New findings published in the Journal of the Science of Food and Agriculture take a closer look at what makes this fruit biologically active. The research focuses on the specific chemical compounds inside Luo Han Guo and how they may support health.

The Compounds Inside Monkfruit

One of the most important features of Luo Han Guo is its abundance of what scientists call secondary metabolites. These are natural plant chemicals that are not required for basic growth, but often play a role in defense, color, flavor, and health effects in humans. In this case, researchers concentrated on three major groups: terpenoids, flavonoids, and amino acids.

Terpenoids are compounds commonly found in plants that can have antioxidant and anti inflammatory properties. Flavonoids are another large class of plant compounds known for their ability to neutralize free radicals and support heart and metabolic health. Amino acids, which are the building blocks of proteins, are essential for many processes in the body, including tissue repair and immune function.

The scientists examined both the peel and the pulp of four different Luo Han Guo varieties. By doing so, they were able to identify where these compounds are concentrated and how they behave biologically.

How These Compounds Interact With the Body

Beyond simply identifying these substances, the study explored how they interact with antioxidant receptors and other biological targets. Receptors are structures in cells that receive chemical signals and trigger responses in the body. These interactions influence various biological pathways, which are step by step processes that help regulate functions such as inflammation, metabolism, and cellular protection.

Understanding these pathways helps explain why monk fruit may have health promoting properties beyond its use as a natural sweetener.

Why Researchers Studied Multiple Varieties

The researchers emphasized that not all monkfruit is chemically identical. Different varieties can contain different levels and combinations of active compounds. As the authors explained, “it is crucial to conduct an in-depth investigation on the high-resolution metabolic profiles of different Luo Han Guo varieties, providing valuable insights into the nutritional and health characteristics as well as the manufacturing suitability of the various resources available from this plant.”

This type of detailed chemical mapping helps scientists determine which varieties may be best suited for food products, supplements, or other uses.

Monk Fruit at a Glance

Monk fruit is a perennial vine native to China and a member of the gourd family. It is best known today as a zero calorie natural sweetener, but research shows it also contains a wide range of bioactive compounds. These include antioxidants, plant metabolites, and amino acids that interact with important biological systems in the body. As scientists continue to study its chemistry, monk fruit is emerging as more than just a sugar substitute, offering potential nutritional and health benefits rooted in its unique natural makeup.

The research, published in Communications Earth & Environment, introduces the concept of a marine darkwave. These events are short-lived but intense periods of underwater darkness that can seriously disrupt kelp forests, seagrass meadows, and other marine organisms that depend on light to survive.

Why Light Matters in the Ocean

“We have long known that light levels are critical for photosynthetic organisms — like algae, seagrasses and corals — and that factors that reduce light to the seafloor can impact them,” said co-author Bob Miller, a research biologist at UC Santa Barbara’s Marine Science Institute. “This study creates a framework for comparing such events, which we call darkwaves.”

Until now, scientists lacked a shared method for evaluating extreme losses of underwater light across different regions. The goal of the new framework is to make these events measurable and comparable worldwide.

“Light is a fundamental driver of marine productivity, yet until now we have not had a consistent way to measure extreme reductions in underwater light,” said lead author François Thoral, a postdoctoral fellow at the University of Waikato and Earth Sciences New Zealand.

Decades of Data Reveal a Hidden Pattern

To build the framework, researchers analyzed long-term data from multiple coastal regions. The study used 16 years of measurements from the Santa Barbara Coastal Long Term Ecological Research Site (LTER) and 10 years of observations from New Zealand coastal locations in Hauraki Gulf/Tīkapa Moana, in the Firth of Thames. The team also examined 21 years of seafloor light estimates derived from satellite data along New Zealand’s East Cape.

Across these areas, marine darkwaves ranged from brief events lasting only a few days to prolonged episodes that persisted for more than two months. In some cases, light reaching the seabed was almost completely eliminated.

The researchers identified between 25 and 80 darkwave events along the East Cape since 2002. Many were associated with powerful storms and large-scale weather systems, including Cyclone Gabrielle.

Short-Term Darkness With Lasting Consequences

For years, scientists have focused on slow, long-term declines in water clarity as a major threat to coastal ecosystems. The new findings suggest that sudden darkwave events may be just as damaging.

“Even short periods of reduced light can impair photosynthesis in kelp forests, seagrass and corals,” Thoral said. “These events can also influence the behaviour of fish, sharks and marine mammals. When darkness persists, the ecological effects can be significant.”

A New Tool for Tracking Ocean Stress

The marine darkwave framework adds to existing tools used to monitor marine heatwaves, ocean acidification, and deoxygenation. Together, these systems offer coastal communities, conservation groups, and resource managers a clearer way to recognize when marine ecosystems are under intense and immediate stress.

Because the Santa Barbara Coastal LTER is one of the few programs worldwide that collects long-term measurements of light on the seafloor, Miller and his colleagues at UCSB plan to expand their work. They aim to investigate how sedimentation and turbidity — which are influenced by fires and mudslides — affect California’s kelp forests.

