Congenital heart diseases (CHDs) are structural or functional defects present at birth due to improper heart development. Current therapeutic approaches to treating severe CHDs are primarily palliative surgical interventions during the peri- or prenatal stages, when the heart has fully developed from faulty embryogenesis. However, earlier interventions during embryonic development have the potential for better outcomes, as demonstrated by fetal cardiac interventions performed in utero, which have shown improved neonatal and prenatal survival rates, as well as reduced lifelong morbidity. Extensive research on heart development has identified key steps, cellular players, and the intricate network of signaling pathways and transcription factors governing cardiogenesis. Additionally, some reports have indicated that certain adverse genetic and environmental conditions leading to heart malformations and embryonic death may be amendable through the activation of alternative mechanisms. This review first highlights key molecular and cellular processes involved in heart development. Subsequently, it explores the potential for future therapeutic strategies, targeting early embryonic stages, to prevent CHDs, through the delivery of biomolecules or exosomes to compensate for faulty cardiogenic mechanisms. Implementing such non-surgical interventions during early gestation may offer a prophylactic approach toward reducing the occurrence and severity of CHDs.
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http://dx.doi.org/10.3390/jpm13081263 | DOI Listing |
Purpose: Heart failure (HF) is a disease that leads to approximately 300,000 fatalities annually in Europe and 250,000 deaths each year in the United States. Type 2 Diabetes Mellitus (T2DM) is a significant risk factor for HF, and testing for N-terminal (NT)-pro hormone BNP (NT-proBNP) can aid in early detection of HF in T2DM patients. We therefore developed and validated the HFriskT2DM-HScore, an algorithm to predict the risk of HF in T2DM patients, so guiding NT-proBNP investigation in a primary care setting.
View Article and Find Full Text PDFClin Epigenetics
January 2025
Department of Ultrasound, The People's Hospital of China Medical University, The People's Hospital of Liaoning Province, 33 Wenyi Road, Shenhe District, Shenyang, 110067, People's Republic of China.
As an important element of the human body, iron participates in numerous physiological and biochemical reactions. In the past decade, ferroptosis (a form of iron-dependent regulated cell death) has been reported to contribute to the pathogenesis and progression of various diseases. The stability of iron in cardiomyocytes is crucial for the maintenance of normal physiological cardiac activity.
View Article and Find Full Text PDFBMC Geriatr
January 2025
Department of Cardiology, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Lanzhou, 730000, China.
Objective: Constructing a predictive model for the occurrence of heart disease in elderly hypertensive individuals, aiming to provide early risk identification.
Methods: A total of 934 participants aged 60 and above from the China Health and Retirement Longitudinal Study with a 7-year follow-up (2011-2018) were included. Machine learning methods (logistic regression, XGBoost, DNN) were employed to build a model predicting heart disease risk in hypertensive patients.
BMC Med Res Methodol
January 2025
Department of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
Time-to-event data are very common in medical applications. Regression models have been developed on such data especially in the field of survival analysis. Kernels are used to handle even more complicated and enormous quantities of medical data by injecting non-linearity into linear models.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
The 12-lead electrocardiogram (ECG) is inexpensive and widely available. Whether conditions across the human disease landscape can be detected using the ECG is unclear. We developed a deep learning denoising autoencoder and systematically evaluated associations between ECG encodings and ~1,600 Phecode-based diseases in three datasets separate from model development, and meta-analyzed the results.
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