A recent study published in Neurochemistry International offers a surprising possibility. Researchers from Kindai University and partner institutions found that arginine, a naturally occurring amino acid, can reduce the buildup of harmful Aβ proteins in animal models of Alzheimer’s. Arginine also acts as a safe chemical chaperone, helping proteins maintain their proper structure.

The team noted that while arginine is widely available as an over-the-counter supplement, the doses and methods used in this study were specifically designed for research and are not the same as commercial products.

The research group included Graduate Student Kanako Fujii and Professor Yoshitaka Nagai from the Department of Neurology at Kindai University Faculty of Medicine in Osaka, along with Associate Professor Toshihide Takeuchi from the Life Science Research Institute at Kindai University.

Lab and Animal Studies Show Strong Effects

In laboratory experiments, the scientists first showed that arginine can block the formation of Aβ42 aggregates, which are considered especially toxic. The effect increased with higher concentrations.

They then tested oral arginine in two well-established Alzheimer’s models:

• A Drosophila model, expressing Aβ42 with the Arctic mutation (E22G)
• An App^NL-G-F knock-in mouse model, carrying three familial AD mutations

In both cases, arginine treatment reduced the accumulation of Aβ and lessened its harmful effects.

“Our study demonstrates that arginine can suppress Aβ aggregation both in vitro and in vivo,” explains Prof. Nagai. “What makes this finding exciting is that arginine is already known to be clinically safe and inexpensive, making it a highly promising candidate for repositioning as a therapeutic option for AD.”

Improved Brain Health and Reduced Inflammation

In the mouse model, the benefits went beyond reducing protein buildup. Arginine lowered amyloid plaque levels and reduced the amount of insoluble Aβ42 in the brain. Treated mice also performed better in behavioral tests.

The researchers found that arginine reduced the activity of genes linked to pro-inflammatory cytokines, which are associated with neuroinflammation, a major feature of Alzheimer’s disease. This suggests that arginine may not only prevent harmful protein aggregation but also protect brain cells more broadly.

“Our findings open up new possibilities for developing arginine-based strategies for neurodegenerative diseases caused by protein misfolding and aggregation,” notes Prof. Nagai. “Given its excellent safety profile and low cost, arginine could be rapidly translated to clinical trials for Alzheimer’s and potentially other related disorders.”

A Low-Cost Path Toward New Alzheimer’s Treatments

The study highlights the growing interest in drug repositioning, which involves finding new uses for existing, well-established compounds. Because arginine is already used clinically in Japan and has been shown to safely reach the brain, it could bypass some of the early hurdles that slow down traditional drug development.

Still, the researchers caution that more work is needed. Additional preclinical and clinical studies will be required to determine whether these results can be reproduced in humans and to establish the most effective dosing strategies.

Even so, the findings provide strong early evidence that simple nutritional or pharmacological approaches may help reduce amyloid buildup and improve brain function.

Expanding Understanding of Alzheimer’s Biology

Beyond its potential as a treatment, this work sheds new light on how Aβ proteins form and accumulate in the brain. It also points to a practical and cost-effective strategy that could eventually benefit millions of people living with Alzheimer’s worldwide.

Professor Yoshitaka Nagai, a neurologist and Chair of the Department of Neurology at Kindai University Faculty of Medicine in Osaka, focuses his research on neurodegenerative diseases including Alzheimer’s, Parkinson’s, and amyotrophic lateral sclerosis. His work centers on protein misfolding and RNA-related mechanisms, and he has received multiple honors from organizations such as the Japanese Society of Neurochemistry and the Japanese Dementia Society.

This research was supported by the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) (Grant No. 20H05927), Japan Society for the Promotion of Science (JSPS) (Grant Nos. 24H00630, 21H02840, 22H02792, and 25K02432), Japan Science and Technology Agency (JST) Super-Highway Program (SHW2023-03), and National Center of Neurology and Psychiatry.

Conditions like obesity and diabetes are often connected with a higher risk of depression, anxiety, and other mental health challenges. At the same time, people with psychiatric disorders are more likely to develop metabolic diseases. Because of this two-way relationship, researchers have been exploring whether treatments for physical health conditions might also influence mental well-being.

Large Study Tracks Nearly 100,000 People

To investigate this, researchers analyzed data from Swedish national health registers, following nearly 100,000 individuals between 2009 and 2022. More than 20,000 participants had used GLP-1 medications during that time. This large dataset allowed scientists to compare periods when people were taking the drugs with periods when they were not.

Significant Reductions in Depression, Anxiety, and Hospital Care

The findings showed a strong link between GLP-1 drug use and improved mental health outcomes. The use of GLP-1 medications — particularly semaglutide, the active ingredient in Ozempic and Wegovy — was associated with fewer psychiatric hospital visits and reduced sickness absence.

During times when people were taking semaglutide, psychiatric-related hospital care and sick leave dropped by 42% compared with periods when they were not using these medications. The risk of depression was 44% lower, while anxiety disorders were reduced by 38%.

