Independent.co.uk
Cheese contains a chemical found in addictive drugs, scientists have found.
The team behind the study set out to pin-point why certain foods are more addictive than others.
Using the Yale Food Addiction Scale, designed to measure a person’s dependence on, scientists found that cheese is particularly potent because it contains casein.
Recent therapeutic trials of “classical” psychedelic drugs, such as psilocybin (from magic mushrooms) or LSD, have reported benefits to wellbeing, depression and anxiety. These effects seem to be linked to a sense of “ego dissolution” — a dissolving of the subjective boundaries between the self and the wider world. However, the neurochemistry behind this effect has been unclear. Now a new paper, published in Neuropsychopharmacology, suggests that changes in brain levels of the neurotransmitter glutamate are key to understanding reports of ego dissolution — and perhaps the therapeutic effects of psychedelics.
Natasha Mason at Maastricht University, the Netherlands, and colleagues recruited 60 participants for their study. All had taken a psychedelic drug before, but not in the three months prior to the study. Half received a placebo and the other half were given a low to moderate dose of psilocybin (0.17 mg/kg of body weight).
The team then used a technique called proton magnetic resonance spectroscopy (MRS) to look at concentrations of glutamate (as well as other neurochemicals) in the medial prefrontal cortex (mPFC) and the hippocampus — two regions that have been implicated as key to the psychedelic drug experience. The team also looked at patterns of “functional connectivity” within networks of brain regions, a measure of how closely correlated brain activity is across those regions. Six hours after taking the drug or placebo, the participants reported on their subjective experiences using two surveys: The 5 Dimensions of Altered States of Consciousness and the Ego Dissolution Inventory.
As the researchers expected (based on the findings of earlier research), those given the drug reported increased feelings of ego dissolution, as well as altered states of consciousness. They also showed disruptions in the connectivity of particular networks, including the default mode network, which has also been implicated in past work on the effects of psychedelic drugs…
But, for the first time in humans, the team also observed higher levels of glutamate in the mPFC and lower levels in the hippocampus after taking psilocybin — and they linked these changes to different aspects of ego dissolution. Increases in the mPFC were most strongly linked to unpleasant aspects, such as a loss of control over thoughts and decision-making, and also anxiety. Decreases in the hippocampus, meanwhile, were most strongly linked to more positive aspects, such as feelings of unity with the wider world, and of having undergone a spiritual-type experience.
The hippocampus is our most important memory structure. Based on earlier work on the impacts of psychedelic drugs on patterns of brain connectivity, it’s been suggested that a temporary reduction or loss of access to memories about our own lives might contribute to a weakening of the “self”. The new work suggests that changes in glutamate levels in the hippocampus might be key to this process.
But if glutamate rises in the mPFC are linked to unpleasant aspects of ego dissolution, and also to anxiety, how does this fit in with trial results finding that psychedelic drugs can treat anxiety disorders?
Researchers at the Nanoscience Center and at the Faculty of Information Technology at the University of Jyväskylä in Finland have demonstrated that new distance-based machine learning methods developed at the University of Jyväskylä are capable of predicting structures and atomic dynamics of nanoparticles reliably. The new methods are significantly faster than traditional simulation methods used for nanoparticle research and will facilitate more efficient explorations of particle-particle reactions and particles’ functionality in their environment. The study was published in a Special Issue devoted to machine learning in the Journal of Physical Chemistry on May 15, 2020.
The new methods were applied to ligand-stabilized metal nanoparticles, which have been long studied at the Nanoscience Center at the University of Jyväskylä. Last year, the researchers published a method that is able to successfully predict binding sites of the stabilizing ligand molecules on the nanoparticle surface. Now, a new tool was created that can reliably predict potential energy based on the atomic structure of the particle, without the need to use numerically heavy electronic structure computations. The tool facilitates Monte Carlo simulations of the atom dynamics of the particles at elevated temperatures.
Potential energy of a system is a fundamental quantity in computational nanoscience, since it allows for quantitative evaluations of system’s stability, rates of chemical reactions and strengths of interatomic bonds. Ligand-stabilized metal nanoparticles have many types of interatomic bonds of varying chemical strength, and traditionally the energy evaluations have been done by using the so-called density functional theory (DFT) that often results in numerically heavy computations requiring the use of supercomputers. This has precluded efficient simulations to understand nanoparticles’ functionalities, e.g., as catalysts, or interactions with biological objects such as proteins, viruses, or DNA. Machine learning methods, once trained to model the systems reliably, can speed up the simulations by several orders of magnitude.
Researchers at Linköping University, Sweden, are attempting to convert carbon dioxide, a greenhouse gas, to fuel using energy from sunlight. Recent results have shown that it is possible to use their technique to selectively produce methane, carbon monoxide or formic acid from carbon dioxide and water.
