Objective: Aim: To determine the influence of maternal and neonatal variants of the eNOS (G894T, rs1799983) and IL1B (C3953T, rs1143634) genes and their intergenic interactions on the development of HIE in newborns.
Patients And Methods: Materials and Methods: The study included a cohort of 105 newborns and their 99 mothers. Determination of variants of the genes eNOS (G894T, rs1799983) and IL1B (C3953T, rs1143634) was carried out for the patients of study groups.
Results: Results: The frequency of detection of the 894TT genotype by the eNOS gene was increased in newborns with severe asphyxia (p=0.018) and in their mothers (p=0,0057). Further analysis of intergenic interactions, performed in mother-child pairs, revealed an increased frequency of the neonatal 894GG (eNOS)/maternal 3953С (IL-1B) genotype combination in the comparison group versus the group of newborns with HIE (p=0.007).
Conclusion: Conclusions: The significance of the intergenic maternal combination of 894GG/3953CT genotypes for the eNOS and IL1B genes and the intergenic combination of neonatal 894GG (eNOS)/maternal 3953CT (IL-1B) genotypes in the development of HIE in newborns has been proven. Associations of maternal and neonatal 894TT genotypes for the eNOS gene with the development of severe asphyxia, bradycardia, and respiratory failure were found in newborns with HIE.
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http://dx.doi.org/10.36740/WLek/197108 | DOI Listing |
BMC Nurs
January 2025
Faculty of Applied Medical Sciences in Alnamas, University of Bisha, 255, Al Nakhil, Al-Namas, 67714, Saudi Arabia.
Background: One of the best ways to impart important skills to trainees is through simulation-based training, which is more reliable than the conventional clinical examination method. It is used in pediatric nursing training to allow nurses to practice and improve their clinical and conversational skills during an actual child encounter. A heel-prick is a complex psychomotor task that requires skill and knowledge from the pediatric nurse performing the procedure while applying for the National Newborn Screening Program.
View Article and Find Full Text PDFBMC Pediatr
January 2025
School of Nursing and Health Sciences, The College of New Jersey, Ewing Township, USA.
Background: Preterm infants may experience many health and developmental issues, which continue even after discharge from the neonatal intensive care unit. Once home, the mother, as a non-professional and the primary caregiver will be responsible for the essential care of her preterm infant.
Purpose: Understanding the take care ability in mothers with preterm infants.
BMC Pregnancy Childbirth
January 2025
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of Utah Health, 30 N. Mario Capecchi Dr., Level 5 South, Salt Lake City, UT, 84132, USA.
Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.
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BMC Public Health
January 2025
Department of Women & Children's Health, King's College London, London, UK.
Background: Recurrent early pregnancy loss [rEPL] is a traumatic experience, marked by feelings such as grief and depression, and often anxiety. Despite this, the psychological consequences of rEPL are often overlooked, particularly when considering future reproductive health or approaching subsequent pregnancies. The SARS-CoV-2 pandemic led to significant reconfiguration of maternity care and a negative impact on the perinatal experience, but the specific impact on women's experience of rEPL has yet to be explored.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Statistics and Data Science, Jahangirnagar University, Dhaka, 1342, Bangladesh.
Background: Child mortality is a reliable and significant indicator of a nation's health. Although the child mortality rate in Bangladesh is declining over time, it still needs to drop even more in order to meet the Sustainable Development Goals (SDGs). Machine Learning models are one of the best tools for making more accurate and efficient forecasts and gaining in-depth knowledge.
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