An unexpected discovery surprised a scientist at the Max Planck Institute for Intelligent Systems in Stuttgart: nanometer-sized diamond particles, which were intended for a completely different purpose, shone brightly in a magnetic resonance imaging experiment — much brighter than the actual contrast agent, the heavy metal gadolinium. Could diamond dust — in addition to its use in drug delivery to treat tumor cells — one day become a novel contrast agent used for MRI? The research team now published their discovery in Advanced Materials.
Some of the world’s greatest discoveries happened by accident. While the discovery of diamond dust’s potential as a future MRI contrast agent may never be considered a turning point in science history, its signal-enhancing properties are nevertheless an unexpected finding which may open-up new possibilities: Diamond dust glows brightly even after days of being injected. Does that mean it could perhaps one day become an alternative to the widely used contrast agent gadolinium?
This heavy metal is used in clinics to detect tumors, inflammation, or vascular abnormalities for more than 30 years. It enhances the brightness of the image of affected areas. However, when injected into a patient’s bloodstream, gadolinium travels not only to tumor tissue but also to surrounding healthy tissue. It is retained in the brain and kidneys, persisting months to years after the last administration. The long-term effects on the patient are not yet known. Gadolinium also causes a number of other side effects. The search for an alternative has been underway for years.
Could diamond dust, a carbon-based material, become a well-tolerable alternative because of an unexpected discovery made in a laboratory at the Max Planck Institute for Intelligent Systems in Stuttgart?
Dr. Jelena Lazovic Zinnanti was working on an experiment using nanometer-sized diamond particles for an entirely different purpose. The research scientist, who heads the Central Scientific Facility Medical Systems at MPI-IS, was surprised when she put the 3 to 5 nanometer particles into tiny drug-delivery capsules made of gelatin. She wanted these capsules to rupture when exposed to heat. She assumed that diamond dust, with its high heat capacity, could help.
“I had intended to use the dust only to heat up the drug carrying capsules,” Jelena recollects. “I used gadolinium to track the dust particles’ position. I intended to learn if the capsules with diamonds inside would heat up better. While performing preliminary tests, I got frustrated, because gadolinium would leak out of the gelatin — just as it leaks out of the bloodstream into the tissue of a patient. I decided to leave gadolinium out. When I took MRI images a few days later, to my surprise, the capsules were still bright. Wow, this is interesting, I thought! The diamond dust seemed to have better signal enhancing properties than gadolinium. I hadn’t expected that.”
Jelena took these findings further by injecting the diamond dust into live chicken embryos. She discovered that while gadolinium diffuses everywhere, the diamond nanoparticles stayed in the blood vessels, didn’t leak out and later shone brightly in the MRI, just as they had done in the gelatin capsules. While other scientists had published papers showing how they used diamond particles attached to gadolinium for magnetic resonance imaging, no one had ever shown that diamond dust itself could be a contrast agent.
Two years later, Jelena became the lead author of a paper now published in Advanced Materials.
“Why the diamond dust shines bright in our MRI still remains a mystery to us,” says Jelena, who worked with Prof. Metin Sitti and researchers from the Physical Intelligence Department at MPI-IS and with Dr. Eberhard Goering from the MPI-IS’ neighboring institute, the MPI for Solid State Research. She can only assume the reason for the dust’s magnetic properties: “I think the tiny particles have carbons that are slightly paramagnetic. The particles may have a defect in their crystal lattice, making them slightly magnetic. That’s why they behave like a T1 contrast agent such as gadolinium. Additionally, we don’t know whether diamond dust could potentially be toxic, something that needs to be carefully examined in the future.”
If diamond dust is found to be safe and well tolerated by patients, Jelena believes it has the potential to become a new contrast agent option for future MRI scans, where it would be deposited in tissue with abnormal vasculature, such as tumors, but not in healthy tissue.

Physical activity in natural environments prevent almost 13,000 cases of non-communicable diseases a year in England and save treatment costs of more than £100m, new research from the University of Exeter has found.
According to the World Health Organization (WHO) the most common non-communicable diseases — including heart disease, stroke, cancer, diabetes, and chronic lung disease — cause 74 percent of global mortality. Non communicable diseases, also known as chronic diseases, are not passed from person to person and deaths attributed to these diseases are increasing in most countries.
