Machine learning-based classification of cardiac relaxation impairment using sarcomere length and intracellular calcium transients.
Machine learning-based classification of cardiac relaxation impairment using sarcomere length and intracellular calcium transients.
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DOI：
10.1016/j.compbiomed.2023.107134
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发表时间：
2023-09
影响因子：
7.7
通讯作者：
中科院分区：
文献类型：
--
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心肌细胞松弛受损导致左心室舒张功能障碍。松弛速度部分地由细胞内钙（Ca 2+）循环调节，并且在肌节期间较慢的Ca 2+外流转化为肌节的松弛速度降低。肌节长度瞬时和细胞内钙动力学是表征心肌松弛行为的组成部分。然而，一个分类工具，可以分离正常细胞与细胞松弛受损肌小节长度瞬态和/或钙动力学仍有待开发。在这项工作中，我们采用了九种不同的分类器来分类正常和受损的细胞，使用肌节运动学和细胞内钙动力学数据的离体测量。从野生型小鼠（称为正常）和表达受损左心室舒张的转基因小鼠（称为受损）中分离细胞。我们利用来自正常和受损心肌细胞的总共n=126个细胞（n=60个正常细胞和（n=60个受损细胞））的肌节长度瞬态数据和总共n=116个细胞（n=57个正常细胞和n=59个受损细胞）的细胞内钙循环测量作为机器学习（ML）模型的输入进行分类。我们分别使用这两组输入特征使用交叉验证方法训练所有ML分类器，并比较它们的性能指标。分类器对测试数据的性能表明，我们的软投票分类器优于所有其他个人分类器的两组输入功能，与0.94和0.95面积下的肌节长度瞬变和钙瞬变的接收器工作特性曲线，分别，而多层感知器分别达到0.93和0.95，分别可比的分数。然而，发现决策树和极端梯度提升的性能取决于用于训练的输入特征集。我们的研究结果强调了选择适当的输入特征和分类器对正常和受损细胞进行准确分类的重要性。逐层相关传播（LRP）分析表明，肌节收缩50%的时间具有肌节长度瞬时的最高相关分数，而钙衰减50%的时间具有钙瞬时输入特征的最高相关分数。尽管数据集有限，但我们的研究表现出令人满意的准确性，这表明该算法可用于分类心肌细胞的松弛行为时，细胞的潜在松弛损伤是未知的。
Impaired relaxation of cardiomyocytes leads to diastolic dysfunction in the left ventricle. Relaxation velocity is regulated in part by intracellular calcium (Ca2+) cycling, and slower outflux of Ca2+ during diastole translates to reduced relaxation velocity of sarcomeres. Sarcomere length transient and intracellular calcium kinetics are integral parts of characterizing the relaxation behavior of the myocardium. However, a classifier tool that can separate normal cells from cells with impaired relaxation using sarcomere length transient and/or calcium kinetics remains to be developed. In this work, we employed nine different classifiers to classify normal and impaired cells, using ex-vivo measurements of sarcomere kinematics and intracellular calcium kinetics data. The cells were isolated from wild-type mice (referred to as normal) and transgenic mice expressing impaired left ventricular relaxation (referred to as impaired). We utilized sarcomere length transient data with a total of n=126 cells (n=60 normal cells and (n=60 impaired cells) and intracellular calcium cycling measurements with a total of n=116 cells (n=57 normal cells and n=59 impaired cells) from normal and impaired cardiomyocytes as inputs to machine learning (ML) models for classification. We trained all ML classifiers with cross-validation method separately using both sets of input features, and compared their performance metrics. The performance of classifiers on test data showed that our soft voting classifier outperformed all other individual classifiers on both sets of input features, with 0.94 and 0.95 area under the receiver operating characteristic curves for sarcomere length transient and calcium transient, respectively, while multilayer perceptron achieved comparable scores of 0.93 and 0.95, respectively. However, the performance of decision tree, and extreme gradient boosting was found to be dependent on the set of input features used for training. Our findings highlight the importance of selecting appropriate input features and classifiers for the accurate classification of normal and impaired cells. Layer-wise relevance propagation (LRP) analysis demonstrated that the time to 50% contraction of the sarcomere had the highest relevance score for sarcomere length transient, whereas time to 50% decay of calcium had the highest relevance score for calcium transient input features. Despite the limited dataset, our study demonstrated satisfactory accuracy, suggesting that the algorithm can be used to classify relaxation behavior in cardiomyocytes when the potential relaxation impairment of the cells is unknown.
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影响因子：
4.6
作者：
Hwang H;Liu R;Maxwell JT;Yang J;Xu C
通讯作者：
Xu C
DOI：
10.1016/j.ecl.2022.02.005
发表时间：
2022-09-01
影响因子：
4.5
作者：
Goldsborough III, Earl;Osuji, Ngozi;Blaha, Michael J.
通讯作者：
Blaha, Michael J.
影响因子：
4.8
作者：
Bidwell, Philip A.;Haghighi, Kobra;Kranias, Evangelia G.
通讯作者：
Kranias, Evangelia G.
影响因子：
4.6
作者：
Juhola M;Joutsijoki H;Penttinen K;Aalto-Setälä K
通讯作者：
Aalto-Setälä K
影响因子：
3.8
作者：
BACKX, PH;GAO, WD;MARBAN, E
通讯作者：
MARBAN, E