AIMC Topic: Placenta
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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).
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 model based on placental magnetic resonance imaging and clinical factors to predict fetal growth restriction.
BMC pregnancy and childbirth
Mar 20, 2025
OBJECTIVES: To create a placental radiomics-clinical machine learning model to predict FGR.
PBScreen: A server for the high-throughput screening of placental barrier-permeable contaminants based on multifusion deep learning.
Environmental pollution (Barking, Essex : 1987)
Feb 14, 2025
Contaminants capable of crossing the placental barrier (PB) adversely affect female reproduction and fetal development. The rapid identification of PB-permeable contaminants is urgently needed due to the inefficiencies of conventional cell-based tran...
Identification of critical biomarkers and immune infiltration in preeclampsia through bioinformatics and machine learning methods.
BMC pregnancy and childbirth
Feb 11, 2025
BACKGROUND: Preeclampsia (PE) is a multisystem progressive disease that occurs during pregnancy. Previous studies have shown that the immune system is involved in the placental trophoblast function and the pathological process of uterine vascular rem...
Machine learning-enhanced surface-enhanced spectroscopic detection of polycyclic aromatic hydrocarbons in the human placenta.
Proceedings of the National Academy of Sciences of the United States of America
Feb 10, 2025
The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential for environmental and health monitoring, for assessing toxicological exposure and their associated ...
Association between deep learning radiomics based on placental MRI and preeclampsia with fetal growth restriction: A multicenter study.
European journal of radiology
Feb 5, 2025
PURPOSE: Preeclampsia (PE) is associated with placental insufficiency and could lead to adverse pregnancy outcomes. The study aimed to develop a placental T2-weighted image-based automatic quantitative model for the identification of PE pregnancies a...
NMF typing and machine learning algorithm-based exploration of preeclampsia-related mechanisms on ferroptosis signature genes.
Cell biology and toxicology
Dec 21, 2024
BACKGROUND: Globally, pre-eclampsia (PE) poses a major threat to the health and survival of pregnant women and fetuses, contributing significantly to morbidity and mortality. Recent studies suggest a pathological link between PE and ferroptosis. We a...
Optimizing lipid nanoparticles for fetal gene delivery in vitro, ex vivo, and aided with machine learning.
Journal of controlled release : official journal of the Controlled Release Society
Oct 28, 2024
There is a clinical need to develop lipid nanoparticles (LNPs) to deliver congenital therapies to the fetus during pregnancy. The aim of these therapies is to restore normal fetal development and prevent irreversible conditions after birth. As a firs...
Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restriction.
Frontiers in immunology
Sep 4, 2024
BACKGROUND: Fetal growth restriction (FGR) occurs in 10% of pregnancies worldwide. Placenta dysfunction, as one of the most common causes of FGR, is associated with various poor perinatal outcomes. The main objectives of this study were to screen pot...
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