Physical inactivity is associated with a range of non-communicable diseases, including cardiovascular diseases, type-2 diabetes, cancers, and mental health outcomes. In their Global Status Report on Physical Activity 2022, the WHO estimated 500 million new cases will occur globally between 2020 and 2030 should physical activity remain at today’s levels, incurring more than £21b a year in treatment costs.
Natural environments support recreational physical activity, with this new study focusing particularly on places such as beaches and coast, countryside, and open spaces in towns and cities like parks. Using data including a representative cross-sectional survey of the English population, researchers at the University of Exeter have estimated how many cases of six non-communicable diseases — major depressive disorder, type 2 diabetes, ischaemic heart disease, ischaemic stroke, colon cancer, and breast cancer — are prevented through nature-based recreational physical activity.
Speaking about the findings, published in Environment International, Dr James Grellier from the University of Exeter Medical School said: “We believe this is the first time an assessment like this has been conducted on a national scale and we’ve almost certainly underestimated the true value of nature-based physical activity in terms of disease prevention. Although we have focused on six of the most common non-communicable diseases, there are several less common diseases that can be prevented by physical activity, including other types of cancer and mental ill health. It’s important to note that our estimates represent annual costs. Since chronic diseases can affect people for many years, the overall value of physical activity at preventing each case is certainly much higher.”
Increasing population levels of physical activity is an increasingly important strategic goal for public health institutions globally. The WHO recommends that adults aged 18 to 64?should do at least 150 to 300 minutes of moderate intensity aerobic physical activity (or at least 75 to 150?minutes of vigorous-intensity aerobic physical activity) per week to maintain good health. However, globally 27.5 percent of adults do not meet these recommendations.
In 2019, 22-million adults in England aged 16 years or older visited natural environments at least once a week. At reported volumes of nature-based physical activity, Exeter researchers estimate this prevented 12,763 cases of non-communicable diseases, creating annual healthcare savings of £108.7m.
Population-representative data from the Monitor of Engagement with the Natural Environment survey were used to estimate the weekly volume of nature-based recreational physical activity by adults in England in 2019. Researchers used epidemiological dose-response data to calculate incident cases of six non-communicable diseases prevented through nature-based physical activity, and estimated associated savings using published costs of healthcare, informal care, and productivity losses. It’s estimated the healthcare cost of physical inactivity in England in 2019 is approximately £1b.
Dr James Grellier from the University of Exeter Medical School said: “For people without the access, desire, or confidence to take part in organised sports or fitness activities, nature-based physical activity is a far more widely available and informal option. We believe that our study should motivate decision-makers seeking to increase physical activity in the local population to invest in natural spaces, such as parks, to make it easier for people to be physically active.”

A new UCLA Health study found certain genetic variants could help explain how long-term pesticide exposure could increase the risk of Parkinson’s disease.
While decades of research have linked pesticide exposure and Parkinson’s disease risk, researchers have sought to explain why some individuals with high exposure develop the disease while others do not.
One longstanding hypothesis has been that susceptibility to the disease is a combination of both environmental and genetic factors.
The new study, published in the journal NPJ Parkinson’s Disease, used genetic data from nearly 800 Central Valley (California) residents with Parkinson’s disease, many of whom had long-term exposure to 10 pesticides used on cotton crops for at least a decade prior to developing the disease, with some patients having been exposed as far back as 1974. They examined their genetic makeup for rare variants in genes associated with the function of lysosomes, cellular compartments that break down waste and debris, thought to be associated with the development of Parkinson’s disease, and looked for enrichment of variants in patients with high exposure to pesticide use compared to a representative sample of the general population.
Researchers found that variants in these genes were enriched in patients with more severe Parkinson’s disease who also had higher exposure to pesticides. These genetic variants also appeared to be deleterious to protein function suggesting that disruption of lysosomal activity may be underling the development of Parkinson’s disease combined with pesticide exposure.
Dr. Brent Fogel, the study’s corresponding author and professor of Neurology and Human Genetics, said while the specific interactions between pesticides and the expression of these genetic variants requires further study, the results suggest that in someone with such variants, long-term exposure to the cotton pesticides could lead to the buildup of toxic compounds, due to alterations of the cells’ ability to break down damaged proteins and organelles — a process known as autophagy — and thus lead to Parkinson’s disease.
