (IgG) and Immunoglobulin M (IgM ) to toxoplasma gondii among pregnant women and to identify the risk factors.
Type of the study: A cross-sectional study.
Methods: A total of 110 blood samples of pregnant women were collected from
... Show MoreTwo unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.