Little understood brain region linked to how we perceive pain

A new review paper, published in the journal Brain, has shown that a poorly understood region of the brain called the claustrum may play an important role in how we experience pain.

The little understood area of the brain called the claustrum may be the next frontier in improving outcomes for brain damage patients.

A collaboration of Oxford University research groups from the Department of Physiology, Anatomy & Genetics (DPAG), the Nuffield Department of Clinical Neurosciences (NDCN) and Experimental Psychology (EP) have uncovered new clues regarding the function of one of most densely interconnected, yet rarely studied, areas of the brain.

The researchers reviewed studies of patients with lesions in the claustrum, which although rare show cognitive impairments and seizures. Furthermore, the lack of clinical focus on the claustrum may mean there are many more cases yet to be uncovered.

They also uncovered an underappreciated link between the claustrum and pain. It is already known that there are links between the claustrum and perception, salience and the sleep-wake cycle, but this is the first time a research team has shown how the claustrum might be more involved in the debilitating experience of pain.

Unprecedented videos show RNA switching ‘on’ and ‘off’

Similar to a light switch, RNA switches (called riboswitches) determine which genes turn “on” and “off.” Although this may seem like a simple process, the inner workings of these switches have confounded biologists for decades.

Now researchers led by Northwestern University and the University at Albany discovered one part of RNA smoothly invades and displaces another part of the same RNA, enabling the structure to rapidly and dramatically change shape. Called “strand displacement,” this mechanism appears to switch genetic expression from “on” to “off.”

Using a simulation they launched last year, the researchers made this discovery by watching a slow-motion simulation of a riboswitch up close and in action. Affectionately called R2D2 (short for “reconstructing RNA dynamics from data”), the new simulation models RNA in three dimensions as it binds to a compound, communicates along its length and folds to turn a gene “on” or “off.”

The findings could have potential implications for engineering new RNA-based diagnostics and for designing successful drugs to target RNA to treat illness and disease.

Scientists identify overgrowth of key brain structure in babies who later develop autism

The amygdala is a small structure deep in the brain important for interpreting the social and emotional meaning of sensory input – from recognizing emotion in faces to interpreting fearful images that inform us about potential dangers in our surroundings. Historically the amygdala has been thought to play a prominent role in the difficulties with social behavior that are central to autism.

Researchers have long known the amygdala is abnormally large in school-age children with autism, but it was unknown precisely when that enlargement occurs. Now, for the first time, researchers from the Infant Brain Imaging Study Network, used magnetic resonance imaging to demonstrate that the amygdala grows too rapidly in infancy. Overgrowth begins between six and 12 months of age, prior to the age when the hallmark behaviors of autism fully emerge, enabling the earliest diagnosis of this condition. Increased growth of the amygdala in infants who were later diagnosed with autism differed markedly from brain-growth patterns in babies with another neurodevelopmental disorder, fragile X syndrome, where no differences in amygdala growth were observed.

Published in the American Journal of Psychiatry, the official journal of the American Psychiatric Association, this research demonstrated that infants with fragile X syndrome already exhibit cognitive delays at six months of age, whereas infants who will later be diagnosed with autism do not show any deficits in cognitive ability at six months of age, but have a gradual decline in cognitive ability between six and 24 months of age, the age when they were diagnosed with Autism Spectrum Disorder in this study. Babies who go on to develop autism show no difference in the size of their amygdala at six months. However, their amygdala begins growing faster than other babies (including those with fragile X syndrome and those who do not develop autism), between six and 12 months of age, and is significantly enlarged by 12 months. This amygdala enlargement continues through 24 months, an age when behaviors are often sufficiently evident to warrant a diagnosis of autism.

Promising nose spray could prevent and treat COVID-19

A newly discovered small molecule could be sprayed into people’s noses to prevent COVID-19 illness prior to exposure and provide early treatment if administered soon after infection, according to a study in mice led by Cornell researchers.

