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.
This review examines how artificial intelligence (AI) including machine learning (ML), deep learning (DL), and the Internet of Things (IoT) is transforming operations across exploration, production, and refining in the Middle Eastern oil and gas sector. Using a systematic literature review approach, the study analyzes AI adoption in upstream, midstream, and downstream activities, with a focus on predictive maintenance, emission monitoring, and digital transformation. It identifies both opportunities and challenges in applying AI to achieve environmental and economic goals. Although adoption levels vary across the region, countries such as Saudi Arabia, the UAE, and Qatar are leading initiatives that align with global sustainability targets.
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