AIMC Topic: Pregnancy
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Epigenome-wide association study identifies a specific panel of DNA methylation signatures for antenatal and postpartum depressive symptoms.
Journal of affective disorders
Apr 24, 2025
Depression during pregnancy and postpartum poses significant risks to both maternal and child well-being. The underlying biological mechanisms are unclear, but epigenetic variation could be exploited as a plausible candidate for early detection. We i...
Unveiling fetal heart health: harnessing auto-metric graph neural networks and Hazelnut tree search for ECG-based arrhythmia detection.
Computer methods in biomechanics and biomedical engineering
Apr 24, 2025
Fetal electrocardiogram (ECG) provides a non-invasive means to assess fetal heart health, but isolating the fetal signal from the dominant maternal ECG remains challenging. This study introduces the FHH-AMGNN-HTSOA-ECG-AD method for enhanced fetal ar...
Deep learning for fetal inflammatory response diagnosis in the umbilical cord.
Placenta
Apr 24, 2025
INTRODUCTION: Inflammation of the umbilical cord can be seen as a result of ascending intrauterine infection or other inflammatory stimuli. Acute fetal inflammatory response (FIR) is characterized by infiltration of the umbilical cord by fetal neutro...
Effect of pregnancy and infancy exposure to outdoor particulate matter (PM, PM, PM) and SO on childhood pneumonia in preschool children in Taiyuan City, China.
Environmental pollution (Barking, Essex : 1987)
Apr 24, 2025
There is currently a paucity of research on the effects of early life exposure to particulate matter (PM) of various size fractions on pneumonia in preschool-aged children. We explored the connections between antenatal and postnatal exposure to atmos...
Birth weight prediction using artificial intelligence-based placental assessment from macroscopic photo: a retrospective study.
Placenta
Apr 23, 2025
BACKGROUND: This study aimed to predict newborn birth weight through multifactorial analysis of macroscopic placental images using artificial intelligence (AI).
GDM-BC: Non-invasive body composition dataset for intelligent prediction of Gestational Diabetes Mellitus.
Computers in biology and medicine
Apr 23, 2025
Gestational Diabetes Mellitus (GDM) refers to any degree of impaired glucose tolerance with onset or first recognition during pregnancy. As a high-prevalence disease, GDM damages the health of both pregnant women and fetuses in the short and long ter...
Random forest algorithm for predicting tobacco use and identifying determinants among pregnant women in 26 sub-Saharan African countries: a 2024 analysis.
BMC public health
Apr 23, 2025
INTRODUCTION: Tobacco use during pregnancy is a significant public health concern, associated with adverse maternal and neonatal outcomes. Despite its critical importance, comprehensive data on tobacco use among pregnant women in sub-Saharan Africa i...
Dose-response association between OGTT and adverse perinatal outcomes after IVF treatment: A cohort study based on a twin population.
Journal of endocrinological investigation
Apr 19, 2025
BACKGROUND: Investigate the association between Oral Glucose Tolerance Test (OGTT) after in vitro fertilization (IVF) treatment and adverse maternal and neonatal outcomes in twin pregnancies.
ULK2 deficiency stratifies autophagy-driven molecular subtypes and exacerbates trophoblasts apoptosis in preeclampsia.
Placenta
Apr 18, 2025
INTRODUCTION: Preeclampsia (PE), a placenta-originated hypertensive disorder of pregnancy, lacks targeted therapies despite its significant contribution to maternal and fetal morbidity. Emerging evidence implicates autophagy dysregulation in PE patho...
A machine learning-based framework for predicting postpartum chronic pain: a retrospective study.
BMC medical informatics and decision making
Apr 17, 2025
BACKGROUND: Postpartum chronic pain is prevalent, affecting many women after delivery. Machine learning algorithms have been widely used in predicting postoperative conditions. We investigated the prevalence of and risk factors for postpartum chronic...
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