March 3, 2026Open Access

Machine Learning Identifies Distinct Treg-Mediated Remodeling in HFpEF Hearts Treated with Neonatal Mesenchymal Stem Cells and Their Secretome

Key Points

Distinct remodeling mechanisms mediated by Treg play a crucial role in improving heart function.
The study highlights Treg-related signaling as a significant factor in myocardial recovery in HFpEF.
Using machine learning and VIPcell, causal regulators were identified that enhance the immune modulation effects.
Findings indicate potential pathways for developing targeted therapies aimed at Treg interventions in HFpEF.

Abstract

nMSC and SEC therapies improve diastolic function in HFpEF through distinct remodeling mechanisms converging on T reg -mediated immune modulation. VIPcell supported identification of causal regulators, highlighting T reg -related signaling as a key driver of myocardial recovery in HFpEF. These findings offer mechanistic insight into cellular therapies for HFpEF and support the development of targeted, T reg -focused interventions.

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Authors

Zhi-Dong Ge

Jonghyeuk Han

Felipe Takaesu

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Institutions

Northwestern University

Emory University

University of Maryland, Baltimore

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Machine Learning Identifies Distinct Treg-Mediated Remodeling in HFpEF Hearts Treated with Neonatal Mesenchymal Stem Cells and Their Secretome

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Actions

Institutions

References and Citations

Citation Network

Connected Papers

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