Phenolic compounds, particularly flavonoids, are already highly valued in medicine for their antioxidant, anti-inflammatory, and anti-carcinogenic effects. This new finding suggests Cannabis may contain even more biologically important compounds than previously recognized.

Dozens of Previously Unknown Cannabis Compounds Identified

In their study, researchers analyzed three commercially grown Cannabis strains from South Africa and identified 79 phenolic compounds. Of these, 25 had never before been reported in Cannabis. Among them were 16 compounds tentatively classified as flavoalkaloids, a group that is rarely found in nature.

Interestingly, these flavoalkaloids were concentrated mainly in the leaves of just one of the strains, highlighting how much chemical variation can exist between different types of Cannabis. The findings were recently published in the Journal of Chromatography A.

Why These Compounds Are Hard To Detect

Dr. Magriet Muller, an analytical chemist in the LC-MS laboratory of the Central Analytical Facility (CAF) at Stellenbosch University and the study’s first author, explains that studying plant phenolics is especially difficult because they occur in very small amounts and have highly diverse structures.

“Most plants contain highly complex mixtures of phenolic compounds, and while flavonoids occur widely in the plant kingdom, the flavoalkaloids are very rare in nature,” she explains.

She also notes just how chemically complex Cannabis is. “We know that Cannabis is extremely complex — it contains more than 750 metabolites — but we did not expect such high variation in phenolic profiles between only three strains, nor to detect so many compounds for the first time in the species. Especially the first evidence of flavoalkaloids in Cannabis was very exciting.”

Advanced Techniques Reveal Hidden Chemistry

As part of her postgraduate work in SU’s Department of Chemistry and Polymer Science, Muller developed advanced analytical methods that combine comprehensive two-dimensional liquid chromatography with high-resolution mass spectrometry. These tools allow scientists to separate and identify compounds in extraordinary detail.

“We were looking for a new application for the methods that I developed, after successfully testing them on rooibos tea, grapes and wine. I then decided to apply the methods to Cannabis because I knew it was a complex sample, and that Cannabis phenolics have not been well characterized,” she explains.

Prof. André de Villiers, who led the study and heads the analytical chemistry research group at SU, said the results were striking. “The excellent performance of two-dimensional liquid chromatography allowed separation of the flavoalkaloids from the much more abundant flavonoids, which is why we were able to detect these rare compounds for the first time in Cannabis.”

Untapped Medical Potential in Overlooked Plant Material

According to Prof. de Villiers, the discovery underscores how much remains to be learned about Cannabis. So far, most research has focused on cannabinoids, the compounds responsible for the plant’s psychoactive effects.

“Our analysis again highlights the medicinal potential of Cannabis plant material, currently regarded as waste. Cannabis exhibits a rich and unique non-cannabinoid phenolic profile, which could be relevant from a biomedical research perspective,” he concludes.

The findings suggest that even parts of the plant often discarded, such as leaves, may hold valuable compounds with potential uses in medicine.

To release this stored energy, the body relies on a protein called HSL. This protein works like a switch. When energy levels drop, hormones like adrenaline activate HSL, triggering the release of fat that can be used by organs throughout the body.

Why Losing HSL Does Not Lead to Weight Gain

At first glance, it might seem logical that without HSL, fat would build up since the body would struggle to access its stored energy. However, research in mice and in people with mutations in the HSL gene shows a surprising outcome. Instead of gaining fat, these individuals actually lose it.

This loss of fat leads to a condition called lipodystrophy, where the body has too little fat tissue. Rather than causing obesity, the absence of HSL disrupts normal fat storage and results in reduced fat mass.

Obesity and Lipodystrophy Share Hidden Risks

Although obesity and lipodystrophy appear to be opposite conditions, they share an important similarity. In both cases, fat cells do not function properly. This dysfunction can lead to similar health problems, including metabolic issues and increased risk of cardiovascular disease.

A Surprising Discovery Inside Fat Cells

To better understand this unexpected behavior, researchers led by Dominique Langin at the University of Toulouse within the I2MC examined where HSL operates inside fat cells. Traditionally, HSL has been known to sit on the surface of lipid droplets, where it helps break down fat.

