Using stem cells and smart machines to warn of heart problems

Despite advances in treatments in recent years heart disease remains the leading cause of death in the US. It accounts for one in three deaths in this country, and many people are not even aware they have a problem until they have a heart attack.

One of the early warning signs of danger is a heart arrhythmia; that’s when electrical signals that control the hearts beating don’t work properly and can result in the heart beating too fast, too slow, or irregularly. However, predicting who is at risk of these arrhythmias is difficult. Now new research may have found a way to change that.

A research team at the Institute of Molecular and Cell Biology in Singapore combined stem cells with machine learning, and developed a way to predict arrhythmias, with a high degree of accuracy.

The team used stem cells to create different batches of cardiomyocytes or heart muscle cells. Some of these batches were healthy heart cells, but some had arrhythmias caused by different problems such as a genetic disorder or drug induced.

They then trained a machine learning program to use videos to scan the 3,000 different groups of cells. By studying the different beating patterns of the cells, and then using the levels of calcium in the cells, the machine was able to predict, with 90 percent accuracy, which cells were most likely to experience arrhythmias.

The researchers say their approach is faster, simpler and more accurate than current methods of trying to predict who is at risk for arrhythmias and could have a big impact on our ability to intervene before the individual suffers a fatal heart attack.

The research was published in the journal Stem Cell Reports.

The California Institute for Regenerative Medicine has invested more than $180 million in more than 80 different projects, including four clinical trials, targeting heart disease.

One thought on “Using stem cells and smart machines to warn of heart problems

  1. Stem cells require growth factors for growth and differentiation. These cells require many stages of maturation to develop into mature and functioning cells. Each stage of maturation, progenitor cells response to specific growth factor for activation of gene expression and changes to be more mature state . Therefore, maturation is the last phase of heart development that prepares the organ for strong, efficient and persistent pumping throughout the mammals lifespan. This process is characterized by structural, gene expression, metabolic and functioning specialization in cardiomyocytes as the heart transits from fetal to adult states. The mature cardiomyocytes undergo changes that permit the cells to sustain billions of cycles of forceful contraction and relaxation. Although, current pluripotent stem cells (PSCs) technology allows for efficient differentiation of human PSCs into cardiomyocytes, these PSCs-cardiomyocytes exhibit immature phenotypes that resembles fetal cardiomyocutes. Despite tremendous progress in promoting PSCs-derived cardiomyocytes (PSC-CM) maturation by tissue engineering -based methods. Complete maturation of PSC-CM has yet to be archived. The maturation bottleneck severely impairs the use of PSC-CMs in vitro modeling for pathological, pharmacological or therapeutic purpose. Electrophysiological maturation defects of PSC-CM also produce arrhythmogenis risk from cell replacement therapy.

    To note, PSC technology produces stem cells in the form of organoid. However, the expansion of organoid is limited by ability of the structural size to deliver the nutrients, oxygen and transportation of waste. The inequlvalent delivery and transportation of food, oxygen and waste may cause process of maturation producing more defective progenitor cells. PSC-CMs with errors in gene expression and defective development provide no difference of indication between healthy and arrhythmia subjects. Hence, the early culture of PSC-CMs with small number of genes expression does not support ‘the state of functioning heart’ and results of clinical finding do not translate into clinically benefits to diagnose patient with arrhythmia.

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