Passage Of California Stem Cell Proposition Boosts Research

For the last decade and more, Stem Cell research and regenerative medicine have been the rave of the healthcare industry, a delicate area that has seen steady advancements over the last few years.

The promise of regenerative medicine is simple but profound that one day medical experts will be able to diagnose a problem, remove some of our body cells called stem cells and use them to grow a cure for our ailment. Using our body cells will create a highly personalized therapy attuned to our genes and systems.

The terminologies often used in this field of medicine can get a bit fuzzy for the uninitiated, so in this article, I have relied heavily on the insights of Christian Drapeau, a neurophysiologist and stem cell expert.

Drapeau was one of the first voices who discovered and began to speak about stem cells being the bodys repair system in the early 2000s. Since then, he has gone on to discover the first stem cell mobilizer, and his studies and research delivered the proof of concept that the AFA (Aphanizomenon flos-aquae) extract was capable of enhancing repair from muscle injury.

Christian Drapeau is also the founder of Kalyagen, astem cell research-based company, and the manufacturers of Stemregen. This stem cell mobilizer combines some of the most effective stem cell mobilizers Drapeau has discovered to create an effective treatment for varying diseases.

How exactly do stem cell-based treatments work? And how is it delivering on its promise of boosting our abilities to regenerate or self-heal?

Drapeau explains the concept for us;

Stem cells are mother cells or blank cells produced by the bone marrow. As they are released from the bone marrow stem cells can travel to any organ and tissue of the body, where they can transform into cells of that tissue.Stem cells constitute the repair system of the body.

The discovery of this function has led scientists on a long journey to discover how to use stem cells to cure diseases, which are essentially caused by cellular loss. Diseases like Diabetes and age-related degenerative diseases are all associated with the loss of a type of cell or cellular function.

However, what Drapeaus research has unearthed over the last few decades is that there are naturally occurring substances that show a demonstrated ability to induce the release of stem cells from the bone marrow. These stem cells then enter the bloodstream, from where they can travel to sites of cell deficiency or injury in the body to aid healing and regeneration. This process is referred to as Endogenous Stem Cell Mobilization (ESCM).

Stemregen is our most potent creation so far, explains Drapeau, and it has shown excellent results with the treatment of problems in the endocrine system, muscles, kidneys, respiratory systems, and even with issues of erectile dysfunction.

Despite the stunning advancements that have been made so far, a concern that both Drapeau and I share is how this innovation can be merged with another exciting innovation; AI.

Is it even a possibility? Drapeau, an AI enthusiast, explains that AI has already been a life-saver in stem cell research and has even more potential.

On closer observation, there are a few areas in which AI has greatly benefited stem cell research and regenerative medicine.

One obstacle that scientists have consistently faced with delivering the full promise of regenerative medicine is the complexity of the available data.Cells are so different from each other that scientists can struggle with predicting what the cells will do in any given therapeutic scenario. Scientists are faced with millions of ways that medical therapy could go wrong.

Most AI experts believe that in almost any field, AI can provide a solution whenever there is a problem with data analysis and predictive analysis.

Carl Simon, a biologist at the National Institute of Standards and Technology (NIST) and Nicholas Schaub recentlytested this hypothesiswhen they applied Deep Neural Networks (DNN), an AI program to the data they had collected in their experiments on eye cells. Their research revolved around causes and solutions for age-related eye degeneration. The results were stunning; the AI made only one incorrect prediction about cell changes out of 36 predictions it was asked to make.

Their program learned how to predict cell function in different scenarios and settings from annotated images of cells. It soon could rapidly analyze images of the lab-grown eye tissues to classify the tissues as good or bad. This discovery has raised optimism in the stem cell research space.

Drapeau explains why this is so exciting;

When we talk about stem cells in general, we say stem cells as if they were all one thing, but there are many different types of stem cells.For example, hair follicle and dental pulp stem cells contain neuronal markers and can easily transform into neurons to repair the brain. Furthermore, the tissue undergoing repair must signal to attract stem cells and must secrete compounds to stimulate stem cell function. A complex analysis of the tissue that needs repair and the conditions of that tissue using AI, in any specific individual, will help select the right type of stem cells and the best cells in that stem cell population, along with the accompanying treatment to optimize stem cell-based tissue repair.

Christian Drapeau

Ina study published in Februaryof this year inStem Cells, researchers from Tokyo Medical and Dental University (TMDU) reported that their AI system, called DeepACT, had successfully identified healthy, productive skin stem cells with the same accuracy that a human could. This discovery further strengthens Drapeaus argument on the potentials of AI in this field.

This experiment owes its success to AIs machine learning capabilities, but it is expected that Deep Learning can be beneficially introduced into regenerative medicine.There are many futuristic projections for these possibilities, but many of them are not as far-fetched as they may first seem.

Researchers believe that AI can help fast-track the translation of regenerative medicine into clinical practice; the technology can be used to predict cell behavior in different environments. Therefore, hypothetically, it can be used to simulate the human environment. This means that researchers can gain in-depth information more rapidly.

Perhaps the most daring expectation is the possibility of using AI to pioneer the 3D printing of organs. In a world where organ shortage is a harsh reality, this would certainly come in handy. AI algorithms can be utilized to identify the best materials for artificial organs, understand the anatomic challenges during treatment, and design the organ.

Can stem cells actually be used along with other biological materials to grow functional 3D-printed organs? If this is possible, then pacemakers will soon give way to 3D-printed hearts. A 3D-printedheart valvehas already become a reality in India, making this even more of an imminent possibility.

While all of these possibilities excite Drapeau, he is confident that AIs capabilities with data analysis and prediction, which is already largely in use, would go down as its most beneficial contribution to stem cell research;

It was already shown that stem cells laid on the connective tissue of the heart, the soft skeleton of the heart, can lead the entire formation of a new heart. Stem cells have this enormous regenerative potential. AI can take this to another level by helping establish the conditions in which this type of regeneration can be orchestrated inside the body.But we have to be grateful for what we already have, over the last 20 years, I have studied endogenous stem cell mobilization and today the fact that we have such amazing results with Stemregen is testament that regenerative medicine is already a success.

As AI continues to scale over industry boundaries, we can only sit back and hope it delivers on its full potential promise. Who knows? Perhaps AI really can change the world.

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How The Overlap Between Artificial Intelligence And Stem Cell Research Is Producing Exciting Results - Forbes