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  • Open Access

    MINI REVIEW

    Role of Adipose-Derived Stem Cells Therapy in Heart Failure

    Gabriel Matheus Da Silva Batista, Lucas Pina Rodrigues, Andrey Jorge Serra*

    BIOCELL, Vol.49, No.10, pp. 1873-1885, 2025, DOI:10.32604/biocell.2025.067186 - 22 October 2025

    Abstract Adipose-derived stem cells (ADSCs) therapy has emerged as a promising strategy for treating degenerative, inflammatory, and cardiometabolic diseases. In addition to their higher bioavailability in adipose tissue, ADSCs demonstrate superior activity in producing specific immune modulators and growth factors when compared to other stem cell types. The detrimental impact of heart failure (HF)—a condition that still lacks a fully effective therapy—has driven significant interest in the therapeutic potential of ADSCs. This interest is supported by robust evidence from experimental studies employing HF animal models. Accordingly, this review aims to explore the cardioprotective mechanisms through which More >

  • Open Access

    ARTICLE

    QHF-CS: Quantum-Enhanced Heart Failure Prediction Using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data

    Prasanna Kottapalle1,*, Tan Kuan Tak2, Pravin Ramdas Kshirsagar3, Gopichand Ginnela4, Vijaya Krishna Akula5

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3857-3892, 2025, DOI:10.32604/cmc.2025.065287 - 03 July 2025

    Abstract Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide, exacerbated by the COVID-19 pandemic. Age, cholesterol, and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators. These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult, and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles. Modern medical datasets’ complexity and high dimensionality challenge traditional prediction models like Support Vector Machines and Decision Trees. Quantum approaches include QSVM, QkNN, QDT, and others.… More >

  • Open Access

    REVIEW

    Sodium-Glucose Cotransporter 2 Inhibitors in Adult and Pediatric Congenital Heart Disease: Review of Emerging Data and Future Directions

    William H. Marshall V1,2,*, Lydia K. Wright2

    Congenital Heart Disease, Vol.19, No.4, pp. 419-433, 2024, DOI:10.32604/chd.2024.056608 - 31 October 2024

    Abstract Heart failure (HF) is common in patients with congenital heart disease (CHD) and there are limited medical therapies. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are a proven medical therapy in patients with acquired HF, though data are limited in patients with CHD. The aim of this review is to summarize the current evidence for use of SGLT2i in patients with CHD and identify future directions for study. In available publications, SGLT2i in patients with CHD seem to be well tolerated, with similar side effect profile to patients with acquired HF. Improvement in functional capacity and natriuretic More >

  • Open Access

    ARTICLE

    Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

    Polin Rahman1, Ahmed Rifat1, MD. IftehadAmjad Chy1, Mohammad Monirujjaman Khan1,*, Mehedi Masud2, Sultan Aljahdali2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 757-775, 2023, DOI:10.32604/csse.2023.021469 - 01 June 2022

    Abstract Heart failure is now widely spread throughout the world. Heart disease affects approximately 48% of the population. It is too expensive and also difficult to cure the disease. This research paper represents machine learning models to predict heart failure. The fundamental concept is to compare the correctness of various Machine Learning (ML) algorithms and boost algorithms to improve models’ accuracy for prediction. Some supervised algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), Logistic Regression (LR) are considered to achieve the best results. Some boosting algorithms like Extreme Gradient… More >

  • Open Access

    ARTICLE

    Heart Failure Patient Survival Analysis with Multi Kernel Support Vector Machine

    R. Sujatha1, Jyotir Moy Chatterjee2, NZ Jhanjhi3, Thamer A. Tabbakh4, Zahrah A. Almusaylim5,*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 115-129, 2022, DOI:10.32604/iasc.2022.019133 - 26 October 2021

    Abstract Heart failure (HF) is an intercontinental pandemic influencing in any event 26 million individuals globally and is expanding in commonness. HF healthiness consumptions are extensive and will increment significantly with a maturing populace. As per the World Health Organization (WHO), Cardiovascular diseases (CVDs) are the major reason for all-inclusive death, taking an expected 17.9 million lives per year. CVDs are a class of issues of the heart, blood vessels and include coronary heart sickness, cerebrovascular illness, rheumatic heart malady, and various other conditions. In the medical care industry, a lot of information is as often… More >

  • Open Access

    ARTICLE

    Multi-Institutional US Experience of the Occlutech© AFR Device in Congenital and Acquired Heart Disease

