Nikhith Kalkunte, Jorge Cisneros, Edward Castillo, Janet Zoldan
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引用次数: 0
摘要
心脏组织工程(CTE)有望应对心血管疾病这一全球主要死亡原因所带来的临床挑战。人类诱导多能干细胞(hiPSC)是心脏再生治疗的关键,它提供了一种免疫兼容的高密度细胞来源。然而,hiPSC 衍生的心肌细胞(hiPSC-CMs)表现出重要的功能缺陷,这些缺陷尚未得到很好的了解,阻碍了其临床应用。我们认为,机器学习(ML)可以通过强大的数学模型和预测改善这些细胞的表型和功能,从而克服这些挑战。本综述论文探讨了 ML 在推动 CTE 方面的变革性作用,并介绍了相关的 ML 算法。我们重点关注 ML 最近是如何解决 CTE 中的六个关键难题的:细胞分化、形态、钙处理和细胞-细胞耦合、收缩和组织组装。论文介绍了常见的 ML 模型,从基于树的模型、概率模型到神经网络和深度学习模型,说明了它们在更好地理解 hiPSC-CM 行为方面的应用。我们承认整合 ML 所面临的挑战,如有限的生物医学数据集、学习数据的计算成本以及模型的可解释性和可靠性等,同时我们还研究了改进建议,强调需要更广泛、更多样的数据集,其中包括时间和成像数据,并通过合成生成模型加以扩充。通过将 ML 与数学模型和现有的专家知识相结合,我们预见到将创新的数据驱动模型与生物物理学信息模型结合起来的合作会取得丰硕成果,从而有效缩小 CTE 的差距。
A review on machine learning approaches in cardiac tissue engineering
Cardiac tissue engineering (CTE) holds promise in addressing the clinical challenges posed by cardiovascular disease, the leading global cause of mortality. Human induced pluripotent stem cells (hiPSCs) are pivotal for cardiac regeneration therapy, offering an immunocompatible, high density cell source. However, hiPSC-derived cardiomyocytes (hiPSC-CMs) exhibit vital functional deficiencies that are not yet well understood, hindering their clinical deployment. We argue that machine learning (ML) can overcome these challenges, by improving the phenotyping and functionality of these cells via robust mathematical models and predictions. This review paper explores the transformative role of ML in advancing CTE, presenting a primer on relevant ML algorithms. We focus on how ML has recently addressed six key address six key challenges in CTE: cell differentiation, morphology, calcium handling and cell-cell coupling, contraction, and tissue assembly. The paper surveys common ML models, from tree-based and probabilistic to neural networks and deep learning, illustrating their applications to better understand hiPSC-CM behavior. While acknowledging the challenges associated with integrating ML, such as limited biomedical datasets, computational costs of learning data, and model interpretability and reliability, we examine suggestions for improvement, emphasizing the necessity for more extensive and diverse datasets that incorporate temporal and imaging data, augmented by synthetic generative models. By integrating ML with mathematical models and existing expert knowledge, we foresee a fruitful collaboration that unites innovative data-driven models with biophysics-informed models, effectively closing the gaps within CTE.