The phase 3b, double-blind, randomised, placebo-controlled trial enrolled 1,457 influenza-positive index patients and 2,681 household contacts across 15 countries from 2019 to 2024. The index patients, aged 5 to 64, were assigned to receive either baloxavir or a placebo within 48 hours of symptom onset. The primary endpoint was laboratory-confirmed influenza transmission to household contacts by day 5.

Key Findings:

• Treatment with baloxavir reduced the odds of untreated household members contracting the virus by 32%.
• Transmission resulting in symptomatic influenza was lower with baloxavir (5.8% vs. 7.6%), though the difference was not statistically significant (P=0.16).
• Baloxavir led to a faster reduction in viral titers, with a mean reduction of 2.22 log₁₀ TCID₅₀/mL by day 3 compared to 1.85 log₁₀ TCID₅₀/mL for placebo.
• Drug-resistant viruses emerged in 7.2% of baloxavir-treated index patients but were not detected in household contacts, suggesting limited transmission risk.
• No new safety concerns were identified, with adverse events reported in 4.6% of baloxavir-treated patients compared to 7.0% in the placebo group.

‘These results highlight baloxavir’s potential not only to treat influenza but also to reduce its spread within communities,’ said Professor Benjamin Cowling, co-author of the study and Helen and Francis Zimmern Professor in Population Health, Chair Professor of Epidemiology, and Head of the Division of Epidemiology and Biostatistics, School of Public Health, HKUMed. ‘This dual effect could transform how we manage seasonal influenza and prepare for future pandemics.’

The study underscores the complementary role of antiviral drugs alongside vaccination, particularly in unvaccinated populations or during pandemics when vaccines may not be immediately available.

Professor Seyoung Kim and Dr. Hyunjeong Kwak from the Departments of Materials Science & Engineering and Semiconductor Engineering at POSTECH, in collaboration with Dr. Oki Gunawan from the IBM T.J. Watson Research Center, have become the first to uncover the hidden operating mechanisms of Electrochemical Random-Access Memory (ECRAM), a promising next-generation technology for AI. This groundbreaking study has been published in the journal, Nature Communications.

As AI technologies advance, data processing demands have exponentially increased. Current computing systems, however, separate data storage (‘memory’) from data processing (‘processors’), resulting in significant time and energy consumption due to data transfers between these units. To address this issue, researchers developed the concept of ‘In-Memory Computing.’

‘In-Memory Computing’ enables calculations directly within memory, eliminating data movement and achieving faster, more efficient operations. ECRAM is a critical technology for implementing this concept. ECRAM devices store and process information using ionic movements, allowing for continuous analog-type data storage. However, understanding their complex structure and high-resistive oxide materials has remained challenging, significantly hindering commercialization.

To address this, the research team developed a multi-terminal structured ECRAM device using tungsten oxide and applied the ‘Parallel Dipole Line Hall System’, enabling observation of internal electron dynamics from ultra-low temperatures (-223°C, 50K) to room temperature (300K). They observed, for the first time, that oxygen vacancies inside the ECRAM create shallow donor states (~0.1 eV), effectively forming ‘shortcuts’ through which electrons move freely. Rather than simply increasing electron quantity, the ECRAM inherently creates an environment facilitating easier electron transport. Crucially, this mechanism remained stable even at extremely low temperatures, demonstrating the robustness and durability of the ECRAM device..

Prof. Seyoung Kim from POSTECH emphasized, “This research is significant as it experimentally clarified the switching mechanism of ECRAM across various temperatures. Commercializing this technology could lead to faster AI performance and extended battery life in devices such as smartphones, tablets, and laptops.”

This work was supported by K-CHIPS(Korea Collaborative & High-tech Initiative for Prospective Semiconductor Research)funded by the Ministry of Trade, Industry & Energy of Korea (MOTIE).

Notes:

1. ECRAM(Electrochemical Random-Access Memory): An electrochemical memory device whose channel conductivity varies according to the concentration of ions within the channel. This behavior allows for the expression of analog memory states. The device features a three-terminal structure consisting of a source, drain, and gate. By applying voltage to the gate, ion movement is controlled, and the channel conductivity is read through the source and drain.

2. Parallel Dipole Line Hall System, PDL Hall System: A Hall measurement system composed of two cylindrical dipole magnets. When one magnet is rotated, the other rotates automatically, enabling the generation of a strong, superimposed magnetic field. This configuration allows for enhanced sensitivity in observing internal electron behaviors.

They are the building blocks of life, serving as essential components in nearly all cellular processes. They facilitate communication between cells and ensure that biological systems function properly.

Put simply, life wouldn’t exist without proteins. That’s why researchers around the world are dedicated to understanding them.

Now, a new study from the University of Copenhagen highlights how protein research could revolutionize multiple areas within biology and medicine. The study, published in the journal Cell, was led by scientists at the University of Copenhagen’s Novo Nordisk Foundation Center for Protein Research.

