Objective: To evaluate the clinical significance of open diagnostic testicular biopsy as prognostic predictor of
successful sperm retrieval among azoospermic infertile patients.
Design: Prospective study.
Setting: Infertility clinic and assisted reproduction unit at the institute of embryo research and infertility
treatment, Baghdad University.
Patients: Sixty infertile azoospermic patients.
Intervention: Pieces of testicular tissue taking during open diagnostic multiple bilateral testicular biopsies was
prepared for histological examination and grouped according to well-defined histopathological patterns.
Measurement of testicular size and serum reproductive hormones (FSH, LH, Testosterone, and PRL) were done
for all these sixty azoospermic patients.
Main Outcome Measures: Sperm found with a new histopatholigical categorization and sub categorization.
Results: Our study showed no significant difference between mean testicular size and mean serum reproductive
hormonal (FSH, LH, T and PRL) concentrations of MAFS compared to CMA and that of SCOFS compared to
SCO. The sperm found with open diagnostic bilateral biopsy was positive in transverse section of seminiferous
tubules of NS, HS, MAFS, and SCOFS, where as it was negative in CMA, CSCO, and TF.
Conclusions: It was concluded from the results of the work that the new histological categorization of open
testicular biopsies was found to be practical, informative, and most useful diagnostic and prognostic predictor to
select the patients for TESE-ICSI
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Insurance companies seeking to develop programs to promote and market their services and to increase its customer through the use of modern technical marketing and reduce its dependence on agents and take advantage of work of the banks by alliances with them and including reinforcing get the parties to competitive advantages in the financial market , the insurance services intangible service stops marketed over the insurance awareness and requires exceptional promotional efforts. &nbs
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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
... Show MoreArtificial 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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