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.
The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet
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