Lower Risk of Substance Use and Suicidal Behavior

The study also found notable reductions in substance use disorders. Hospital care and work absence linked to substance use were 47% lower during periods of semaglutide use. In addition, GLP-1 receptor agonists were associated with a reduced risk of suicidal behavior.

One of the study’s authors, Professor Mark Taylor from Griffith University, says such results were to be expected: “An earlier study examining Swedish registers found the use of GLP-1 medications to be associated with a reduced risk of alcohol use disorder. Alcohol-related problems often have downstream effects on mood and anxiety, so we expected the effect to be positive on these as well.”

Why Might These Drugs Affect the Brain?

Even so, the strength of the results surprised the researchers. Because the study relied on registry data, it cannot prove exactly how or why these medications influence mental health.

“Because this is a registry-based study, we cannot determine exactly why or how these medications affect mood symptoms, but the association was quite strong. It is possible that, in addition to factors such as reduced alcohol consumption, weight loss-related improvements in body image, or relief associated with better glycemic control in diabetes, there may also be direct neurobiological mechanisms involved — for example, through changes in the functioning of the brain’s reward system,” says Research Director, Docent Markku Lähteenvuo from the University of Eastern Finland.

Published in a Leading Psychiatry Journal

The findings were published in The Lancet Psychiatry, a leading journal in the field. While some earlier studies on GLP-1 drugs and mental health have produced mixed results, many of those were smaller in scale. This large, long-term analysis adds stronger evidence that these widely used medications could have broader effects beyond managing blood sugar and weight.

The puzzle comes from a deep tension within statistical physics. A key foundation for understanding why time seems to move in one direction is Boltzmann’s H theorem, a central principle in statistical mechanics tied to the second law of thermodynamics. That law explains why entropy tends to increase over time, giving us a sense of past and future.

However, the H theorem itself is time-symmetric, meaning it does not prefer one direction of time over another. This creates a surprising implication. From a strictly formal standpoint, it is more probable for the patterns that make up our memories and observations to arise from random entropy fluctuations than from a real sequence of past events. Put simply, physics appears to allow the possibility that our memories are not reliable records but instead detailed illusions produced by chance. This unsettling idea is what defines the Boltzmann brain hypothesis.

How Assumptions About Time Shape the Debate

To better understand this problem, the researchers built a formal framework that examines how different assumptions affect conclusions about entropy and memory. Their work connects the Boltzmann brain hypothesis, the second law of thermodynamics, and the related “past hypothesis,” which assumes the universe began in a state of low entropy.

A crucial issue is which points in time are treated as fixed when analyzing how entropy evolves. Some approaches take the current state of the universe as given and work outward from there. Others assume a low-entropy starting point at the Big Bang. Importantly, the laws of physics do not specify which of these perspectives is correct, leaving room for interpretation.

Circular Reasoning in Entropy and Memory Arguments

The study introduces what the authors call the “entropy conjecture” to highlight a key problem in many existing arguments. They show that discussions about entropy, time, and memory often rely on subtle circular reasoning. In these cases, assumptions about the past are used to support conclusions, such as the reliability of memory or the direction in which entropy increases. Those same conclusions are then used to justify the original assumptions.

Rather than settling the debate, the researchers focus on making these hidden structures clear. By separating the role of physical laws from the assumptions we use to interpret them, the study provides a more transparent way to think about long-standing questions surrounding time, entropy, and the nature of memory.

Their findings, published in MNRAS, are based on a detailed analysis of observations from more than 2.2 million stars gathered during TESS’s first four years. The researchers focused on planets that orbit very close to their stars, completing a full orbit in less than 16 days. This approach has produced one of the most precise measurements yet of how common these short-period planets are.

“Using our newly developed RAVEN pipeline, we were able to validate 118 new planets, and over 2,000 high-quality planet candidates, nearly 1,000 of them entirely new,” said first author Dr. Marina Lafarga Magro, Postdoctoral Researcher at the University of Warwick. “This represents one of the best characterized samples of close in planets and will help us identify the most promising systems for future study.”

Rare and Extreme Planet Types Identified

The newly confirmed planets include several especially interesting categories. Some are ultra-short-period planets that circle their stars in under 24 hours. Others belong to the so-called ‘Neptunian desert,’ a region where few planets are expected to exist based on current theories. The study also revealed tightly packed multi-planet systems, including previously unknown pairs of planets orbiting the same star.

How RAVEN Improves Planet Detection

Modern planet-hunting missions often flag thousands of possible planets, but determining which signals are genuine remains difficult. Many false signals can mimic planets, including eclipsing binary stars.

“The challenge lies in identifying if the dimming is indeed caused by a planet in orbit around the star or by something else, like eclipsing binary stars, which is what RAVEN tries to answer. Its strength stems from our carefully created dataset of hundreds of thousands of realistically simulated planets and other astrophysical events that can masquerade as planets. We trained machine learning models to identify patterns in the data that can tell us the type of event we have detected, something that AI models excel at.” said Warwick’s Dr. Andreas Hadjigeorghiou, who led the development of the pipeline.

