Objective: The aim of this study was to measure interleukin-6 (IL-6) levels in maternal serum of women undergoing preterm labor without a clear infection.
Materials And Methods: Twenty two pregnant women with diagnosis of preterm labor who presented to the outpatient clinic of 19 Mayis University Faculty of Medicine from July 2011 through December 2011 were enrolled in the study group. Twenty two healthy pregnant women who were at the same gestational age as the study group were selected as the control group.
Results: Gestational age in the study and control groups varied from 24 weeks and 4 days to 34 weeks and 6 days. In the study group, 11 patients (50%) underwent preterm birth. Pregnant women in preterm labor were compared to healthy pregnant women with regards to serum IL-6 levels. No significant difference was found in the IL-6 levels of maternal serum between the 2 groups.
Conclusion: In this study we have shown that there is no increase in lL-6 levels in patients undergoing preterm labor without clinical or biochemical infection signs.
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http://dx.doi.org/10.17772/gp/1591 | DOI Listing |
Br J Hosp Med (Lond)
January 2025
Department of Obstetrics and Gynecology, The First Clinical Medical College of Three Gorges University, Yichang Central People's Hospital, Yichang, Hubei, China.
Gestational diabetes mellitus (GDM) is a common complication during pregnancy. This retrospective study investigates the correlation between umbilical blood flow index and maternal-fetal outcomes in pregnant women with GDM, aiming to contribute to evidence-based risk assessment and management strategy in this high-risk obstetric population. This retrospective study recruited 119 pregnant women with GDM who were admitted to the Yichang Central People's Hospital, between January 2022 and January 2024.
View Article and Find Full Text PDFBiomedicines
January 2025
Third Department of Obstetrics and Gynecology, University General Hospital "ATTIKON", Medical School, National and Kapodistrian University of Athens, 124 62 Athens, Greece.
Background/objectives: Preterm labor is a leading cause of neonatal morbidity and mortality worldwide. Previous research has indicated that an inflammatory response or microbial invasion of the amniotic cavity is a pathological condition linked to preterm birth; hence, inflammatory markers such as metalloproteinase-9 (MMP-9), interleukin-6 (IL-6), and interleukin-8 (IL-8) have been utilized to predict preterm delivery. The identification of reliable biomarkers for early prediction is critical for improving maternal, fetal, and neonatal outcomes.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Emergency, Hainan Clinical Research Center for Acute and Critical Diseases, The Second Affiliated Hospital of Hainan Medical University, Haikou, Hainan, 570100, China.
Background: Due to climate change, the frequency and intensity of heat waves and other extreme weather events are rapidly increasing. Compared to the general population, pregnant women and fetuses are increasingly vulnerable to the effects of extreme temperatures and are associated with the occurrence of adverse birth outcomes, including preterm birth (PTB). However, its risk of preterm birth is currently uncertain.
View Article and Find Full Text PDFLancet Child Adolesc Health
February 2025
The Royal Women's Hospital, Melbourne, VIC, Australia; Department of Obstetrics, Gynaecology and Newborn Health, The University of Melbourne, Melbourne, VIC, Australia; Murdoch Children's Research Institute, Melbourne, VIC, Australia.
In this Viewpoint, we discuss the challenges facing perinatal clinical researchers, many of which are unique to this field, and how traditional two-arm randomised trials using frequentist analysis might no longer be fit for purpose for perinatology. We propose a solution: the adoption of adaptive platform trials (APTs) with Bayesian methodology to address perinatal research questions to improve outcomes of preterm birth. APTs use a master protocol as a foundation to efficiently assess multiple interventions simultaneously for a particular disease.
View Article and Find Full Text PDFAm J Obstet Gynecol MFM
January 2025
The Josef Buchmann Gynecology and Maternity Center, Sheba Medical Center, Tel Hashomer, Israel; ARC Innovation Center, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel; The Dina Recanati School of Medicine, Reichmann University, Herzliya, Israel.
Objective: Machine learning (ML), a subtype of artificial intelligence (AI), presents predictive modeling and dynamic diagnostic tools to facilitate early interventions and improve decision-making. Considering the global challenges of maternal, fetal, and neonatal morbidity and mortality, ML holds the potential to enable significant improvements in maternal and neonatal health outcomes. We aimed to conduct a comprehensive review of ML applications in peripartum care, summarizing the potential of these tools to enhance clinical decision-making and identifying emerging trends and research gaps.
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