Lanqing Qu^1,2,#, Jinbiao Zhang^1,2,#, Wei Jiang^1,2, Jiayu Zhang^1,2, Die Li¹, Wei Cheng³, Linghua Tao⁴, Hongdan Zhu⁵, Jing Li⁶, Min Xue⁷, Feng Chen⁸, Cuicui Xu⁹, Qiang Shu^1,2,*, Weize Xu^1,2,*

Congenital Heart Disease, Vol.20, No.5, pp. 571-580, 2025, DOI:10.32604/chd.2025.066258 - 30 November 2025

Abstract Background: Congenital heart disease (CHD) is the most common congenital anomaly, but whether the COVID-19 pandemic affects its prevalence is unknown. We aimed to compare the incidence of CHD during the COVID-19 pandemic with that before the pandemic in China. Methods: This multicenter retrospective observational study involved all newborns in seven representative cities of China between 01 September 2019, and 31 December 2021. All the newborns underwent pulse oximetry monitoring combined with cardiac murmur auscultation in the first 6 h to 72 h after birth for CHD screening. We defined fetuses born in and beyond September… More >

Sha Chen^1,#, Minkai Cao^1,#, Kerong Liu^2,*, Ying Gu^1,*

BIOCELL, Vol.49, No.2, pp. 181-198, 2025, DOI:10.32604/biocell.2025.058745 - 28 February 2025

Abstract The increasing prevalence of gestational diabetes mellitus (GDM) is associated with an array of pregnancy complications and enduring health challenges in both mothers and their offspring. Studies have indicated that exposure to the intrauterine environment can prompt adaptations in the offspring, thereby programming transgenerational inheritance. Physical activity during pregnancy, as a non-pharmacological intervention, mitigates metabolic risks through epigenetic modifications, mediating placental adaptations, the action of exercise factors, and gut microbiota. Here, we provide a review summarizing how regular exercise can reduce the risk of GDM and positively influence pregnancy outcomes. It also discusses the exercise-induced More >

Ali Hasan Dakheel^1,*, Mohammed Raheem Mohammed¹, Zainab Ali Abd Alhuseen¹, Wassan Adnan Hashim^2,3

Intelligent Automation & Soft Computing, Vol.40, pp. 195-220, 2025, DOI:10.32604/iasc.2025.061622 - 28 February 2025

Abstract The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health. This study aims to enhance risk prediction in pregnancy with a novel deep learning model based on a Long Short-Term Memory (LSTM) generator, designed to capture temporal relationships in cardiotocography (CTG) data. This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction, normalization, and segmentation to create high-quality input for the model. It uses convolutional layers to extract spatial information, followed by LSTM layers to model sequences for superior predictive performance. The overall More >

Almagul Kurmanova^1,2, Gulfairuz Urazbayeva³, Laura Kayupova², Damilya Salimbaeva², Nurzhamal Dzhardemalieva⁴

European Cytokine Network, Vol.35, No.2, pp. 21-27, 2024, DOI:10.1684/ecn.2024.0497

Abstract The aim of the present study was to evaluate the diagnostic significance of the dynamics of cytokines and growth factors during pregnancy with and without preeclampsia. The study included 168 pregnant women at risk of hypertensive disorders. The levels of biomarkers of all pregnant women were studied at 12-16 weeks, 28-30 weeks and 36-38 weeks. These included cytokines (tumour necrosis factor-α, interferon-γ, interleukin-4) and growth factors (placental growth factor, vascular endothelial growth factor). All pregnant women were divided into two groups: 124 patients with preeclampsia and 44 without preeclampsia (control group). In patients with preeclampsia,… More >

Yuchen Ye^1,3, Dadi Wu², Jiahu Hao^1,2,*

International Journal of Mental Health Promotion, Vol.26, No.12, pp. 1009-1016, 2024, DOI:10.32604/ijmhp.2024.056235 - 31 December 2024

Abstract Objective: In recent years, psychological problems in pregnant women have become an important public health problem. Depression is a common psychological problem during pregnancy. At present, most studies focus on prenatal depression in pregnant women, and there is a lack of relevant studies on prenatal negative cognition and its relationship with depression. This study aims to examine the relationship between depression and negative cognitive bias in women in late pregnancy and identify the influencing factors. Methods: A total of 829 women in late pregnancy were recruited from a tertiary hospital between April 2023 and October… More >