The study, published March 28 in the journal Nature, employed experimental mice engineered with human receptors for the SARS-CoV-2 virus on their cell surfaces and found that a molecule, called N-0385, inhibited entry of the virus into cells in the body. At Cornell, N-0385 was shown to protect mice from infection prior to exposure, while also providing effective treatment when administered up to 12 hours after exposure. The molecule was developed in collaboration with investigators at the Université de Sherbrooke in Quebec, Canada.

The treatment holds promise for both preventing disease and reducing severity of and mortality from COVID-19 post-infection with a few single daily doses.

“There are very few, if any, small molecule antivirals that have been discovered that work prophylactically to prevent infection,” said Hector Aguilar-Carreno, associate professor of virology in the Department of Microbiology and Immunology in the College of Veterinary Medicine, and a senior author of the paper, “A TMPRSS2 Inhibitor ACTS as a pan-SARS-CoV-2 prophylactic and therapeutic.” Other senior authors include Francois Jean, associate professor of microbiology and immunology at the University of British Columbia in Vancouver, and Richard Leduc, professor of pharmacology at the Université de Sherbrooke.

New trials for alopecia treatment are a success

A new study shows that one in three patients with a severe skin disease were able to regrow hair after being treated with a common arthritis drug.

The study is based on Phase 3 clinical trials using baricitinib, a Janus kinase (JAK) inhibitor, to treat alopecia areata, an often-disfiguring skin disease characterized by rapid loss of scalp hair, and sometimes eyebrows and eyelashes.

Phase 3 clinical trials are the final testing hurdle before a new treatment can be considered for U.S. Food and Drug Administration (FDA) approval.

“This is so exciting, because the data clearly show how effective baricitinib is,” said Dr. Brett King, an associate professor of dermatology at the Yale School of Medicine and lead author of the new study, published March 26 in the New England Journal of Medicine. “These large, controlled trials tell us that we can alleviate some of the suffering from this awful disease.”

Before and after images for participants who received 36 weeks of treatment for alopecia areata with baricitinib.

Alopecia areata is an autoimmune disorder in which the body’s immune system attacks hair follicles. More than 200,000 new cases emerge each year in the United States. Although alopecia areata can develop in patients of any age, it typically occurs in people under the age of 40.

There is currently no FDA-approved treatment for the disease.

For the new study, King and his colleagues conducted two large, randomized trials involving a total of 1,200 people. The participants were adults with severe alopecia areata, who had lost at least half of their scalp hair; many had lost all of their scalp hair.

Let quantum dots grow regularly

With this experimental setup, the researchers check the quality of the quantum dots. Green laser light is used to stimulate the quantum dots that then emit infrared light.
© İsmail Bölükbaşı

With the previous manufacturing process, the density of the structures was difficult to control. Now researchers can create a kind of checkerboard pattern. A step towards application, for example in a quantum computer.

Quantum points could one day form the basic information units of quantum computers. Researchers at the Ruhr University Bochum (RUB) and the Technical University of Munich (TUM) have significantly improved the manufacturing process for these tiny semiconductor structures, together with colleagues from Copenhagen and Basel. The quantum dots are generated on a wafer, a thin semiconductor crystal disc. So far, the density of the structures on it has been difficult to control. Now scientists can create specific arrangements - an important step towards an applicable component that should have a large number of quantum dots.

The team published the results on 28. March 2022 in the journal Nature Communications. A group led by Nikolai Bart, Prof. Dr. Andreas Wieck and Dr. Arne Ludwig from the RUB Chair for Applied Solid State Physics with the team around Christian Dangel and Prof. Dr. Jonathan Finley from the TUM working group semiconductor nanostructures and quantum systems as well as with colleagues from the universities of Copenhagen and Basel.

A tool for predicting the future

MIT researchers created a tool that enables people to make highly accurate predictions using multiple time-series data with just a few keystrokes. The powerful algorithm at the heart of their tool can transform multiple time series into a tensor, which is a multi-dimensional array of numbers (pictured). Credits: Figure courtesy of the researchers Source: MIT

Whether someone is trying to predict tomorrow’s weather, forecast future stock prices, identify missed opportunities for sales in retail, or estimate a patient’s risk of developing a disease, they will likely need to interpret time-series data, which are a collection of observations recorded over time.

