Recently, a fake news article circulated the internet claiming that scientists had proven that stopping the ageing process was not possible. In this brief article, we explain why this claim is patently false and based entirely upon a wilful misinterpretation of scientific data.
A study in the Journal of Investigative Dermatology suggested that using a cannabinoid receptor type 2 (CB2) agonist called lenabasum may lessen the discomfort caused by amyopathic dermatomyositis. Dermatomyositis is a rare systemic autoimmune disease with distinctive cutaneous features frequently accompanied by muscle inflammation, interstitial lung disease, and malignancy. This phase 2 trial examined the potential benefits of activating the endocannabinoid system to reduce the inflammation causing the symptoms.
Study participants included twenty-two adults diagnosed with moderate to severe skin disease caused by dermatomyositis. They received 20 mg daily of lenabasum or a placebo for 28 days, then 20 mg twice daily for 56 days. Their Cutaneous Dermatomyositis Disease Area and Severity Index (CDASI) levels were evaluated relative to baseline as well as secondary outcomes such as quality of life (measured with the Skindex-29) and specific biomarkers.
This is according to a press release by the institutions published on Thursday.
“We’ve come up with an unprecedented principle. Yes, the wood transistor is slow and bulky, but it does work, and has huge development potential,” said Isak Engquist, senior associate professor at the Laboratory for Organic Electronics at Linköping University.
This isn’t the first time scientists have attempted to produce wooden transistors but previous trials resulted in versions that could regulate ion transport only. Making matters worse was the fact that when the ions ran out, the transistor stopped functioning.
Research investigates whether the severity of SARS-CoV-2 infection can be predicted by analyzing the immunophenotype in the blood of cardiovascular disease (CVD) patients.
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 machine learning approach that can learn patterns from ECGs and MRIs simultaneously, and based on those patterns, predict characteristics of a patient’s heart. 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.
Worms can entangle themselves into a single, giant knot, only to quickly unravel themselves from the tightly wound mess within milliseconds. Now, math shows how they do it.
Researchers studied California blackworms (Lumbriculus variegatus) — thin worms that can grow to be 4 inches (10 centimeters) in length — in the lab, watching as the worms intertwined by the thousands. Even though it took the worms minutes to form into a ball-shaped blob akin to a snarled tangle of Christmas lights, they could untangle from the jumble in the blink of an eye when threatened, according to a study published April 28 in the journal Science (opens in new tab).
Explore the latest trends in deep learning and artificial intelligence with five emerging technologies including federated learning, XAI and more!
Using AI to read people’s thoughts? 😀
In a groundbreaking study, scientists employ a ChatGPT-like AI model to passively decode human thoughts with unprecedented accuracy, unlocking new potential in brain imaging and raising privacy concerns.
An artificial intelligence chatbot was able to outperform human doctors in responding to patient questions posted online, according to evaluators in a new study.
Research published in the Journal of the American Medical Association (JAMA) Internal Medicine found that a chatbot’s responses to patient questions, pulled from a social media platform, were rated “significantly higher for both quality and empathy.”
Researchers designed nanoparticles that can deliver mRNA gene editing solutions directly to the lungs to address rare genetic diseases.