The new study revealed something unexpected. HSL is also found inside the nucleus of adipocytes, the part of the cell that controls gene activity. “In the nucleus of adipocytes, HSL is able to associate with many other proteins and take part in a program that maintains an optimal amount of adipose tissue and keeps adipocytes ‘healthy’,” explains Jérémy Dufau, co-author of the study, who defended his doctoral thesis on this subject.

How HSL Moves Within the Cell

The researchers also discovered that the amount of HSL in the nucleus is carefully controlled. Adrenaline, which activates HSL to release fat, also signals the protein to leave the nucleus. This process occurs during fasting, when the body needs energy.

In contrast, studies in obese mice show higher levels of HSL remaining in the nucleus, suggesting that this balance may be disrupted in disease.

A New Role for a Well-Known Fat Enzyme

“HSL has been known since the 1960s as a fat-mobilizing enzyme. But we now know that it also plays an essential role in the nucleus of adipocytes, where it helps maintain healthy adipose tissue,” concludes Dominique Langin.

This newly identified function helps explain why people lacking HSL develop lipodystrophy. It also opens new avenues for understanding metabolic diseases, including obesity and its complications.

Why This Discovery Matters Now

The timing of this discovery is significant. In France, one in two adults is overweight or obese. Globally, about two and a half billion people are affected. Obesity raises the risk of serious conditions such as diabetes and heart disease and can reduce quality of life.

Advances like this highlight the need for continued research to improve prevention strategies and develop better treatments for metabolic disorders.

A major focus in Alzheimer’s research has been the buildup of plaque in the brain. This plaque consists of amyloid-β (Aβ), a peptide that forms naturally but can accumulate and cluster together over time. These deposits are widely believed to play a key role in driving the disease.

Targeting PTP1B To Improve Memory

Tonks, along with graduate student Yuxin Cen and postdoctoral fellow Steven Ribeiro Alves, has identified a new potential strategy. Their research shows that blocking a protein known as PTP1B can improve learning and memory in a mouse model of Alzheimer’s disease.

Tonks first discovered PTP1B in 1988 and has spent decades studying its role in health and disease. In this latest work, his team found that PTP1B interacts with another protein called spleen tyrosine kinase (SYK). SYK helps control microglia (the brain’s immune cells), which are responsible for clearing debris such as excess Aβ.

“Over the course of the disease, these cells become exhausted and less effective,” says Cen. “Our results suggest that PTP1B inhibition can improve microglial function, clearing up Aβ plaques.”

Links to Metabolism and Disease Risk

Alzheimer’s disease is also strongly associated with obesity and type 2 diabetes, both of which are recognized risk factors. These conditions are thought to contribute to the growing global burden of Alzheimer’s. Because PTP1B is already considered a therapeutic target for metabolic disorders, this connection strengthens the case for exploring it in Alzheimer’s treatment as well.

Toward More Effective Alzheimer’s Treatments

Current therapies for Alzheimer’s disease largely focus on reducing Aβ buildup, but their benefits are often limited for many patients. “Using PTP1B inhibitors that target multiple aspects of the pathology, including Aβ clearance, might provide an additional impact,” says Ribeiro Alves.

The Tonks lab is now collaborating with DepYmed, Inc. to develop PTP1B inhibitors for several medical applications. For Alzheimer’s disease, Tonks envisions combining these inhibitors with existing approved drugs. “The goal is to slow Alzheimer’s progression and improve quality of life of the patients,” he says. With PTP1B emerging as a promising target, this approach could help move closer to that goal.

Researchers note that while other types of exercise can still help, they should be used alongside aerobic activity rather than replacing it as the primary approach.

What Causes Knee Osteoarthritis

Osteoarthritis develops when the cartilage that cushions the ends of bones gradually breaks down. This leads to pain, swelling, and difficulty moving the joint. Although it can affect different parts of the body, the knees are especially vulnerable. Nearly 30% of adults over 45 show signs of knee osteoarthritis on x-rays, and about half of them experience severe symptoms.