    Barry O’Callaghan1, Jenny Zablah1, Joseph Vettukattil2, Daniel Levi3,4, Morris Salem4, Allison Cabalka5, Jason Anderson6, Makram Ebeid6, Ryan Alexy7, Gareth J. Morgan1,*

    Congenital Heart Disease, Vol.17, No.1, pp. 107-116, 2022, DOI:10.32604/CHD.2022.018590 - 26 October 2021

    Abstract Objectives: To detail the US multi-institutional experience with the Occlutech© (Occlutech International AB, Helsingborg, Sweden) atrial flow regulator (AFR) in children and adults with acquired or congenital heart disease. Background: The creation of a long-term atrial communication is desirable in several cardiovascular disease phenotypes, most notably pulmonary arterial hypertension, disorders of increased left ventricular filling and increased cavopulmonary pressures in patients with a Fontan type circulation. Methods: Patients were identified for inclusion from the AFR device manufacturer database. Data was collected using a RedCap database following IRB approval. 8 weeks of follow up data was sought for… More >

  • Open Access

    ARTICLE

    Hypertension and Heart Failure as Predictors of Mortality in an Adult Congenital Heart Defect Population

    Cheryl Raskind-Hood1,*, Kashaine A. Gray2,3, Jayne Morgan3, Wendy M. Book4,*

    Congenital Heart Disease, Vol.16, No.4, pp. 333-355, 2021, DOI:10.32604/CHD.2021.014384 - 19 April 2021

    Abstract Early intervention to prevent premature mortality is vital for adults with congenital heart defects (CHD). Anatomic complexity and comorbid conditions are thought to contribute to CHD mortality. Since hypertension (HTN) and heart failure (HF) are the comorbid conditions among the most prevalent causes of death in the United States, and commonly accompany CHD, it is crucial to evaluate whether they are reliable predictors of mortality for adults with CHD (ACHD) independent of anatomic CHD complexity. A retrospective cross-sectional analysis of ACHD, aged 18–64, with concomitant HTN and/or HF and at least one health care encounter… More >

  • Open Access

    ARTICLE

    A Novel Method of Heart Failure Prediction Based on DPCNNXGBOOST Model

    Yuwen Chen1, 2, 3, *, Xiaolin Qin1, 3, Lige Zhang1, 3, Bin Yi4

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 495-510, 2020, DOI:10.32604/cmc.2020.011278 - 23 July 2020

    Abstract The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s More >

  • Open Access

    ARTICLE

    Pulmonary Hemodynamics and Outcome in a Large Cohort of Patients with Sinus Venosus Septal Defect

    Liesbeth van Dessel1, Béatrice Santens2, Els Troost2, Pieter De Meester2,3, Leen Roggen2, Filip Rega3,4, Bart Meyns3,4, Bjorn Cools5, Marc Gewillig5, Philip Moons6,7,8, Lukas Meier9, Werner Budts2,3, Alexander Van De Bruaene2,3,*

    Congenital Heart Disease, Vol.15, No.2, pp. 69-78, 2020, DOI:10.32604/CHD.2020.011512 - 23 June 2020

    Abstract Background: Left-to-right shunt in sinus venosus septal defect (SVSD) may affect resistive (pulmonary vascular resistance–PVR) and elastic (pulmonary artery compliance-PAC) pulmonary artery properties. This study aimed at evaluating (1) impact of age, (2) pulmonary hemodynamics, and (3) outcome in a large cohort of SVSD patients. Methods: This study included 136 patients with SVSD (median age at diagnosis 14 (IQR 5–48) years, 47% male) of which 87 underwent catheterization. Pressures were measured and cardiac output was evaluated using the Fick principle at diagnosis. PVR, PAC and their product (RC time) were calculated. Results: Surgical repair was performed in… More >

  • Open Access

    ARTICLE

    Opportunities for training to advance the care for adults with congenital heart disease with advanced circulatory failure

    Christopher R. Broda

    Congenital Heart Disease, Vol.14, No.3, pp. 487-490, 2019, DOI:10.1111/chd.12748

    Abstract Heart failure is an emerging issue with important implications in adult patients with congenital heart disease. Practitioners with expertise in both adult congenital heart disease and heart failure are needed to manage this growing and often complex pop‐ ulation. In the United States, the optimal training pathway to enable practitioners to best care for these patients is ill‐defined. This article explores possibilities and issues that interested trainees may encounter during their training experience. More >

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