“In addition, RAVEN is designed to handle the whole process in one go, from detecting the signal, to vetting it with machine learning and statistically validating it. This gives the pipeline an additional edge over contemporary tools that only focus on specific parts of the workflow.”

Dr. David Armstrong, Associate Professor at Warwick and senior co-author on the RAVEN studies, added: “RAVEN allows us to analyse enormous datasets consistently and objectively. Because the pipeline is well-tested and carefully validated, this is not just a list of potential planets — it is also reliable enough use as a sample to map the prevalence of distinct types of planets around Sun-like stars.”

Measuring How Common Planets Really Are

With this carefully validated dataset, the researchers were able to go beyond individual discoveries and examine broader patterns. In a companion MNRAS study, they measured how often close-in planets occur around Sun-like stars, mapping results by orbital period and planet size with an unprecedented level of detail.

The results show that about 9-10% of Sun-like stars host a close-in planet. This aligns with earlier findings from NASA’s Kepler mission — a space telescope that previously measured planet occurrence rates, but the new analysis reduces uncertainties by up to a factor of ten.

The team also made the first direct measurement of how rare ‘Neptunian desert’ planets are, finding that they appear around just 0.08% of Sun-like stars.

“For the first time, we can put a precise number on just how empty this ‘desert’ is,” said Dr. Kaiming Cui, Postdoctoral Researcher at Warwick and first author of the population study. “These measurements show that TESS can now match, and in some cases surpass, Kepler for studying planetary populations.”

A New Era for Planet Discovery

Together, these studies highlight how advances in artificial intelligence are transforming astronomy. By combining massive datasets with machine learning, researchers can uncover new planets while also improving the tools themselves through challenging real-world data.

The team has also released interactive catalogs and tools so other scientists can explore the results and identify promising targets for follow-up observations using ground-based telescopes and future missions such as ESA’s PLATO.

What Is RAVEN

RAVEN is an automated system designed to address one of astronomy’s biggest challenges, turning enormous volumes of space telescope data into reliable discoveries. It scans data from millions of stars to find the tiny drops in brightness caused by planets passing in front of them. The system then uses artificial intelligence trained on realistic simulations to filter out false signals such as binary stars or instrument noise, before statistically confirming the strongest candidates.

Importantly, RAVEN also evaluates which types of planets are easier or harder to detect, helping researchers correct for hidden biases. This means it not only speeds up the discovery of new worlds but also produces cleaner, more reliable datasets that can be used to answer larger questions about how common different kinds of planets are across the galaxy.

The original idea came from Sir William Hamilton, British ambassador to Naples and Sicily from 1765 to 1800, who was also deeply interested in volcanology. His concept blended artistic expression with mechanical design to capture the dramatic visual effects of a volcanic eruption.

Inspired by the 1771 watercolor ‘Night view of a current of lava’ by British-Italian artist Pietro Fabris, the device was designed to use light and movement to mimic flowing lava and explosive bursts from Vesuvius. It remains uncertain whether Hamilton ever constructed the mechanism, but a detailed sketch preserved in the Bordeaux Municipal Library served as the foundation for its modern recreation.

Reconstructing the Historic Vesuvius Device

Dr. Richard Gillespie, Senior Curator in the Faculty of Engineering and Information Technology, launched the project and guided its development.

“It is fitting that after 250 years exactly, our students have brought this dormant project to life,” he said.

“It is a wonderful piece of science communication. People around the world have always been fascinated by the immense power of volcanoes.”

Modern Engineering Meets 18th-Century Design

Master of Mechatronics student Xinyu (Jasmine) Xu and Master of Mechanical Engineering student Yuji (Andy) Zeng spent three months building the device in The Creator Space student workshop. Using modern materials and technologies, including laser-cut timber and acrylic, programmable LED lighting, and electronic control systems, they adapted Hamilton’s clockwork-based design for today’s audience.

“The project offered a wealth of learning opportunities. I’ve extended many skills, including programming, soldering and physics applications,” Ms. Xu said.

Mr. Zeng said the experience gave him a deeper understanding of mechanical engineering in practice.

“It was a fantastic way to build my hands-on problem-solving skills,” he said. “We still faced some of the challenges that Hamilton faced. The light had to be designed and balanced so the mechanisms were hidden from view.”

Hands-On Learning and Engineering Skills

Research engineer Mr. Andrew Kogios, who supervised the students, highlighted the growth they achieved through the project.

“From selecting materials and 3D printing, to troubleshooting electronics and satisfying requirements, working collaboratively with Yuji and Xinyu has been extremely rewarding,” Mr. Kogios said. “Experiences like these, supplementing their university studies, position them well for their future endeavors.”

On Display at The Grand Tour Exhibition

The completed device is now the centerpiece of The Grand Tour, an exhibition at the University’s Baillieu Library, where it will be on display until June 28, 2026.