Previous studies have shown that altered autophagy can result in a buildup of a protein called alpha synuclein, which is abundant in the brain and neurons. As the protein builds up, it forms clumps known as “Lewy” bodies that are a pathological hallmark of Parkinson’s disease.

“The study supports the hypothesis that the genetic predisposition comes from minor changes in genes that are associated with lysosomal function,” Fogel said. “On a day-to-day basis, these variants are not having much of an impact. But under the right stress, such as exposure to certain pesticides, they can fail and that could, over time, lead to the development of Parkinson’s disease. This is called a gene-environment interaction.”
The findings build on decades of research by UCLA Health investigators Drs. Jeff Bronstein and Beate Ritz into the associations between pesticide exposure and Parkinson’s disease risk in the Central Valley.
The study’s co-lead author and assistant professor of Neurology at UCLA, Dr. Kimberly Paul, said Parkinson’s disease is the fastest growing neurodegenerative disease in the world. While an increase in the number of new patients is expected given the large aging population in the U.S., the rate of new Parkinson’s disease patients is outpacing the rate that is expected from aging alone, Paul said.
Paul said the findings of the new study raise the question of whether there are other genetic variants that may be altering the susceptibility to Parkinson’s disease among this population, including other biological pathways affected by different types of pesticides.
“These patients were susceptible somehow and if we can figure out why they were susceptible, maybe we can act on those pathways,” Paul said.
“There are data for a lot of common disorders suggesting that environmental influences impact the development of these diseases, but we don’t yet have a good way of measuring that impact or determining who is specifically at risk,” Fogel said. “This is a step forward in that direction.”

Uveitis is a rare inflammatory eye disease. Posterior and panuveitis in particular are associated with a poor prognosis and a protracted course of the disease. Diagnosis and monitoring can be challenging for healthcare professionals. Fundus autofluorescence (FAF) is a fast and non-invasive imaging technique that supports this. Researchers from the University Hospital Bonn and the University of Bonn, together with experts from Berlin, Münster and Mannheim, have drafted a review on how FAF can facilitate the diagnosis and monitoring of posterior uveitis and panuveitis. The results have now been published in the journal Biomolecules.
Uveitis is a rare inflammatory disease of the choroid of the eye, which lies between the retina and the sclera. “Depending on the inflamed anatomical structure, this disease can be divided into the subtypes anterior, intermediate, posterior and panuveitis. The exact diagnosis of posterior uveitis and panuveitis can be challenging, as there are many different and sometimes extremely rare subtypes,” explains Dr. Maximilian Wintergerst from the Eye Clinic at the University Hospital Bonn (UKB), who also conducts research at the University of Bonn. In the review, the researchers from Bonn, Berlin, Münster, and Mannheim now show how imaging using fundus autofluorescence (FAF) supports the diagnosis and monitoring of some posterior uveitis forms.
FAF provides indications of active inflammation
Fundus autofluorescence is a non-invasive method for imaging the fundus of the eye. “Using light of a precisely defined wavelength, so-called fluorophores in the tissue of the eye are stimulated to glow. The distribution of these fluorophores, the intensity of the light signal, and certain resulting light patterns can provide information about the underlying form of uveitis,” explains Wintergerst. In unclear cases, this can help to make the correct diagnosis. “In addition, the autofluorescence signal can also provide us with information on the current state of inflammation in certain forms of uveitis. For example, brightly illuminated areas in the retina are sometimes associated with active inflammation, while darker areas can indicate inactive inflammation,” adds Dr. Matthias Mauschitz, Head of the Uveitis Clinic at the UKB.
The wavelength used influences the result
“Depending on the wavelength used, the autofluorescence signal from the retina and choroid can differ significantly. Depending on the excitation wavelength, lesions can be imaged at different depths and therefore in different areas,” explains Mauschitz. In addition to their review, the researchers included a case series in which they compared the autofluorescence of different wavelengths. Overall, they found that the combination of different wavelengths can provide additional information about the underlying form of uveitis.
