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The advance of technology has created a demand on face detection, which is the first phase in face recognition, one of the important biometric technologies. In this research, the Facial Feature Crop Detection Based on Haar Feature and Adaboost Training framework is presented. The framework’s objective is to detect face crops and each facial feature’s crop like eye, mouth and nose crops; the framework depends on using Haar-like features and the AdaBoost learning algorithm. This algorithm has been chosen according to its computational effectiveness and easiness. The presented framework was tested on a dataset containing 500 colour images of faces collected from the FERET database. The training achieved 100% accuracy and 98.8% achieved for the testing set.

Background: MIC is the lowest concentration of an antibiotic required to inhibit the growth of an organism. Methods: MIC was done against six conventional antibiotics e.g., Amoxicillin, Ceftriaxone, Gentamycin, Tetracycline, Ciprofloxacin, and Levofloxacin. E. coli O157:H7 was collected from hospital. The antibacterial activity of six conventional antibiotics was assessed against E. coli O157:H7by using the broth microdilution method then the fractional inhibitory concentration (FIC) index was used to define the interactions between antibiotics. Results:The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of antibiotics against E. coli O157:H7 showed that the highest concentration appeared in ciprofloxacin was 62.5 µg/ml. while ceftriaxone and gentamycin were 15.62 µg/ml. amoxicillin and levofloxacin was 7.812 µg/ml. the lowest concentration appeared in Tetracycline was 0.976 µg/ml. The higher FICI was seen in Ceftriaxone + Levofloxacin combination 0.3 % followed by Levofloxacin + Ciprofloxacin (0.1). Conclusions: The antibacterial activity of both Ceftriaxone and Levofloxacin was enhanced by the combination which proved a highly synergistic effect against E. coli O157:H7.

Objectives: This study aimed to investigate the associations between various biomarkers and the specific mutation of mitochondrial DNA in type 2 diabetes mellitus with ischemic heart diseases and compared with T2DM patients without ischemic heart disease. Methods: The study select two groups of patients admitted to Kerbala Heart Center and Al-Hassan Center for Endocrinology and Diabetes, Al-Hussein Teaching Hospital, Al-Hussein Medical City, Kerbala Health Directorates / Kerbala – Iraq between Nov., 2020 and Aug., 2021. The first group includes 50 patients of type 2 diabetes mellitus with ischemic heart disease (28 male and 22 female) with age ranged between 45-76 years, and the second group includes another 50 patients with type 2 diabetes mellitus without ischemic heart disease (24 male and 26 female) with age ranged between 49-82 years. Fasting serum glucose, insulin and insulin resistance have been determined and then correlated with nutation mitDNA investigated in sera of T2DM with/without ischemic heart diseases. Results: The amplification of the MTLL1 gene gives one genotypes as indicated by (422 bp) bands for those with homozygous wild type (AA), homozygous mutant (GG) genotypes and two genotypes bands (422 bp) for those with heterozygous (GA). The obtained data revealed that a strong positive correlation between Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and insulin (r = 0.926) with significant differences (P<0.05) was obtained in the sera of type 2 diabetic patient with ischemic heart disease as compared with that obtained in type 2 diabetic patients without ischemic heart diseases. Conclusion: The prevalence of association between HOMA-IR with MTTL1 G3243A mutation (GG allele) in type 2 diabetic patients with ischemic heart disease was only 8.0% and may be associated with maternally inherited of type 2 diabetes mellitus with ischemic heart disease as a pathogenic mutation in Iraqi population.