Mosquitoes can taste your blood using unique sensory abilities. Can we use that to keep them off us?
A new artificial intelligence program is helping scientists speedily sift through thousands of data sets and millions of papers to home in on genes that underly disease, drastically condensing a search process that once took months.
Using computer software, scientists can scan entire genomes, or an organism’s full set of DNA, of mice that model human diseases. The goal: to identify genetic mutations that cause those diseases and open new doors for scientists to better harness genetics to develop disease treatments, said Gary Peltz, MD, PhD, professor of anesthesiology, perioperative and pain medicine at Stanford Medicine.
But to do that, scientists must search through massive sets of genomic data, which yields more false positives than researchers care to admit. It’s also time intensive. Peltz wanted to make the genetic discovery process easier, faster and more accurate.
The genetic code of a rare form of kidney cancer, called reninoma, has been studied for the first time. In a paper, published in Nature Communications, researchers at the Wellcome Sanger Institute, Great Ormond Street Hospital and The Royal Free Hospital also revealed a new drug target that could serve as an alternative treatment if surgery is not recommended.
There are around 100 cases of reninoma reported to date worldwide, and it is among the rarest of tumors in humans. Although it can usually be cured with surgery, it can cause severe hypertension or it can spread and develop into metastases. There are no existing medical treatments for reninoma and management involves surgery alone. Until now, it had been unknown what genetic error causes reninoma.
In the new study, a collaboration between the Wellcome Sanger Institute and Great Ormond Street Hospital and The Royal Free Hospital, researchers found that there is a specific error in the genetic code of a known cancer gene, NOTCH1, that is behind the development of this rare cancer.
Elon Musk claims Neuralink could produce Star Wars-style robotic hands, and help save the world from AI.
Surgeons at Boston Children’s Hospital have invented a new kind of surgical robot that is extremely adept at handling brain operations.
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“We needed to use basically hollow arms,” DuPont explained to the magazine.
Call it the big bang for bug-sized robots.
Cornell researchers combined soft microactuators with high-energy-density chemical fuel to create an insect-scale quadrupedal robot that is powered by combustion and can outrace, outlift, outflex and outleap its electric-driven competitors.
YouTuber Lucas VRTech has designed and built a pair of finger-tracking VR gloves using just $22 in materials — and he’s released all the details on the build so others can make their own.
The challenge: We use our hands to manipulate objects in the real world, but in VR, users typically have to use controllers with buttons and joysticks.
That breaks some of the immersion, limiting the use of VR for not only gaming, but also applications like therapy and job training.
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However, there may be a solution on the way. Machine learning is being used by a group of academics from the National Research Council (NRC) and the University of Waterloo to address this age-old problem.
Researchers at the Francis Crick Institute, the University of Cambridge, Imperial College London, the University of Liverpool, the University of Cape Town and UKHSA have uncovered a link between an antiviral drug for COVID-19 infections called molnupiravir and a pattern of mutations in the SARS-CoV-2 virus.
Molnupiravir works by inducing mutations in the virus’s genetic information, or genome, during replication. Many of these mutations will damage or kill the virus, reducing viral load in the body. It was one of the first antivirals available on the market during the COVID-19 pandemic and was widely adopted by many countries.
In research published in Nature, the scientists used global sequencing databases to map mutations in the SARS-CoV-2 virus over time. They analyzed a family tree of 15 million SARS-CoV-2 sequences so that at each point in each virus’s evolutionary history they could see which mutations had occurred.