Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases

Wang, Rui-Sheng; Maron, Bradley A.; Loscalzo, Joseph

doi:10.1161/atvbaha.122.318731

Cited by 24 publications

(10 citation statements)

References 87 publications

(100 reference statements)

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“…Other domains of cardiac phenotyping are continuously evolving [ 57 ]. A number of omics technologies are available that can contribute to the biological characterization of these patients and various computational methods are being investigated for the utilization of large amounts of data to identify pathways of disease [ 58 – 60 ]. Techniques such as MR spectroscopy may further contribute to our understanding of ongoing energy deficits in these patients [ 61 ].…”

Section: How Can Disease Etiology and Underlying Disease Activity Inf...mentioning

confidence: 99%

Your Heart Function Has Normalized—What Next After TRED-HF?

Kasiakogias,

Ragavan,

Halliday

2023

Curr Heart Fail Rep

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Purpose of Review With the widespread implementation of contemporary disease-modifying heart failure therapy, the rates of normalization of ejection fraction are continuously increasing. The TRED-HF trial confirmed that heart failure remission rather than complete recovery is typical in patients with dilated cardiomyopathy who respond to therapy. The present review outlines key points related to the management and knowledge gaps of this growing patient group, focusing on patients with non-ischaemic dilated cardiomyopathy. Recent Findings There is substantial heterogeneity among patients with normalized ejection fraction. The specific etiology is likely to affect the outcome, although a multiple-hit phenotype is frequent and may not be identified without comprehensive characterization. A monogenic or polygenic genetic susceptibility is common. Ongoing pathophysiological processes may be unraveled with advanced cardiac imaging, biomarkers, multi-omics, and machine learning technologies. There are limited studies that have investigated the withdrawal of specific heart failure therapies in these patients. Diuretics may be safely withdrawn if there is no evidence of congestion, while continued therapy with at least some disease-modifying therapy is likely to be required to reduce myocardial workload and sustain remission for the vast majority. Summary Understanding the underlying disease mechanisms of patients with normalized ejection fraction is crucial in identifying markers of myocardial relapse and guiding individualized therapy in the future. Ongoing clinical trials should inform personalized approaches to therapy.

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Section: How Can Disease Etiology and Underlying Disease Activity Inf...mentioning

confidence: 99%

Your Heart Function Has Normalized—What Next After TRED-HF?

Kasiakogias,

Ragavan,

Halliday

2023

Curr Heart Fail Rep

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“…3 Human induced pluripotent stem cells (hiPSCs) have an intrinsic capability for indefinite self-renewal and differentiation into various types of cells, including cardiomyocytes. 4 HiPSC-derived cardiomyocytes (hiPSC-CMs), free from ethical concerns associated with embryonic stem cells, possess the structural, regulatory, and functional attributes of human cardiomyocytes, making them valuable in precision medicine, 5,6 drug screening, 3,7 and cardiac tissue engineering. 8,9 To meet the growing demand for these cells for various applications, automatic hiPSC culture and differentiation systems have been developed and made commercially available.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Assessment of hiPSC Differentiation toward Cardiomyocytes Using Pretrained Convolutional Neural Networks and the Channel Pruning Algorithm

Duan,

He,

Lan

et al. 2024

ACS Biomater. Sci. Eng.

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Human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (hiPSC-CMs) offer versatile applications in tissue engineering and drug screening. To facilitate the monitoring of hiPSC cardiac differentiation, a noninvasive approach using convolutional neural networks (CNNs) was explored. HiPSCs were differentiated into cardiomyocytes and analyzed using the quantitative real-time polymerase chain reaction (qRT-PCR). The bright-field images of the cells at different time points were captured to create the dataset. Six pretrained models (AlexNet, GoogleNet, ResNet 18, ResNet 50, DenseNet 121, VGG 19-BN) were employed to identify different stages in differentiation. VGG 19-BN outperformed the other five CNNs and exhibited remarkable performance with 99.2% accuracy, recall, precision, and F1 score and 99.8% specificity. The pruning process was then applied to the optimal model, resulting in a significant reduction of model parameters while maintaining high accuracy. Finally, an automation application using the pruned VGG 19-BN model was developed, facilitating users in assessing the cell status during the myocardial differentiation of hiPSCs.

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“…As the single-cell sequencing paradigm matures, we're seeing the convergence of multiple data modalities, offering a holistic view of cellular states [3,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Effective single-cell multi-omics data integration remains challenging, particularly when integrating unmatched data.…”

Section: Introductionmentioning

confidence: 99%

scCross: A Deep Generative Model for Unifying Single-cell Multi-omics with Seamless Integration, Cross-modal Generation, and In-silico Exploration

Yang,

Mann,

et al. 2023

Preprint

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Single-cell multi-omics illuminate intricate cellular states, yielding transformative insights into cellular dynamics and disease. Yet, while the potential of this technology is vast, the integration of its multifaceted data presents challenges. Some modalities have not reached the robustness or clarity of established scRNA-seq. Coupled with data scarcity for newer modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross: a tool adeptly engineered using variational autoencoder, generative adversarial network principles, and the Mutual Nearest Neighbors (MNN) technique for modality alignment. This synergy ensures seamless integration of varied single-cell multi-omics data. Beyond its foundational prowess in multi-omics data integration, scCross excels in single-cell cross-modal data generation, multi-omics data simulation, and profound in-silico cellular perturbations. Armed with these capabilities, scCross is set to transform the field of single-cell research, establishing itself in the nuanced integration, generation, and simulation of complex multi-omics data.

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Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases

Cited by 24 publications

References 87 publications

Your Heart Function Has Normalized—What Next After TRED-HF?

Your Heart Function Has Normalized—What Next After TRED-HF?

Noninvasive Assessment of hiPSC Differentiation toward Cardiomyocytes Using Pretrained Convolutional Neural Networks and the Channel Pruning Algorithm

scCross: A Deep Generative Model for Unifying Single-cell Multi-omics with Seamless Integration, Cross-modal Generation, and In-silico Exploration

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