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Category: biotech/medical – Page 2,612

Automating Drug Discoveries Using Computer Vision

“Every time you miss a protein crystal, because they are so rare, you risk missing on an important biomedical discovery.”

- Patrick Charbonneau, Duke University Dept. of Chemistry and Lead Researcher, MARCO initiative.

Protein crystallization is a key step to biomedical research concerned with discovering the structure of complex biomolecules. Because that structure determines the molecule’s function, it helps scientists design new drugs that are specifically targeted to that function. However, protein crystals are rare and difficult to find. Hundreds of experiments are typically run for each protein, and while the setup and imaging are mostly automated, finding individual protein crystals remains largely performed through visual inspection and thus prone to human error. Critically, missing these structures can result in lost opportunity for important biomedical discoveries for advancing the state of medicine.

Artificial Intelligence And Prosthetics Join Forces To Create New Generation Bionic Hand

“Our main goal is to let patients control them as naturally as though they were their biological limbs,” says Professor Dario Farina from Imperial College.

A team of scientists from Imperial College London and the University of Göttingen have teamed up to create a ‘next generation’ bionic hand. This bionic hand is special because it uses artificial intelligence to improve its functionality.

Synthetic surfactant could ease breathing for patients with lung disease and injury

Human lungs are coated with a substance called surfactant which allows us to breathe easily. When lung surfactant is missing or depleted, which can happen with premature birth or lung injury, breathing becomes difficult. In a collaborative study between Lawson Health Research Institute and Stanford University, scientists have developed and tested a new synthetic surfactant that could lead to improved treatments for lung disease and injury.

Lung surfactant is made up of lipids and proteins which help lower tension on the ’s surface, reducing the amount of effort needed to take a breath. The proteins, called surfactant-associated proteins, are very difficult to create in a laboratory and so the surfactant most commonly used in medicine is obtained from animal lungs.

London, Ontario has a rich legacy in surfactant research and innovation. Dr. Fred Possmayer, a scientist at Lawson and Western University, pioneered the technique used to purify and sterilize lung surfactant extracted from cows. Called bovine lipid extract surfactant (BLES), the therapeutic is made in London, Ontario and used by nearly all neonatal intensive care units in Canada to treat premature babies with respiratory distress.

New Quantum Computer Milestone Would Make Richard Feynman Very Happy

A commercially available “quantum computer” has been on the market since 2011, but it’s controversial. The D-Wave machine is nothing like other quantum computers, and until recently, scientists have doubted that it was even truly quantum at all. But the company has released an important new result, one that in part realizes Richard Feynman’s initial dreams for a quantum computer.

Scientists from D-Wave announced they have simulated a large quantum mechanical system with their 2000Q machine—essentially a cube of connected bar magnets. The D-Wave can’t take on the futuristic, mostly non-physics-related goals that many people have for quantum computers, such as finding solutions in medicine, cybersecurity, and artificial intelligence. Nor does it work the same way as the rest of the competition. But it’s now delivering real physics results. It’s simulating a quantum system.

How to predict the side effects of millions of drug combinations

An example graph of polypharmacy side effects derived from genomic and patient population data, protein–protein interactions, drug–protein targets, and drug–drug interactions encoded by 964 different polypharmacy side effects. The graph representation is used to develop Decagon. (credit: Marinka Zitnik et al./Bioinformatics)

Millions of people take up to five or more medications a day, but doctors have no idea what side effects might arise from adding another drug.*

Now, Stanford University computer scientists have developed a deep-learning system (a kind of AI modeled after the brain) called Decagon** that could help doctors make better decisions about which drugs to prescribe. It could also help researchers find better combinations of drugs to treat complex diseases.