Combination of different wavelengths provides additional information
With their work, the research team would like to draw attention to autofluorescence imaging, which is very helpful in some forms of uveitis, and highlight new approaches for future research, such as the combination of autofluorescence imaging of different wavelengths. “Fundus autofluorescence plays an important role in the diagnosis and monitoring of posterior uveitis and panuveitis. In some specific subtypes of uveitis, it can also provide important indications of a flare-up of inflammatory activity,” summarizes Wintergerst.

Machines can learn not only to make predictions, but also to handle causal relationships. An international research team shows how this could make therapies safer, more efficient, and more individualized.
Artificial intelligence is making progress in the medical arena. When it comes to imaging techniques and the calculation of health risks, there is a plethora of AI methods in development and testing phases. Wherever it is a matter of recognizing patterns in large data volumes, it is expected that machines will bring great benefit to humanity. Following the classical model, the AI compares information against learned examples, draws conclusions, and makes extrapolations.
Now an international team led by Professor Stefan Feuerriegel, Head of the Institute of Artificial Intelligence (AI) in Management at LMU, is exploring the potential of a comparatively new branch of AI for diagnostics and therapy. Can causal machine learning (ML) estimate treatment outcomes — and do so better than the ML methods generally used to date? Yes, says a landmark study by the group, which has been published in the journal Nature Medicine: causal ML can improve the effectiveness and safety of treatments.
In particular, the new machine learning variant offers “an abundance of opportunities for personalizing treatment strategies and thus individually improving the health of patients,” write the researchers, who hail from Munich, Cambridge (United Kingdom), and Boston (United States) and include Stefan Bauer and Niki Kilbertus, professors of computer science at the Technical University of Munich (TUM) and group leaders at Helmholtz AI.
As regards machine assistance in therapy decisions, the authors anticipate a decisive leap forward in quality. Classical ML recognizes patterns and discovers correlations, they argue. However, the causal principle of cause and effect remains closed to machines as a rule; they cannot address the question of why. And yet many questions that arise when making therapy decisions contain causal problems within them. The authors illustrate this with the example of diabetes: Classical ML would aim to predict how probable a disease is for a given patient with a range of risk factors. With causal ML, it would ideally be possible to answer how the risk changes if the patient gets an anti-diabetes drug; that is, gauge the effect of a cause (prescription of medication). It would also be possible to estimate whether another treatment plan would be better, for example, than the commonly prescribed medication, metformin.
To be able to estimate the effect of a — hypothetical — treatment, however, “the AI models must learn to answer questions of a ‘What if?’ nature,” says Jonas Schweisthal, doctoral candidate in Feuerriegel’s team. “We give the machine rules for recognizing the causal structure and correctly formalizing the problem,” says Feuerriegel. Then the machine has to learn to recognize the effects of interventions and understand, so to speak, how real-life consequences are mirrored in the data that has been fed into the computers.
Even in situations for which reliable treatment standards do not yet exist or where randomized studies are not possible for ethical reasons because they always contain a placebo group, machines could still gauge potential treatment outcomes from the available patient data and thus form hypotheses for possible treatment plans, so the researchers hope. With such real-world data, it should generally be possible to describe the patient cohorts with ever greater precision in the estimates, thereby bringing individualized therapy decisions that much closer. Naturally, there would still be the challenge of ensuring the reliability and robustness of the methods.
“The software we need for causal ML methods in medicine doesn’t exist out of the box,” says Feuerriegel. Rather, “complex modeling” of the respective problem is required, involving “close collaboration between AI experts and doctors.” Like his TUM colleagues Stefan Bauer and Niki Kilbertus, Feuerriegel also researches questions relating to AI in medicine, decision-making, and other topics at the Munich Center for Machine Learning (MCML) and the Konrad Zuse School of Excellence in Reliable AI. In other fields of application, such as marketing, explains Feuerriegel, the work with causal ML has already been in the testing phase for some years now. “Our goal is to bring the methods a step closer to practice. The paper describes the direction in which things could move over the coming years.”

Researchers at Karolinska Institutet have developed a web-based platform that offers an unprecedented view of the human body at the cellular level. The aim is to create an invaluable resource for researchers worldwide to increase knowledge about human health and disease. The study is published in Genome Biology.