The system, known as CytoDiffusion, relies on generative AI, the same type of technology used in image generators such as DALL-E, to analyze blood cell appearance in detail. Rather than focusing only on obvious patterns, it studies subtle variations in how cells look under a microscope.

Moving Beyond Pattern Recognition

Many existing medical AI tools are trained to sort images into predefined categories. In contrast, the team behind CytoDiffusion demonstrated that their approach can recognize the full range of normal blood cell appearances and reliably flag rare or unusual cells that may signal disease. The work was led by researchers from the University of Cambridge, University College London, and Queen Mary University of London, and the findings were published in Nature Machine Intelligence.

Identifying small differences in blood cell size, shape, and structure is central to diagnosing many blood disorders. However, learning to do this well can take years of experience, and even highly trained doctors may disagree when reviewing complex cases.

“We’ve all got many different types of blood cells that have different properties and different roles within our body,” said Simon Deltadahl from Cambridge’s Department of Applied Mathematics and Theoretical Physics, the study’s first author. “White blood cells specialize in fighting infection, for example. But knowing what an unusual or diseased blood cell looks like under a microscope is an important part of diagnosing many diseases.”

Handling the Scale of Blood Analysis

A standard blood smear can contain thousands of individual cells, far more than a person can realistically examine one by one. “Humans can’t look at all the cells in a smear — it’s just not possible,” Deltadahl said. “Our model can automate that process, triage the routine cases, and highlight anything unusual for human review.”

This challenge is familiar to clinicians. “The clinical challenge I faced as a junior hematology doctor was that after a day of work, I would face a lot of blood films to analyze,” said co-senior author Dr. Suthesh Sivapalaratnam from Queen Mary University of London. “As I was analyzing them in the late hours, I became convinced AI would do a better job than me.”

Training on an Unprecedented Dataset

To build CytoDiffusion, the researchers trained it on more than half a million blood smear images collected at Addenbrooke’s Hospital in Cambridge. The dataset, described as the largest of its kind, includes common blood cell types, rare examples, and features that often confuse automated systems.

Instead of simply learning how to separate cells into fixed categories, the AI models the entire range of how blood cells can appear. This makes it more resilient to differences between hospitals, microscopes, and staining techniques, while also improving its ability to detect rare or abnormal cells.

Detecting Leukemia With Greater Confidence

When tested, CytoDiffusion identified abnormal cells associated with leukemia with much higher sensitivity than existing systems. It also performed as well as or better than current leading models, even when trained with far fewer examples, and was able to quantify how confident it was in its own predictions.

“When we tested its accuracy, the system was slightly better than humans,” said Deltadahl. “But where it really stood out was in knowing when it was uncertain. Our model would never say it was certain and then be wrong, but that is something that humans sometimes do.”

Co-senior author Professor Michael Roberts from Cambridge’s Department of Applied Mathematics and Theoretical Physics said the system was evaluated against real-world challenges faced by medical AI. “We evaluated our method against many of the challenges seen in real-world AI, such as never-before-seen images, images captured by different machines and the degree of uncertainty in the labels,” he said. “This framework gives a multi-faceted view of model performance which we believe will be beneficial to researchers.”

When AI Images Fool Human Experts

The team also found that CytoDiffusion can generate synthetic images of blood cells that look indistinguishable from real ones. In a ‘Turing test’ involving ten experienced hematologists, the specialists were no better than random chance at telling real images apart from those created by the AI.

“That really surprised me,” Deltadahl said. “These are people who stare at blood cells all day, and even they couldn’t tell.”

Opening Data to the Global Research Community

As part of the project, the researchers are releasing what they describe as the world’s largest publicly available collection of peripheral blood smear images, totaling more than half a million samples.

“By making this resource open, we hope to empower researchers worldwide to build and test new AI models, democratize access to high-quality medical data, and ultimately contribute to better patient care,” Deltadahl said.

Supporting, Not Replacing, Clinicians

Despite the strong results, the researchers emphasize that CytoDiffusion is not intended to replace trained doctors. Instead, it is designed to assist clinicians by quickly flagging concerning cases and automatically processing routine samples.

“The true value of healthcare AI lies not in approximating human expertise at lower cost, but in enabling greater diagnostic, prognostic, and prescriptive power than either experts or simple statistical models can achieve,” said co-senior author Professor Parashkev Nachev from UCL. “Our work suggests that generative AI will be central to this mission, transforming not only the fidelity of clinical support systems but their insight into the limits of their own knowledge. This ‘metacognitive’ awareness — knowing what one does not know — is critical to clinical decision-making, and here we show machines may be better at it than we are.”

The team notes that additional research is needed to increase the system’s speed and to validate its performance across more diverse patient populations to ensure accuracy and fairness.

The research received support from the Trinity Challenge, Wellcome, the British Heart Foundation, Cambridge University Hospitals NHS Trust, Barts Health NHS Trust, the NIHR Cambridge Biomedical Research Centre, NIHR UCLH Biomedical Research Centre, and NHS Blood and Transplant. The work was carried out by the Imaging working group within the BloodCounts! consortium, which aims to improve blood diagnostics worldwide using AI. Simon Deltadahl is a Member of Lucy Cavendish College, Cambridge.