Exercise is widely recommended as a key part of treatment, but current guidelines often lack clear advice on which specific types work best for knee osteoarthritis.

Large Review of 217 Clinical Trials

To address this gap, researchers carried out a comprehensive analysis of exercise therapies. They reviewed 217 randomized trials conducted between 1990 and 2024, involving a total of 15,684 participants. These studies compared several forms of exercise, including aerobic, flexibility, strengthening, mind-body, neuromotor, and mixed programs, against control groups.

Although the quality of the trials varied, the researchers used the recognized GRADE system to evaluate how reliable the evidence was.

Measuring Pain, Function, and Quality of Life

The analysis focused on key outcomes that matter most to patients. These included pain levels, physical function, gait performance, and quality of life. Results were examined at short term (four weeks), mid-term (12 weeks), and long term (24 weeks) follow up.

Across these measures, aerobic exercise consistently ranked as the most effective option among all exercise types studied.

Aerobic Exercise Shows the Strongest Results

Evidence with moderate certainty showed that aerobic exercise reduced pain in both the short and mid-term. It also improved physical function across short term, mid-term, and long term timeframes. In addition, it enhanced walking ability and quality of life in the short and mid-term.

Other forms of exercise also delivered meaningful benefits. Mind-body approaches likely improved short term function, while neuromotor training likely boosted short term gait performance. Strengthening and mixed exercise programs appeared to improve function at mid-term follow up.

Exercise Is Safe and Widely Beneficial

Importantly, none of the exercise types were linked to a higher risk of adverse events compared with control groups. This supports the idea that exercise is a safe and reliable treatment option for knee osteoarthritis.

The researchers did point out some limitations. Many of the comparisons were indirect, some outcomes lacked long term data, and smaller studies may have influenced early results.

Clear Guidance for Treatment

Despite these limitations, the study offers one of the most thorough and up-to-date evaluations of exercise for knee osteoarthritis. The findings can help guide clinicians in choosing the most effective therapies for their patients.

Based on the results, the researchers recommend aerobic exercise “as a first line intervention for knee osteoarthritis management, particularly when the aim is to improve functional capacity and reduce pain.” They also note that if aerobic exercise is not suitable due to individual limitations, “alternative forms of structured physical activity may still be beneficial.”

In July 2025, a study published in Nature introduced an AI model called “Centaur.” Built on standard large language models and refined using data from psychological experiments, Centaur was designed to simulate human cognitive behavior. It reportedly performed well across 160 tasks, including decision-making, executive control, and other mental processes. The results drew widespread attention and were seen as a possible step toward AI systems that could replicate human thinking more broadly.

New Research Raises Doubts

A more recent study published in National Science Open challenges those claims. Researchers from Zhejiang University argue that Centaur’s apparent success may come from overfitting. In other words, instead of understanding the tasks, the model may have learned to recognize patterns in the training data and reproduce expected answers.

To test this idea, the researchers created several new evaluation scenarios. In one example, they replaced the original multiple-choice prompts, which described specific psychological tasks, with the instruction “Please choose option A.” If the model truly understood the task, it should have consistently selected option A. Instead, Centaur continued to choose the “correct answers” from the original dataset.

This behavior suggests that the model was not interpreting the meaning of the questions. Rather, it relied on learned statistical patterns to “guess” answers. The researchers compared this to a student who scores well by memorizing test formats without actually understanding the material.

Why This Matters for AI Evaluation

The findings highlight the need for caution when assessing the abilities of large language models. While these systems can be highly effective at fitting data, their “black-box” nature makes it difficult to know how they arrive at their outputs. This can lead to issues such as hallucinations or misinterpretations. Careful and varied testing is essential to determine whether a model truly has the skills it appears to demonstrate.

The Real Challenge: Language Understanding

Although Centaur was presented as a model capable of simulating cognition, its biggest limitation appears to be in language comprehension. Specifically, it struggles to recognize and respond to the intent behind questions. The study suggests that achieving true language understanding may be one of the most important challenges in developing AI systems that can model human cognition more fully.