Simultaneous measurement of numerous biomolecular variables, known as multi-omics, enables deep and comprehensive profiling of human biology. The new Single Cell Atlas (SCA) is based on analyses of thousands of human tissue samples from 125 different adult and fetal tissues. The researchers combined eight cutting-edge omics technologies, including single-cell RNA sequencing, whole-genome sequencing, and spatial transcriptomics to map and localise genes expressed in the tissue.
The platform provides unique insights into individual cell properties and their interactions within tissues. The extensive collection of data is freely accessible through the platform’s website.
“The Single Cell Atlas not only saves time and resources but also fosters a collaborative environment for scientists from diverse fields, paving the way for new discoveries and innovations,” says the study’s first author Lu Pan, researcher at the Institute of Environmental Medicine, Karolinska Institutet, Sweden.
Looking ahead, the team plans to refine the SCA by introducing more detailed analyses and annual updates. These enhancements will fill gaps in tissue representation and expand the sample size, allowing for more precise research.
“The creation of the SCA marks a significant step forward in biomedical research,” says the study’s last author Xuexin Li, researcher at the Department of Physiology and Pharmacology (previously at the Department of Medical Biochemistry and Biophysics), Karolinska Institutet. “Our goal is to continually enrich the atlas, making it an invaluable resource for understanding human health and disease.”
The research was done in collaboration with China Medical University and several other international collaboration partners in The Single Cell Atlas Consortium. The study was financed by Karolinska Institutet and the KI Network Medicine Global Alliance (KI NMA). Coauthor Volker Lauschke is CEO and shareholder of HepaPredict AB, co-founder and shareholder of PersoMedix AB, and discloses consultancy work for Enginzyme AB. The other authors declare that they have no competing interests.

Diagnosing rare Mendelian disorders is a labor-intensive task, even for experienced geneticists. Investigators at Baylor College of Medicine are trying to make the process more efficient using artificial intelligence. The team developed a machine learning system called AI-MARRVEL (AIM) to help prioritize potentially causative variants for Mendelian disorders. The study is published today in NEJM AI.
Researchers from the Baylor Genetics clinical diagnostic laboratory noted that AIM’s module can contribute to predictions independent of clinical knowledge of the gene of interest, helping to advance the discovery of novel disease mechanisms. “The diagnostic rate for rare genetic disorders is only about 30%, and on average, it is six years from the time of symptom onset to diagnosis. There is an urgent need for new approaches to enhance the speed and accuracy of diagnosis,” said co-corresponding author Dr. Pengfei Liu, associate professor of molecular and human genetics and associate clinical director at Baylor Genetics.
AIM is trained using a public database of known variants and genetic analysis called Model organism Aggregated Resources for Rare Variant ExpLoration (MARRVEL) previously developed by the Baylor team. The MARRVEL database includes more than 3.5 million variants from thousands of diagnosed cases. Researchers provide AIM with patients’ exome sequence data and symptoms, and AIM provides a ranking of the most likely gene candidates causing the rare disease.
Researchers compared AIM’s results to other algorithms used in recent benchmark papers. They tested the models using three data cohorts with established diagnoses from Baylor Genetics, the National Institutes of Health-funded Undiagnosed Diseases Network (UDN) and the Deciphering Developmental Disorders (DDD) project. AIM consistently ranked diagnosed genes as the No. 1 candidate in twice as many cases than all other benchmark methods using these real-world data sets.
“We trained AIM to mimic the way humans make decisions, and the machine can do it much faster, more efficiently and at a lower cost. This method has effectively doubled the rate of accurate diagnosis,” said co-corresponding author Dr. Zhandong Liu, associate professor of pediatrics — neurology at Baylor and investigator at the Jan and Dan Duncan Neurological Research Institute (NRI) at Texas Children’s Hospital.
AIM also offers new hope for rare disease cases that have remained unsolved for years. Hundreds of novel disease-causing variants that may be key to solving these cold cases are reported every year; however, determining which cases warrant reanalysis is challenging because of the high volume of cases. The researchers tested AIM’s clinical exome reanalysis on a dataset of UDN and DDD cases and found that it was able to correctly identify 57% of diagnosable cases.
