Remote Monitoring to Improve Human Health

New data-driven algorithms for intellingent remote patient monitoring systems from AIMLAb at the Technion Faculty of Biomedical Engineering.

With billions of mobile devices around the world and the low cost of embedded medical sensor, capturing and sharing medical data has never been simpler or quicker. It is now possible to obtain continuous and long-term complex physiological data. However, there have been little achievements in harnessing this “wealth” of physiological data to provide actionable clinical evidence.

Part of the problem stems from the wide variety of data content, the absence of data representation requirements (e.g., resolution, sampling, frequency, and metadata), and the use of comparatively limited datasets across certain trials, which struggle to capture the large range of variability between patients and time

Another problem is the lack of intelligent and accurate algorithms capable of decrypting the information stored in a vast number of data points obtained over time, referred as physiological time series. The advancement of machine learning algorithms in combination with current and novel wearable biosensors provides a once-in-a-life-time opportunity to enhance health screening and, as well as help patient management, especially through remote health monitoring.

Assistant Professor Joachim Behar’s Technion Artificial Intelligence in Medicine Laboratory (AIMLab) builds innovative pattern recognition algorithms to manipulate4 the information encoded within vast datasets of physiological time series. These new data-driven algorithms are being used by the AIMLab to develop innovative intelligent remote patient management systems. 

 

 

 

Stem Cell AI

“Brain on a chip” project aims to revolutionize computing power

Scientists will use human brain stem cells on microchips in the pioneering NEU-Chip project to bring artificial intelligence to new heights (AI).

Scientists have begun work on a project in which human brain stem cells can be used to fuel artificial intelligence (AI) devices, ushering in a computer revolution.

The Neu-Chip project, led by Aston University researchers, has been awarded €3.5 million to demonstrate how neurons, the brain’s information processors, can be used to supercharge computers’ abilities to learn while significantly reducing energy usage. 

The researchers are now embarking on a three-year study to show how human brain stem cells developed on a microchip can be taught to solve problems using data, paving the groundwork for a “paradigm change” in machine learning technology. 

AI is increasingly being used in a variety of fields, including healthcare, finance, autonomous cars, and voice recognition, as well as recommending movies via on-demand platforms like Netflix. Machine intelligence is being heavily invested in by the “big four” tech giants – Apple, Google, Amazon and Facebook – as well as many others, to customize their offerings and better understand their customers.

Present electronic methods to machine learning, on the other hand, have their limits, involving ever-increasing processing capacity and heavy energy consumption. The latest breed in “neuromorphic computing”, which attempts to electronically imitate human brain behavior, is hampered by intrinsic shortcomings of traditional electronics.

Human brain cells, on the other hand, effortlessly integrate these functions and need relatively little power demand, consuming only a limited amount of nutrient-rich solution.

In the Neu-Chip project, the team would layer networks of stem cells matching the human cortex onto microchips. they would then use shifting patterns of light beams to stimulate the cells. They will be able to observe the modifications the cells go through using sophisticated 3D data simulation to see how adaptable they are.

Evolutionary Secrets of the Microbiome

How a gut microbiota deals with changes in habitat through reversible genetic inversion

Researchers from the Technion have discovered how a gut microbiota responds to changes in habitat by reversibly inverting its genetic code.

How does our gut respond to shifting circumstances and adapt? What is the source of this essential and crucial flexibility? Scientist at Technion are trying to decode the genius of the intestinal microbiome, from microbiota through genetic inversion.

In partnership with Harvard university scientists, assistant Professor Naama Geva-zatorsky and doctoral student Nadav Ben-Assa of the Rappaport Faculty of Medicine have decoded a reversible genetic inversion process that lets a bacterial species of the gut microbiota cope with changes in its environment. Their results were published in Nucleic Acids Research, an Oxford University Press peer-reviewed scientific journal.

The human microbiota is a group of microbes (bacteria, viruses, etc.) that colonize the human body’s inner and outer surfaces. The microbiota species of the human intestine is the most numerous and complex. 

Gut microbiota have a critical coping mechanism in the gut’s complex climate, which is constantly changing in terms of structural, technological, and chemical changes. Rapid, reversible changes in genomes in response to external stimulus are one mechanism that aids the gut microbiota’s efficiency.

Bacteroides fragilis, one of the most common bacterial organisms in the human gut, is discussed in the article published in Nucleic Acids Research. the bacterium has the ability to invert a large number of specified regions across its entire genome sequence. The Researchers were particularly interested in the connection between this capacity and the organism’s gene expression.

The are reversible genetic inversions, according to the researchers, and are dependent on recombination of regions in the genome in a major structure B. fragilis organism. As a result, the recombination has a large impact on the organism’s gene’s function , including the expression of many important molecules.

The Technion President’s Fund, the Alon Fellowships, the Israel Science Foundation, the Applebaum family foundation, the Gutwirth Fellowships, and Human Frontiers all contributed to the research.