Making predictions using time-series data typically requires several data-processing steps and the use of complex machine-learning algorithms, which have such a steep learning curve they aren’t readily accessible to nonexperts.

To make these powerful tools more user-friendly, MIT researchers developed a system that directly integrates prediction functionality on top of an existing time-series database. Their simplified interface, which they call tspDB (time series predict database), does all the complex modeling behind the scenes so a nonexpert can easily generate a prediction in only a few seconds.

The new system is more accurate and more efficient than state-of-the-art deep learning methods when performing two tasks: predicting future values and filling in missing data points.

NUS-Monash University collaboration produces universal flu vaccine candidate

Current influenza vaccines have shortcomings
Credit: NUS Yong Loo Lin School of Medicine

Influenza, commonly referred to as “flu”, is a major global public health concern and a huge economic burden to societies. Seasonal influenza epidemics afflict between 13 to 100 million individuals annually, including three to five million cases of severe illness and 300,000 to 600,000 deaths worldwide. This represents a top global public health concern and an extraordinary economic burden to all societies. Pandemics are less frequent, but are generally more severe and pose a greater threat. Over the past century, there have been at least four devastating pandemics caused by Influenza A virus which took the lives of hundreds of millions of individuals.

Although vaccination arguably represents the most effective way to prevent influenza, current vaccination strategies suffer from certain limitations, chief of which require current influenza vaccines to be updated annually to match circulating strains. This results in low vaccination take-up rates and poor coverage due to inaccurate prediction of circulating strains. Broadly protective, “universal” flu vaccines that do not need to be updated annually have therefore been pursued.

What Mercury’s Unusual Orbit Reveals About the Sun

Mercury is special. As the closest planet to the Sun, it occupies a region where the Sun’s influence is changing dramatically. The Sun’s magnetic field, which dominates space close to the Sun, is rapidly waning. And Mercury’s orbit – more elliptical or “oval-shaped” than any other planet – allows it to experience a wider range of solar magnetic field conditions than any other planet. As a result, Mercury provides a unique opportunity to study how the Sun’s influence on a planet varies with distance.

In a new study published in Nature Communications, Goddard scientists Norberto Romanelli and Gina DiBraccio used data from NASA’s MESSENGER spacecraft to study the Sun’s changing interaction with Mercury. As Mercury moves through the solar wind, the steady stream of particles escaping the Sun, some of them strike Mercury’s magnetosphere and bounce back towards the Sun. These rebounding solar wind particles generate low-frequency waves that reverberate through space, traveling “upstream” in the solar wind towards the Sun.

A better way to separate gases

A new membrane material, pictured here, could make purification of gases significantly more efficient, potentially helping to reduce carbon emissions.
Credits: Courtesy of the researchers

Industrial processes for chemical separations, including natural gas purification and the production of oxygen and nitrogen for medical or industrial uses, are collectively responsible for about 15 percent of the world’s energy use. They also contribute a corresponding amount to the world’s greenhouse gas emissions. Now, researchers at MIT and Stanford University have developed a new kind of membrane for carrying out these separation processes with roughly 1/10 the energy use and emissions.

Using membranes for separation of chemicals is known to be much more efficient than processes such as distillation or absorption, but there has always been a tradeoff between permeability — how fast gases can penetrate through the material — and selectivity — the ability to let the desired molecules pass through while blocking all others. The new family of membrane materials, based on “hydrocarbon ladder” polymers, overcomes that tradeoff, providing both high permeability and extremely good selectivity, the researchers say.

The findings are reported in the journal Science, in a paper by Yan Xia, an associate professor of chemistry at Stanford; Zachary Smith, an assistant professor of chemical engineering at MIT; Ingo Pinnau, a professor at King Abdullah University of Science and Technology, and five others.