“We can make the reanalysis process much more efficient by using AIM to identify a high-confidence set of potentially solvable cases and pushing those cases for manual review,” Zhandong Liu said. “We anticipate that this tool can recover an unprecedented number of cases that were not previously thought to be diagnosable.”
Researchers also tested AIM’s potential for discovery of novel gene candidates that have not been linked to a disease. AIM correctly predicted two newly reported disease genes as top candidates in two UDN cases.

“AIM is a major step forward in using AI to diagnose rare diseases. It narrows the differential genetic diagnoses down to a few genes and has the potential to guide the discovery of previously unknown disorders,” said co-corresponding author Dr. Hugo Bellen, Distinguished Service Professor in molecular and human genetics at Baylor and chair in neurogenetics at the Duncan NRI.
“When combined with the deep expertise of our certified clinical lab directors, highly curated datasets and scalable automated technology, we are seeing the impact of augmented intelligence to provide comprehensive genetic insights at scale, even for the most vulnerable patient populations and complex conditions,” said senior author Dr. Fan Xia, associate professor of molecular and human genetics at Baylor and vice president of clinical genomics at Baylor Genetics. “By applying real-world training data from a Baylor Genetics cohort without any inclusion criteria, AIM has shown superior accuracy. Baylor Genetics is aiming to develop the next generation of diagnostic intelligence and bring this to clinical practice.”
Other authors of this work include Dongxue Mao, Chaozhong Liu, Linhua Wang, Rami AI-Ouran, Cole Deisseroth, Sasidhar Pasupuleti, Seon Young Kim, Lucian Li, Jill A.Rosenfeld, Linyan Meng, Lindsay C. Burrage, Michael Wangler, Shinya Yamamoto, Michael Santana, Victor Perez, Priyank Shukla, Christine Eng, Brendan Lee and Bo Yuan. They are affiliated with one or more of the following institutions: Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Al Hussein Technical University, Baylor Genetics and the Human Genome Sequencing Center at Baylor.
This work was supported by the Chang Zuckerberg Initiative and the National Institute of Neurological Disorders and Stroke (3U2CNS132415).

Giving drugs at different times of day could significantly affect how they are metabolized in the liver, according to a new study from MIT.
Using tiny, engineered livers derived from cells from human donors, the researchers found that many genes involved in drug metabolism are under circadian control. These circadian variations affect how much of a drug is available and how effectively the body can break it down. For example, they found that enzymes that break down Tylenol and other drugs are more abundant at certain times of day.
Overall, the researchers identified more than 300 liver genes that follow a circadian clock, including many involved in drug metabolism, as well as other functions such as inflammation. Analyzing these rhythms could help researchers develop better dosing schedules for existing drugs.
“One of the earliest applications for this method could be fine-tuning drug regimens of already approved drugs to maximize their efficacy and minimize their toxicity,” says Sangeeta Bhatia, the John and Dorothy Wilson Professor of Health Sciences and Technology and of Electrical Engineering and Computer Science at MIT, and a member of MIT’s Koch Institute for Integrative Cancer Research and the Institute for Medical Engineering and Science (IMES).
The study also revealed that the liver is more susceptible to infections such as malaria at certain points in the circadian cycle, when fewer inflammatory proteins are being produced.
Bhatia is the senior author of the new study, which appears today in Science Advances. The paper’s lead author is Sandra March, a research scientist in IMES.
Metabolic cycles
It is estimated that about 50 percent of human genes follow a circadian cycle, and many of these genes are active in the liver. However, exploring how circadian cycles affect liver function has been difficult because many of these genes are not identical in mice and humans, so mouse models can’t be used to study them.

Bhatia’s lab has previously developed a way to grow miniaturized livers using liver cells called hepatocytes, from human donors. In this study, she and her colleagues set out to investigate whether these engineered livers have their own circadian clocks.
Working with Charles Rice’s group at Rockefeller University, they identified culture conditions that support the circadian expression of a clock gene called Bmal1. This gene, which regulates the cyclic expression of a wide range of genes, allowed the liver cells to develop synchronized circadian oscillations. Then, the researchers measured gene expression in these cells every three hours for 48 hours, enabling them to identify more than 300 genes that were expressed in waves.