Yamixa Delgado^1,*, Caliani Gaytan¹, Naydi Perez², Eric Miranda³, Bryan Colón Morales¹, Mónica Santos¹

Congenital Heart Disease, Vol.19, No.1, pp. 19-31, 2024, DOI:10.32604/chd.2024.046339 - 20 March 2024

Abstract Background: Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States, there is a compelling need to investigate the intricate interplay among body mass index (BMI), pregestational, and gestational maternal diabetes, and their potential impact on the occurrence of congenital heart defects (CHD) during neonatal development. Methods: Using the comprehensive System of Vigilance and Surveillance of Congenital Defects in Puerto Rico, we conducted a focused analysis on neonates diagnosed with CHD between 2016 and 2020. Our assessment encompassed a range of variables, including maternal age, gestational age, BMI,… More >

Yanli Liu^1,2, Fengzhen Han², Jian Zhuang⁴, Yanqiu Ou⁴, Yanji Qu⁵, Yanyan Lin², Weina Zhang², Haiping Wang^3,*, Liping Huang^1,*

Congenital Heart Disease, Vol.18, No.5, pp. 561-570, 2023, DOI:10.32604/chd.2023.030511 - 10 November 2023

Abstract Background: Research on fetal congenital heart defect (CHD) mostly focuses on etiology and mechanisms. However, studies on maternal complications or pathophysiology are limited. Our objective was to determine whether vascular dysfunction exists in pregnant women carrying a fetus with congenital heart defects. Methods: We conducted a case-control study. 27 cases of pregnant women carrying a fetus with major CHD admitted to our hospital for delivery between April 2021 and August 2022 were selected. Every case was matched with about 2 pregnant complication-free controls without fetal abnormalities. The proangiogenic and anti-angiogenic factors and pregnancy outcomes were… More > Graphic Abstract

Daniel Sweeney¹, Scott Cohen^2,3, Salil Ginde^2,3, Jennifer Gerardin^2,3, Peter Bartz^2,3, Matthew Buelow^2,3,*

Congenital Heart Disease, Vol.18, No.3, pp. 315-323, 2023, DOI:10.32604/chd.2023.027349 - 09 June 2023

Abstract Background: Women with congenital heart disease (CHD) have increased risk for adverse events during pregnancy and delivery. Prior studies have assessed pregnancy and delivery outcomes at tertiary referral centers (TRC). The aim of our study was to assess pregnancy outcomes in women with CHD who deliver in a non-tertiary referral center (non-TRC). Methods: Clinical demographics were collected, including anatomic complexity, physiologic state and pre-pregnancy risk assessment. Patients were stratified by delivery location, either TRC or non-TRC. Maternal and neonatal complications of pregnancy were reported. Results: Women with CHD who delivered in a TRC had a higher… More > Graphic Abstract

C. Vairavel^1,*, N. S. Nithya²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2159-2175, 2023, DOI:10.32604/csse.2023.036598 - 09 February 2023

Abstract The immune system goes through a profound transformation during pregnancy, and certain unexpected maternal complications have been correlated to this transition. The ability to correctly examine, diagnoses, and predict pregnancy-hastened diseases via the available big data is a delicate problem since the range of information continuously increases and is scalable. Many approaches for disease diagnosis/classification have been established with the use of data mining concepts. However, such methods do not provide an appropriate classification/diagnosis model. Furthermore, single learning approaches are used to create the bulk of these systems. Classification issues may be made more accurate… More >

Sumayh S. Aljameel¹, Malak Aljabri^1,2, Nida Aslam¹, Dorieh M. Alomari^3,*, Arwa Alyahya¹, Shaykhah Alfaris¹, Maha Balharith¹, Hiessa Abahussain¹, Dana Boujlea¹, Eman S. Alsulmi⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1291-1304, 2023, DOI:10.32604/cmc.2023.035710 - 06 February 2023

Abstract Currently, the risk factors of pregnancy loss are increasing and are considered a major challenge because they vary between cases. The early prediction of miscarriage can help pregnant ladies to take the needed care and avoid any danger. Therefore, an intelligent automated solution must be developed to predict the risk factors for pregnancy loss at an early stage to assist with accurate and effective diagnosis. Machine learning (ML)-based decision support systems are increasingly used in the healthcare sector and have achieved notable performance and objectiveness in disease prediction and prognosis. Thus, we developed a model… More >