In a recent study published in the journal Cell Stem Cell, researchers hypothesized that pacemaker-like activity of human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) resulted in engraftment arrhythmias (EAs), which hampers the clinical use of cell-based therapy using hPSC-CMs for treatment of myocardial infarction (MI).
Study: Gene editing to prevent ventricular arrhythmias associated with cardiomyocyte cell therapy. Image Credit: FrentaN / Shutterstock.
Scientists are still determining whether humans will reach a maximum possible age or if we can extend lifespan indefinitely. One thing we know is that the aging we see and feel in our bodies is connected to aging that individual cells experience. Yeast is a common model in molecular biology that is often used to study aging. In 2020, scientists found that yeast cells could go down one of two aging paths; in one, structures called nucleoli were degraded and ribosomal DNA experienced less silencing; in the other, mitochondria were affected and heme accumulation was reduced. The researchers suggested that these were two distinct types of terminal aging.
In follow-up work, the research team has manipulated the genetics of those pathways, and have extended the lifespan of cells by doing so. The work has been reported in Science. The investigators applied a solution to the cells that altered gene circuits to stop the cells from deteriorating.
To get an inside look at the heart, cardiologists often use electrocardiograms (ECGs) to trace its electrical activity and magnetic resonance images (MRIs) to map its structure. Because the two types of data reveal different details about the heart, physicians typically study them separately to diagnose heart conditions.
Now, in a paper published in Nature Communications, scientists in the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard have developed a machine learning approach that can learn patterns from ECGs and MRIs simultaneously, and based on those patterns, predict characteristics of a patient’s heart. Such a tool, with further development, could one day help doctors better detect and diagnose heart conditions from routine tests such as ECGs.
The researchers also showed that they could analyze ECG recordings, which are easy and cheap to acquire, and generate MRI movies of the same heart, which are much more expensive to capture. And their method could even be used to find new genetic markers of heart disease that existing approaches that look at individual data modalities might miss.
Researchers have taught an AI to make artificial genomes — possibly overcoming the problem of how to protect people’s genetic information while also amassing enough DNA for research.
Generative adversarial networks (GANs) pit two neural networks against each other to produce new, synthetic data that is so good it can pass for real data. Examples have been popping up all over the web — generating pictures and videos (a la “this city does not exist”). AIs can even generate convincing news articles, food blogs, or human faces (take a look here for a complete list of all the oddities created by GANs).
Now, researchers from Estonia are going more in-depth with deepfakes of human DNA. They created an algorithm that repeatedly generates the genetic code of people that don’t exist.
While the world has been captivated by recent advances in artificial intelligence, researchers at Johns Hopkins University have identified a new form of intelligence: organoid intelligence. A future where computers are powered by lab-grown brain cells may be closer than we could ever have imagined.
What is an organoid? Organoids are three-dimensional tissue cultures commonly derived from human pluripotent stem cells. What looks like a clump of cells can be engineered to function like a human organ, mirroring its key structural and biological characteristics. Under the right laboratory conditions, genetic instructions from donated stem cells allow organoids to self-organize and grow into any type of organ tissue, including the human brain.
Although this may sound like science-fiction, brain organoids have been used to model and study neurodegenerative diseases for nearly a decade. Emerging studies now reveal that these lab grown brain cells may be capable of learning. In fact, a research team from Melbourne recently reported that they trained 800,000 brain cells to perform the computer game, Pong (see video). As this field of research continues to grow, researchers speculate that this so-called “intelligence in a dish” may be able to outcompete artificial intelligence.
The potential of satellite-based synthetic biology and genetic engineering to revolutionize healthcare is becoming increasingly clear. Recent advances in the field have opened up a world of possibilities for medical professionals and researchers, allowing them to diagnose and treat diseases more effectively and efficiently than ever before.
Satellite-based synthetic biology and genetic engineering have already been used to develop treatments for a variety of conditions, including cancer, heart disease, and neurological disorders. By using satellite-based techniques, researchers can quickly and accurately identify genetic mutations and other abnormalities in a patient’s DNA. This allows them to develop personalized treatments that are tailored to the individual’s specific needs.
The use of satellite-based synthetic biology and genetic engineering also has the potential to reduce healthcare costs. By identifying genetic mutations and other abnormalities at an early stage, doctors can avoid costly and unnecessary treatments. This could lead to significant savings for both patients and healthcare providers.
An experiment by disgraced scientist He Jiankui resulted in the birth of the first babies with edited genes. What happened next?
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