Most of these genes clustered in two groups — about 70 percent of the genes peaked together, while the remaining 30 percent were at their lowest point when the others peaked. These included genes involved in a variety of functions, including drug metabolism, glucose and lipid metabolism, and several immune processes.
Once the engineered livers established these circadian cycles, the researchers could use them to explore how circadian cycles affect liver function. First, they set out to study how time of day would affect drug metabolism, looking at two different drugs — acetaminophen (Tylenol) and atorvastatin, a drug used to treat high cholesterol.
When Tylenol is broken down in the liver, a small fraction of the drug is converted into a toxic byproduct known as NAPQI. The researchers found that the amount of NAPQI produced can vary by up to 50 percent, depending on what time of day the drug is administered. They also found that atorvastatin generates higher toxicity at certain times of day.
Both of these drugs are metabolized in part by an enzyme called CYP3A4, which has a circadian cycle. CYP3A4 is involved in processing about 50 percent of all drugs, so the researchers now plan to test more of those drugs using their liver models.

“In this set of drugs, it will be helpful to identify the time of the day to administer the drug to reach the highest effectiveness of the drug and minimize the adverse effects,” March says.
The MIT researchers are now working with collaborators to analyze a cancer drug they suspect may be affected by circadian cycles, and they hope to investigate whether this may also be true of drugs used in pain management.
Susceptibility to infection
Many of the liver genes that show circadian behavior are involved in immune responses such as inflammation, so the researchers wondered if this variation might influence susceptibility to infection. To answer that question, they exposed the engineered livers to Plasmodium falciparum, a parasite that causes malaria, at different points in the circadian cycle.
These studies revealed that the livers were more likely to become infected after exposure at different times of day. This is due to variations in the expression of genes called interferon-stimulated genes, which help to suppress infections.
“The inflammatory signals are much stronger at certain times of days than others,” Bhatia says. “This means that a virus like hepatitis or parasite like the one that causes malaria might be better at taking hold in your liver at certain times of the day.”
The researchers believe this cyclical variation may occur because the liver dampens its response to pathogens following meals, when it is typically exposed to an influx of microorganisms that might trigger inflammation even if they are not actually harmful.
Bhatia’s lab is now taking advantage of these cycles to study infections that are usually difficult to establish in engineered livers, including malaria infections caused by parasites other than Plasmodium falciparum.
“This is quite important for the field, because just by setting up the system and choosing the right time of infection, we can increase the infection rate of our culture by 25 percent, enabling drug screens that were otherwise impractical,” March says.
The research was funded by the MIT International Science and Technology Initiatives MIT-France program, the Koch Institute Support (core) Grant from the U.S. National Cancer Institute, the National Institute of Health and Medical Research of France, and the French National Research Agency.

Chemokines are signalling proteins that orchestrate the interaction of immune cells against pathogens and tumours. To understand this complex network, various techniques have been developed to identify chemokine-producing cells. However, it has not yet been possible to determine which cells react to these chemokines. Researchers at the University Hospital Bonn (UKB) and the University of Bonn have developed a new class of genetically modified mice that enables the simultaneous identification of chemokine producers and sensors. Using the chemokine Ccl3 as a “proof of principle,” they discovered that its function in the immune defence against viruses is different than had been previously assumed. Their results have now been published in the Journal of Experimental Medicine.
Our immune response to infections is critically controlled by chemokines. In order to understand how these signalling proteins coordinate immune cells, researchers from Bonn took a closer look at the chemokine Ccl3. Using a novel technology known as Ccl3-EASER mice, they investigated its role in coordinating the immune response to cytomegalovirus (CMV) infection, which can lead to severe diseases in immunocompromised individuals. “Until now, it was thought that certain macrophages, cells which colonize all organs as immune guardians, produce Ccl3 to attract antiviral immune cells,” says co-senior author Prof. Dr. Christian Kurts, Director of the Institute of Molecular Medicine and Experimental Immunology (IMMEI) at the UKB. He is also a member of the Transdisciplinary Research Area 3 (TRA 3) “Life & Health” and the Cluster of Excellence Immunosensation2 at the University of Bonn.
