ReviewFrontiers in cardiovascular medicine2021
Race and Genetics in Congenital Heart Disease: Application of iPSCs, Omics, and Machine Learning Technologies.
Review in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Maternal Myo-Inositol Intake and Congenital Heart Defects in Offspring: A Population-Based Case-Control Study.BJOG : an international journal of obstetrics and gynaecology · 2026Article
- Smarter stem cells: how AI is supercharging iPSC technology.Cell and tissue research · 2025Review
- Fetal cardiac function in pregnancy affected by congenital heart disease: protocol for a multicentre prospective cohort study.BMC pregnancy and childbirth · 2025Article
- Ambient Air Pollution and Congenital Heart Disease: Updated Evidence and Future Challenges.Antioxidants (Basel, Switzerland) · 2025Review
- Predictive Factors of Length of Stay in Intensive Care Unit after Coronary Artery Bypass Graft Surgery based on Machine Learning Methods.Archives of academic emergency medicine · 2025Article
- Comparative assessment of nutritional status in unoperated children with Congenital Heart Defects: Insights from a tertiary pediatric cardiac center in India.PLOS global public health · 2025Article
- Article
- Characterization of genomic variants associated with congenital heart disease in patients from southwestern Colombian.Heliyon · 2024Article
- Technologies to Study Genetics and Molecular Pathways.Advances in experimental medicine and biology · 2024Review
- Accurately assessing congenital heart disease using artificial intelligence.PeerJ. Computer science · 2024Article
- Deciphering Congenital Heart Disease Using Human Induced Pluripotent Stem Cells.Advances in experimental medicine and biology · 2024Article
- Disparities in cardio-oncology: Implication of angiogenesis, inflammation, and chemotherapy.Life sciences · 2023Article
- A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Insights and perspectives into clinical biomarker discovery in pediatric heart failure and congenital heart disease-a narrative review.Cardiovascular diagnosis and therapy · 2023Review
- Generation of three induced pluripotent stem cell lines to model and investigate diseases affecting Hispanics.Stem cell research · 2022Article
- Copy number variant analysis for syndromic congenital heart disease in the Chinese population.Human genomics · 2022Article
- Modeling Susceptibility to Cardiotoxicity in Cancer Therapy Using Human iPSC-Derived Cardiac Cells and Systems Biology.Heart failure clinics · 2022Review
- Utilization of induced pluripotent stem cells to model the molecular network regulating congenital heart disease.Cardiovascular research · 2022Article
- Associations betweenAmerican journal of translational research · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Congenital heart disease (CHD) is a multifaceted cardiovascular anomaly that occurs when there are structural abnormalities in the heart before birth. Although various risk factors are known to influence the development of this disease, a full comprehension of the etiology and treatment for different patient populations remains elusive. For instance, racial minorities are disproportionally affected by this disease and typically have worse prognosis, possibly due to environmental and genetic disparities. Although research into CHD has highlighted a wide range of causal factors, the reasons for these differences seen in different patient populations are not fully known. Cardiovascular disease modeling using induced pluripotent stem cells (iPSCs) is a novel approach for investigating possible genetic variants in CHD that may be race specific, making it a valuable tool to help solve the mystery of higher incidence and mortality rates among minorities. Herein, we first review the prevalence, risk factors, and genetics of CHD and then discuss the use of iPSCs, omics, and machine learning technologies to investigate the etiology of CHD and its connection to racial disparities. We also explore the translational potential of iPSC-based disease modeling combined with genome editing and high throughput drug screening platforms.
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