Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: genetics – Page 262

Machine learning model finds genetic factors for heart disease

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 that can learn patterns from ECGs and MRIs simultaneously, and based on those patterns, predict characteristics of a patient’s . 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.

“This DNA Is Not Real”: Why Scientists Are Deepfaking the Human Genome

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.

Forget AI; Organoid Intelligence May Soon Power Our Computers

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 Future of Satellite-Based Synthetic Biology and Genetic Engineering

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.

AnthropoAge: Biological Age Prediction Without Blood Testing

Join us on Patreon! https://www.patreon.com/MichaelLustgartenPhD

Discount Links:
NAD+ Quantification: https://www.jinfiniti.com/intracellular-nad-test/
Use Code: ConquerAging At Checkout.

Green Tea: https://www.ochaandco.com/?ref=conqueraging.

Oral Microbiome: https://www.bristlehealth.com/?ref=michaellustgarten.

Epigenetic Testing: https://trudiagnostic.com/?irclickid=U-s3Ii2r7xyIU-LSYLyQdQ6…M0&irgwc=1
Use Code: CONQUERAGING

At-Home Blood Testing: https://getquantify.io/mlustgarten.

Continue reading “AnthropoAge: Biological Age Prediction Without Blood Testing” | >

Information ‘deleted’ from the human genome may be what made us human

What the human genome is lacking compared with the genomes of other primates might have been as crucial to the development of humankind as what has been added during our evolutionary history, according to a new study led by researchers at Yale and the Broad Institute of MIT and Harvard.

The new findings, published April 28 in the journal Science, fill an important gap in what is known about historical changes to the human genome. While a revolution in the capacity to collect data from genomes of different species has allowed scientists to identify additions that are specific to the human —such as a gene that was critical for humans to develop the ability to speak—less attention has been paid to what’s missing in the .

For the new study researchers used an even deeper genomic dive into primate DNA to show that the loss of about 10,000 bits of genetic information—most as small as a few base pairs of DNA—over the course of our differentiate humans from chimpanzees, our closest primate relative. Some of those “deleted” pieces of genetic information are closely related to genes involved in neuronal and cognitive functions, including one associated with the formation of cells in the developing brain.

New method improves accuracy of DNA sequencing 1,000-fold to detect rare genetic mutations

A team of researchers at the Broad Institute of MIT and Harvard has developed a new approach to next-generation sequencing that detects genetic mutations within single molecules of DNA.

The method, called Concatenating Original Duplex for Error Correction (CODEC), makes next-generation sequencing about 1,000 times more accurate and opens up the possibility of a range of applications including detecting tiny numbers of cancer mutations in , monitoring cancer during and after treatment, and identifying mutations underlying rare diseases, all at relatively low cost. The study appears today in Nature Genetics.

“The beauty of this approach is that it’s not an overhaul of how sequencing is done,” said Viktor Adalsteinsson, senior author on the study and director of the Gerstner Center for Cancer Diagnostics and leader of the Blood Biopsy Team at the Broad. “It’s not something that requires new instrumentation or —it’s a simple set of steps added into existing sample preparation workflows to improve the accuracy of DNA sequencing.”

/* */