NK cells are both chemokine producers and sensors
“However, we actually found that the natural killer cells — NK cells for short — are the most important Ccl3 producers during CMV infection,” says co-senior author Prof. Dr. Natalio Garbi, research group leader from IMMEI at the UKB. He is also a member of the Cluster of Excellence Immunosensation2 at the University of Bonn. NK cells are white blood cells that can directly destroy virus-infected body cells. The scientists found that NK cells are in a permanent alarm mode to be ready for rapid Ccl3 production. As soon as a viral infection occurs, the body releases type I interferon as an alarm signal. This triggers the NK cells to rapidly produce the chemokine Ccl3. “However, NK cells are not only the cellular source, i.e. the producers of Ccl3, but also the main sensors for the chemokine in this context,” says co-senior author Prof. Dr. Niels A. Lemmermann, research group leader from the Institute of Virology at the UKB and member of the Cluster of Excellence Immunosensation2 at the University of Bonn. This means that Ccl3 acts as an auto/paracrine signal through which NK cells communicate directly with each other and coordinate their antiviral response.
“The experimental strategy used here is completely new. It can also be used for messenger substances other than Ccl3, which are released during various infections, diverse forms of inflammation or cancers,” explains Dr. Maria Belen Rodrigo, first author and scientist at the IMMEI of the UKB. With this work, the Bonn researchers have succeeded in gaining a better understanding of the complex choreography of immune cells in the defence against viruses.

Warning letters from Medicare can safely cut prescribing of a powerful but risky antipsychotic, according to a new study at Columbia University Mailman School of Public Health. Researchers used Medicare data to study the effects of the letters on hundreds of thousands of older adults with dementia. They found a significant and lasting reduction in prescribing but no signs of adverse effects on patient health. The findings are published in JAMA Network Open.
“Our study shows that low-cost letter interventions can safely reduce antipsychotic prescribing to patients with dementia,” said Adam Sacarny, PhD, assistant professor of Health Policy and Management at Columbia Mailman School. The work was conducted with researchers at the London School of Economics, Harvard T.H. Chan School of Public Health and Johns Hopkins University.
The researchers evaluated a large trial in which Medicare sent warning letters to high prescribers of quetiapine, the most popular antipsychotic in the U.S. Antipsychotics are frequently prescribed to people with dementia, but can cause numerous harms in this group. Researchers therefore studied the hundreds of thousands of older adults with dementia who were treated by the prescribers in the trial. Most previous studies on reducing prescribing in dementia care consisted of small trials or observational analyses, with limited evidence from large-scale randomized studies.
The results were striking. “People with dementia living in nursing homes and in the community were prescribed less and we did not detect negative health impacts for these groups,” said Michelle Harnisch, research student at the London School of Economics and first author of the study.
The findings are important because antipsychotics, such as quetiapine, are often used in dementia care to address behavioral symptoms. About 1 in 7 nursing home residents receives an antipsychotic every quarter. However, the drugs have a number of well-known risks. These include weight gain, cognitive decline, falls, and death. In turn, physician specialty societies, government regulators, and policymakers have aimed to reduce prescribing of these medications to people with dementia.
To test whether the warning letters reduced prescribing safely, the researchers used administrative data from Medicare to link the 5,055 physicians in the original trial to the Medicare records of their patients with dementia. They ultimately analyzed 84,881 patients in nursing homes and 261,288 patients living in the community.
The intervention reduced quetiapine use among nursing home patients by 7 percent and community-dwelling patients by 15 percent. The researchers did not find adverse effects across numerous health outcomes, including cognitive function, behavioral symptoms, depression, or metabolic outcomes like diabetes. There were signs of improved mental health outcomes, and the risk of death for patients living in the community fell slightly.

This research follows up on the original evaluation of the warning letters. In that study, members of the same research team also showed that the letters reduced prescribing. However, they focused on a considerably smaller sample of patients and studied a limited set of health outcomes. In contrast, the new research evaluates a number of key health indicators for dementia care and substantially expands the patient sample with a focus on dementia.
“These results show that this intervention and others like it could be leveraged to make prescribing safer and improve dementia care” noted Sacarny. “Similar interventions could also be adapted to other contexts to promote high-quality care.”
Co-authors are Michael L. Barnett, Stephen Coussens, Kali S. Thomas, Mark Olfson, and Kiros Berhane.
The study was supported by the National Institute on Aging (R21-AG070942).