Objective: The study aimed at comparing the effectiveness and maternal satisfaction of oral misoprostol with vaginal misoprostol for induction of labor at term.
Materials And Methods: A randomized controlled trial of 140 term pregnant women at the University of Nigeria Teaching Hospital Enugu, Nigeria, was conducted from April 2011 to May 2012. The women were equally randomized into two groups (A and B) to receive oral and vaginal misoprostol, respectively.
Results: The vaginal route reduced the mean induction-vaginal delivery interval by four-and-half hours (20.7 ± 12.1 vs. 16.2 ± 10.4; mean difference: 4.50, 95% CI 0.63-0.82; p = 0.02). Furthermore, the mean dose of misoprostol required to achieve induction of labor and the mean duration of oxytocin augmentation when indicated were significantly less in the vaginal group than in the oral group (2.5 ± 1.3 vs. 2.0 ± 1.1; mean difference: 0.50, 95% CI 0.10-0.90; p = 0.02 and 4.6 ± 3.2 vs. 3.4 ± 3.1; mean difference: 1.20, 95% CI 0.15-0.23; p = 0.03 respectively). However, neonatal complications and maternal satisfaction were similar between the two groups.
Conclusion: Both routes of administration are effective in the induction of labor at term and have comparable maternal satisfaction. However, the vaginal route has the added advantage of shorter induction-delivery interval among others, and thus should be highly considered when induction of labor is indicated at term.
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http://dx.doi.org/10.1007/s00404-014-3429-8 | DOI Listing |
Unlabelled: The neurodegenerative disorder Frontotemporal Dementia (FTD) can be caused by a repeat expansion (GGGGCC; G4C2) in C9orf72. The function of wild-type C9orf72 and the mechanism by which the C9orf72-G4C2 mutation causes FTD, however, remain unresolved. Diverse disease models including human brain samples and differentiated neurons from patient-derived induced pluripotent stem cells (iPSCs) identified some hallmarks associated with FTD, but these models have limitations, including biopsies capturing only a static snapshot of dynamic processes and differentiated neurons being labor-intensive, costly, and post-mitotic.
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Department of Diabetes, Metabolism and Endocrinology, Japan Labor Health and Safety Organization, Tokyo Rosai Hospital, Tokyo, JPN.
Severe hypoglycemia (SH) is a significant risk, particularly in the elderly, and adrenal insufficiency (AI) may be a contributing factor. This study examines six cases of late dumping syndrome (LDS)-induced reactive hypoglycemia (RH), with AI as a potential trigger. Three of the six patients were diagnosed with AI, and one experienced a hypoglycemic coma.
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Obstetrics, Orlando Regional Medical Center, Orlando, USA.
Amniotic fluid embolism (AFE) is a rare condition that can have catastrophic maternal and infant consequences. It can lead to rapid multisystem failure and is responsible for a significant portion of maternal deaths. The diagnosis is frequently made late in the pathological process, and the treatment is mainly supportive and infant delivery.
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Department of Obstetrics and Gynaecology, Erlangen University Hospital, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
Introduction: Preconception obesity is a risk factor for pregnancy and delivery, which is why giving birth in a perinatal center (care levels I and II) is recommended. There are currently no studies which have investigated the birth outcomes of obese patients based on the care level of the maternity hospital. This study aims to assess the effect of a higher body mass index prior to conception on maternal and fetal outcomes in a maternity hospital (care level IV).
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Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China.
Identifying phage-host interactions (PHIs) is a crucial step in developing phage therapy, which is the promising solution to addressing the issue of antibiotic resistance in superbugs. However, the lifestyle of phages, which strongly depends on their host for life activities, limits their cultivability, making the study of predicting PHIs time-consuming and labor-intensive for traditional wet lab experiments. Although many deep learning (DL) approaches have been applied to PHIs prediction, most DL methods are predominantly based on sequence information, failing to comprehensively model the intricate relationships within PHIs.
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