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The first blood test to diagnose inflammation of the heart muscle (myocarditis) could be in use in as little as a year, following the discovery of a molecular signal in the blood by Queen Mary University of London researchers. The research, published today in the journal Circulation, offers hope of a quick and cheap way of diagnosing the condition.

Myocarditis is a difficult condition to diagnose. Symptoms include a temperature, fatigue, chest pain and shortness of breath, which can all be easily mistaken for other conditions. The gold standard method for diagnosis is a biopsy, an expensive, invasive, and risky procedure which can sometimes still miss signs of the condition. It’s estimated that one young person dies suddenly every week in the UK due to previously undiagnosed myocarditis.

Now, a team of researchers led by BHF Professor Federica Marelli-Berg at Queen Mary University of London have found that the presence of T-cells—a type of white cell—expressing a molecule called cMet in the blood strongly indicates that a person has myocarditis. They say that cMet-expressing T cells levels could be detected through a routine blood test that could cost less than £50 with results available within hours.

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Mitigating and reversing the effects of climate change is the most important scientific challenge facing humanity. Carbon sequestration describes a range of technologies with the potential to reduce the concentration of carbon dioxide (CO2) in the atmosphere. Most of these schemes involve storing the gas underground, however, this is not without risk, and scientists are concerned that underground storage could lead to increased seismic activity (a phenomenon known as “induced seismicity”).

Now, researchers in the US and Switzerland have studied microseismicity, the small seismic events caused by carbon injection into host rock, at the Illinois Basin Decatur Project (IBDP) in the midwestern US. In 2011–2014, the IBDP injected one million tonnes of CO2 into an underground reservoir just above a rhyolite crystalline basin. Nikita Bondarenko and Roman Makhnenko at the University of Illinois and Yury Podladchikov at the University of Lausanne have used a combination of field observations and computer simulations to show how microseismicity at the IBDP is highly dependent on the microscale structure of the host rock.

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LONDON (AP) — The World Health Organization and the U.S. Centers for Disease Control and Prevention say measles immunization has dropped significantly since the coronavirus pandemic began, resulting in a record high of nearly 40 million children missing a vaccine dose last year.

In a report issued Wednesday, the WHO and the CDC said millions of children were now susceptible to measles, among the world’s most contagious diseases. In 2021, officials said there were about 9 million measles infections and 128,000 deaths worldwide.

The WHO and CDC said continued drops in vaccination, weak disease surveillance and delayed response plans due to COVID-19, in addition to ongoing outbreaks in more than 20 countries, mean that “measles is an imminent threat in every region of the world.”

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A research group led by 2014 Nobel laureate Hiroshi Amano at Nagoya University’s Institute of Materials and Systems for Sustainability (IMaSS) in central Japan, in collaboration with Asahi Kasei Corporation, has successfully conducted the world’s first room-temperature continuous-wave lasing of a deep-ultraviolet laser diode (wavelengths down to UV-C region).

These results, published in Applied Physics Letters, represent a step toward the widespread use of a technology with the potential for a wide range of applications, including and medicine.

Since they were introduced in the 1960s, and after decades of research and development, successful commercialization of laser diodes (LDs) was finally achieved for a number of applications with wavelengths ranging from infrared to blue-violet. Examples of this technology include optical communications devices with infrared LDs and Blu-ray discs using blue-violet LDs.

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Astronomers at Caltech have used a machine learning algorithm to classify 1,000 supernovae completely autonomously. The algorithm was applied to data captured by the Zwicky Transient Facility, or ZTF, a sky survey instrument based at Caltech’s Palomar Observatory.

“We needed a helping hand, and we knew that once we trained our computers to do the job, they would take a big load off our backs,” says Christoffer Fremling, a staff at Caltech and the mastermind behind the , dubbed SNIascore. “SNIascore classified its first supernova in April 2021, and, a year and a half later, we are hitting a nice milestone of 1,000 supernovae.”

ZTF scans the night skies every night to look for changes called transient events. This includes everything from moving asteroids to black holes that have just eaten stars to exploding stars known as supernovae. ZTF sends out hundreds of thousands of alerts a night to around the world, notifying them of these transient events. The astronomers then use other telescopes to follow up and investigate the nature of the changing objects. So far, ZTF data have led to the discovery of thousands of supernovae.

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