Objectives: To explore the correlation between maternal and cord blood prolactin, the correlation between cord prolactin and birth weight, and to compare cord blood prolactin in new-borns of women with normal pregnancy and women with pregnancy complications namely; gestational hypertension, gestational diabetes and preterm labour.Methods: This study was performed from September to December 2018. Thirty-two women, delivered at Baghdad teaching hospital, and their newborns (32) were included. Maternal blood (5 ml) was taken before labour and cord blood (5 ml) was collected after placenta expulsion. Maternal and cord blood prolactin were analysed using fluorescence immunoassay. Results: Cord blood prolactin was higher in babies born to hypertensive women (405.28±77.52 ng/ml) than normal pregnancy women (244.80±60.80 ng/ml), P=0.000. Also, cord prolactin in gestational hypertension group was significantly higher than diabetic (P=0.006) and preterm labour (P=0.000) groups. No significant difference was noticed in cord blood prolactin in newborns of diabetic and normal pregnancy women (299.28±37.01, 244.80±60.80 ng/ml respectively, P=0.053). Preterm babies had lower cord prolactin (204.57±22.90 ng/ml) than normal pregnancy babies (244.80±60.80 ng/ml), however the difference was non-significant, P=0.118. Positive correlation was found between cord and maternal prolactin (P=0.000) and between cord prolactin and birth weight (P=0.018). Conclusion: Cord blood prolactin is high in newborns of hypertensive women, low in preterm neonates. Diabetes has no effect on cord prolactin level. doi: https://doi.org/10.12669/pjms.35.4.558 How to cite this:Alawad ZM, Al-Omary HL. Maternal and cord blood prolactin level and pregnancy complications. Pak J Med Sci. 2019;35(4):1122-1127. doi: https://doi.org/10.12669/pjms.35.4.558 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Identification of pathogens and locating their inocul¬um source (S) are the first strategies toward successful disease management program the pretransplating seedl¬ing damping - off problem on vegetable crops was found to be caused by Pythium aphanidermatum and Rhizocto¬nia solani. Both fungi were isolated from peat (moss) for the first time in Iraq. In addition, considerable num¬ber of pathogenic fungi was found as contaminants in soil samples from Alrashidiah vegetable covered farming station. Among the isolated fungi were: Pythium apha¬nidermatum, Rhizoctonia solani, Sclerotinia sclerotiorum, Fusarium oxysporum, Fusarium solani phialophora spp., Cephalisporium spp Rizopus stolonfier and Botrytis cine¬rea, in addition to several
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Flavonoids were isolated by preparative layer chromatography and aromatic acid was isolated by preparative high-
... Show MoreIt is important that real time stability in smart grids is ensured as the integration of renewables and the complexity of the systems grows. In this paper, we provide a solid architecture, which combines a Residual CNNLSTM deep neural network predictor, FPGA-accelerated Model Predictive Control (MPC), and SHAP-based explainability. The proposed method predicted with 99.8% accuracy using the Electrical grid Stability Simulated Dataset (UCI) and minimized the instability rates surpassing 85 percent in all operating conditions. Meeting real-time operating needs, FPGA deployment on a Xilinx Zynq UltraScale+ provided 3.1 ms latency and 5 times reduced energy consumption against CPU processing. By emphasizing bus voltage and frequency as major in
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... Show MoreThis study was conducted in College of Science \ Computer Science Department \ University of Baghdad to compare between automatic sorting and manual sorting, which is more efficient and accurate, as well as the use of artificial intelligence in automated sorting, which included artificial neural network, image processing, study of external characteristics, defects and impurities and physical characteristics; grading and sorting speed, and fruits weigh. the results shown value of impurities and defects. the highest value of the regression is 0.40 and the error-approximation algorithm has recorded the value 06-1 and weight fruits fruit recorded the highest value and was 138.20 g, Gradin