American scientists have created a bioelectronic device to control the growth of human cells

American scientists have created a bioelectronic device to control the growth of human cells
A group of scientists from the University of California, Santa Cruz (UCSC) has developed a promising bioelectronic device. With the help of electronics and machine learning feedback, scientists were able to set and hold a specific membrane voltage in human stem cells for hours. This invention will allow you to control the growth and specialization of stem cells, which leads to progress in regenerative medicine.

A living human cell is a stable self — regulating system, and it cannot be otherwise. And she's on her own mind, even if she's sick. Therefore, changing the homeostasis of the cell is a difficult task that scientists were able to solve. This was helped by electronics controlled by machine learning algorithms, which maintained the balance of ions set by scientists in the immediate vicinity of cultured human stem cells.

Let us explain that the membrane voltage is formed as a potential difference between the internal environment of a living cell and its immediate environment. This potential difference — quite strictly defined for different types of cells — is maintained by proteins in the cell membrane. To do this, proteins create ion channels in the membrane, which leads to the restoration of balance (voltage) when the concentration of ions inside or outside the cell is disturbed. An attempt to change the concentration of ions (and the membrane voltage) causes a reverse reaction of the cell and is nullified. In any case, it will not be possible to keep the exact voltage of the cell membrane for a long time in a simple way.

Scientists solved the problem as follows. They created a system of proton pumps around the stem cell colony that added or removed hydrogen ions from the solution in the immediate vicinity of the cultured stem cells. This system was controlled by a self-learning ML algorithm. Moreover, the system did not undergo preliminary training on models, but learned on the go as it observed the behavior of cells and assessed the concentration of the solution. The system monitored the membrane voltage visually, for which the scientists modified the membrane protein so that it fluoresced depending on the value of the membrane voltage. Thus, the algorithm received a feedback system and could evaluate its effect on the membrane potential.
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