Background: Polycystic ovary syndrome is the most common cause of anovulation, and the number of antral follicles is of great importance in determining ovarian reserve, so identification of patients with diminished ovarian reserve help in choosing individualized and well managed ovulation induction protocol. The aim of the study is to find out if the number of ovarian antral follicles could affect the amount of gonadotropins used in ovarian stimulation in polycystic ovarian patients.
Patients and methods: Ninty four infertile polycystic ovaries women, attending the infertility clinic at Baghdad teaching hospital, during the period of November 2005 to October 2006, were compared to 62 control group women who have unexplained infertility. After exact history and examination, ultrasound was done to both groups at cycle day 3 for antral follicle counting. Then ovarian stimulation was started with gonadotropins, and another ultrasound was done on cycle day 13 for mature follicles confirmation.
Results: Antral follicle number was found to be significantly higher in patients than control groups (9.98 ± 2.09 vs 5.40±2.02). Age was found to be negatively correlated with antral follicle size and number in patient and control groups. After measuring the number of mature follicles at cycle day 13 it was observed that the antral follicle number was correlating positively and significantly with the number of follicles at cycle day 13, but negatively with the amount of gonadotropin used for ovarian stimulation in patients and control groups.
Conclusion: Antral follicles number is significantly higher in polycystic ovary patients and they correlate negatively with age. Antral follicle number is a good predictor of the number of gonadotropin ampouls used for ovarian stimulation.
This study offers numerical simulation results using the ABAQUS/CAE version 2019 finite element computer application to examine the performance, and residual strength of eight recycle aggregate RC one-way slabs. Six strengthened by NSM CFRP plates were presented to study the impact of several parameters on their structural behavior. The experimental results of four selected slabs under monotonic load, plus one slab under repeated load, were validated numerically. Then the numerical analysis was extended to different parameters investigation, such as the impact of added CFRP length on ultimate load capacity and load-deflection response and the impact of concrete compressive strength value on the structural performance of
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Shear and compressional wave velocities, coupled with other petrophysical data, are vital in determining the dynamic modules magnitude in geomechanical studies and hydrocarbon reservoir characterization. But, due to field practices and high running cost, shear wave velocity may not available in all wells. In this paper, a statistical multivariate regression method is presented to predict the shear wave velocity for Khasib formation - Amara oil fields located in South- East of Iraq using well log compressional wave velocity, neutron porosity and density. The accuracy of the proposed correlation have been compared to other correlations. The results show that, the presented model provides accurate
... Show MoreWe aimed to obtain magnesium/iron (Mg/Fe)-layered double hydroxides (LDHs) nanoparticles-immobilized on waste foundry sand-a byproduct of the metal casting industry. XRD and FT-IR tests were applied to characterize the prepared sorbent. The results revealed that a new peak reflected LDHs nanoparticles. In addition, SEM-EDS mapping confirmed that the coating process was appropriate. Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g. The pseudo-first-order and pseudo-second-order kinetic models helped to describe the sorption measure
The current issues in spam email detection systems are directly related to spam email classification's low accuracy and feature selection's high dimensionality. However, in machine learning (ML), feature selection (FS) as a global optimization strategy reduces data redundancy and produces a collection of precise and acceptable outcomes. A black hole algorithm-based FS algorithm is suggested in this paper for reducing the dimensionality of features and improving the accuracy of spam email classification. Each star's features are represented in binary form, with the features being transformed to binary using a sigmoid function. The proposed Binary Black Hole Algorithm (BBH) searches the feature space for the best feature subsets,
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