Background: Bowel preparation prior to
colonic surgery usually includes antibiotic
therapy together with mechanical bowel
preparation which may cause discomfort to the
patients, prolonged hospitalization and water
& electrolyte imbalance.
Objective: to assess whether elective colon
and rectal surgery may be safely performed
without preoperative mechanical bowel
preparation.
Method: the study includes all patients who
had elective large bowel resection at Medical
City – Baghdad Teaching Hospital between
Feb, 2007 to Jan, 2010. Emergency operations
were not included. The patients were randomly
assigned to the 2 study groups (with or without
mechanical bowel preparation.
Results: A total of 165 patients participated
in the study, 82 with mechanical bowel
preparation and 83 without. The 2 groups
were similar in age, sex and type of surgical
procedure. 134 patients (81.2 %) underwent
surgery owing to colorectal cancer & 31
patients (18.8 %) owing to benign disease.
The hospitalization period was longer in the
bowel-prepared group (mean ± SD, 8.2 ± 5.1
days) as compared with the non prepared
group (mean ± SD, 8.0 ± 2.7 days). However,
this difference was not statistically significant.
The time until the 1st bowel movement was
similar between the 2 groups : a mean ± SD of
4.2 ± 1.3 days in the non prepared group as
compared with a men ± SD 4.3 ± 1.1 days in
the prepared group ( P = NS ).
Conclusion: Our results suggest that no
advantage is gained by preoperative
mechanical bowel preparation in elective
colorectal surgery.
Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le
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The first chapter contained (the methodological framework), which included the research problem with regard to the directing treatments between the directors in establishing or creating theatrical venue, the importance of the research and the aim of the research, and the limits of the research to conclude the chapter by defining the terms.
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Enhancing quality image fusion was proposed using new algorithms in auto-focus image fusion. The first algorithm is based on determining the standard deviation to combine two images. The second algorithm concentrates on the contrast at edge points and correlation method as the criteria parameter for the resulted image quality. This algorithm considers three blocks with different sizes at the homogenous region and moves it 10 pixels within the same homogenous region. These blocks examine the statistical properties of the block and decide automatically the next step. The resulted combined image is better in the contras
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The exploitation of all available resources and benefiting from them is one of the most important problems facing the decision makers at the present time. In order to exploit these resources, it is necessary to organize the conflicting objectives, which is the main work in the project management, which enables the development of a plan that decision makers can use to shorten the total completion time and reduce the total cost of the project. Through the use of modern scientific techniques, and therefore the researcher using the critical path method using the technology of programming goals to find more efficient ways to make appropriate decisions where the researcher worked to solve the problems in the construction of the Departm
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