The study has been published in ACS Nano (“Atomic-Scale Tuning of Graphene/Cubic SiC Schottky Junction for Stable Low-Bias Photoelectrochemical Solar-to-Fuel Conversion”).
Plants convert carbon dioxide and water to oxygen and high-energy sugars, which they use as “fuel” to grow. They obtain their energy from sunlight. Jianwu Sun and his colleagues at Linköping University are attempting to imitate this reaction, known as photosynthesis, used by plants to capture carbon dioxide from air and convert it to chemical fuels, such as methane, ethanol and methanol. The method is currently at a research stage, and the long-term objective of the scientists is to convert solar energy to fuel efficiently.
Researchers in the USA have developed a graphene-based electrochemical sensor capable of detecting histamines (allergens) and toxins in food much faster than standard laboratory tests.
The team used aerosol-jet printing to create the sensor. The ability to change the pattern geometry on demand through software control allowed rapid prototyping and efficient optimization of the sensor layout.
Commenting on the findings, which are published today in the IOP Publishing journal 2-D Materials, senior author Professor Mark Hersam, from Northwestern University, said: “We developed an aerosol-jet printable graphene ink to enable efficient exploration of different device designs, which was critical to optimizing the sensor response.”
A laser pulse, a special material, an extraordinary property which appears inexplicably. These are the main elements that emerge from a research conducted by an international team, coordinated by Michele Fabrizio and comprising Andrea Nava and Erio Tosatti from SISSA, Claudio Giannetti from the Università Cattolica di Brescia and Antoine Georges from the Collège de France. The results of their study have recently been published in the journal Nature Physics. The key element of the study is a compound of the most symmetrical molecule that exists in Nature, namely C60 bucky-ball, a spherical fullerene.
It is well known that this compound, with the chemical formula K3C60, can behave as a superconductor — that is, conduct without dissipating energy — below a critical temperature of 20 degrees Kelvin, i.e. around −253 degrees Celsius.
It has recently been discovered that K3C60 is capable of transforming into a high-temperature superconductor when struck by an extremely brief laser pulse. This material takes on superconductive properties — albeit extremely briefly — up to a temperature of −73 degrees Centigrade, almost 100 degrees above the critical equilibrium temperature. The research just published by the scientists explains the reason for this mysterious behaviour.
There is great promise in 2020 that we might be able to make our bodies young without having to explicitly repair molecular damage, but just by changing the signaling environment.
Do we need to add signals that say “young” or remove signals that say “old”?
Does infusion of biochemical signals from young blood plasma rejuvenate tissues of an old animal? Or are there dissolved signal proteins in old animals that must be removed?
Light-sheet images of DEEP-Clear processed zebrafish showing proliferative cells (pink) and the nervous system (green). Credit: TU Wien / Max Perutz Labs.
An important observation that helped to develop the new method was that the combination of different chemical treatments had a synergistic effect, allowing for fast depigmentation and tissue clearing. “Shortening chemical processing preserves the integrity of tissues and organisms, so that the molecules and internal structures of interest are more likely to be retained,” explains Marko Pende, the developer of the clearing method, from the lab of Hans-Ulrich Dodt at the TU Wien and the Center for Brain Research (CBR) of the Medical University of Vienna, and one of the first authors of the study. This way multiple organisms could be imaged from different clades ranging from mollusks to bony fish to amphibians. “These are just a few examples. We believe that the method is applicable to multiple organisms. It was just not tried yet”, explains Prof. Hans Ulrich Dodt, senior author of the study.
Researchers at Tufts University’s School of Engineering have developed biomaterial-based inks that respond to and quantify chemicals released from the body (e.g. in sweat and potentially other biofluids) or in the surrounding environment by changing color. The inks can be screen printed onto textiles such as clothes, shoes, or even face masks in complex patterns and at high resolution, providing a detailed map of human response or exposure. The advance in wearable sensing, reported in Advanced Materials, could simultaneously detect and quantify a wide range of biological conditions, molecules and, possibly, pathogens over the surface of the body using conventional garments and uniforms.
“The use of novel bioactive inks with the very common method of screen printing opens up promising opportunities for the mass-production of soft, wearable fabrics with large numbers of sensors that could be applied to detect a range of conditions,” said Fiorenzo Omenetto, corresponding author and the Frank C. Doble Professor of Engineering at Tufts’ School of Engineering. “The fabrics can end up in uniforms for the workplace, sports clothing, or even on furniture and architectural structures.”
Wearable sensing devices have attracted considerable interest in monitoring human performance and health. Many such devices have been invented incorporating electronics in wearable patches, wristbands, and other configurations that monitor either localized or overall physiological information such as heart rate or blood glucose. The research presented by the Tufts team takes a different, complementary approach—non-electronic, colorimetric detection of a theoretically very large number of analytes using sensing garments that can be distributed to cover very large areas: anything from a patch to the entire body, and beyond.