“We hope our findings will help explore how drugs influence protein turnover and contribute to the development of better medicines. Our research could also reveal how protein stability changes with age and how we might promote healthy aging,” says Professor Jesper Velgaard Olsen.

“In short, we have developed a cutting-edge technology that allows us to analyse and quantify proteins in individual cells with unprecedented depth. We can now identify exactly which proteins are present and in what quantities.”

With this new approach, researchers can measure how individual cells produce and break down proteins — a process known as ‘protein turnover’. The technique, called SC-pSILAC, enables scientists to track both the abundance of proteins and the rate at which they are turned over in single cells. These insights could have significant implications for cancer research, drug development, and personalized medicine.

Mapping the impact of cancer treatments

Despite their fundamental importance, there is still much we don’t know about proteins — including how many exist in a human cell.

SC-pSILAC is a breakthrough since it can distinguish between dividing and non-dividing cells. A prime example is cancer cells, which divide rapidly and are typically targeted by chemotherapy.

However, some cancer cells do not divide, allowing them to evade chemotherapy. The new method helps identify these treatment-resistant cells, leading to better therapies.

“We can now observe that non-dividing cells remain metabolically active and continue to affect their surroundings — something previous methods couldn’t detect,” explains Olsen.

The researchers have also used this technique to examine how specific drugs impact protein turnover in individual cells, including the cancer medication bortezomib. Their findings uncovered specific proteins and previously unknown biological processes influenced by the treatment.

“This method represents a significant leap in protein research,” Olsen states.

“In my field, we have worked for years to analyze proteins within cells. Only recently has technological progress enabled us to do so at the single-cell level.”

Thanks to this innovation, scientists now have a far more detailed understanding of how proteins operate at the molecular level. The hope is that this knowledge will drive advancements in disease diagnostics and treatment strategies.

By studying light anomalies made by the newly found planet’s host star and combining their results with a larger sample from a KMTNet microlensing survey, the team found that super-Earths can exist as far from their host star as our gas giants are from the sun, said Andrew Gould, co-author of the study and professor emeritus of astronomy at The Ohio State University.

“Scientists knew there were more small planets than big planets, but in this study, we were able to show that within this overall pattern, there are excesses and deficits,” he said. “It’s very interesting.”

While it can be relatively easy to locate worlds that orbit close to their star, planets with wider paths can be difficult to detect. Still, researchers further estimated that for every three stars, there should be at least one super-Earth present with a Jupiter-like orbital period, suggesting these massive worlds are extremely prevalent across the universe, said Gould, whose early theoretical research helped develop the field of planetary microlensing.

The findings in this study were made via microlensing, an observational effect that occurs when the presence of mass warps the fabric of space-time to a detectable degree. When a foreground object, such as a star or planet, passes between an observer and a more distant star, light is curved from the source, causing an apparent increase in the object’s brightness that can last anywhere from a few hours to several months.

Astronomers can use these fluctuations, or bumps, in brightness to help locate alien worlds unlike our own. In this case, microlensing signals were used to locate OGLE-2016-BLG-0007, a super-Earth with a mass ratio roughly double that of Earth’s and an orbit wider than Saturn’s.

These observations allowed the team to divide exoplanets into two groups, one that consists of super-Earths and Neptune-like planets and the other comprising gas giants like Jupiter or Saturn. This discovery opens new doors for planetary system science: Having a better understanding of exoplanet distribution can reveal new insights about the processes by which they form and evolve.

The study, led by researchers in China, Korea and at Harvard University and the Smithsonian Institution in the United States, was recently published in the journal Science.

To explain their results, researchers also compared their findings to predictions made from theoretical simulations of planet formation. Their results showed that while exoplanets can be separated into groups by mass and makeup, the mechanisms that may produce them can vary.

“The dominant theory of gas-giant formation is through runaway gas accretion, but other people have said that it could be both accretion and gravitational instability,” said Gould. “We’re saying we can’t distinguish between those two yet.”

Doing so will likely require greater swaths of long-term data from specialized systems such as KMTNet and other microlensing instruments like it, said Richard Pogge, another co-author of the study and a professor of astronomy at Ohio State.

“Finding a microlensing star event is hard. Finding a microlensing star with a planet is hard-squared,” he said. “We have to look at hundreds of millions of stars to find even a hundred of these things.”

These alignments are so rare that only 237 out of the more than 5,000 exoplanets ever discovered have been identified using the microlensing method. Now, with the help of three powerful custom-built telescopes located in South Africa, Chile and Australia, the KMTNet system routinely allows scientists to scour the cosmos for these amazing events, said Pogge.

Most notably, it was scientists in Ohio State’s Imaging Sciences Laboratory who designed and built the Korean Microlensing Telescope Network Cameras (KMTCam) that the system relies on to identify exoplanets. And as technology continues to evolve, having dedicated, global collaborations like this one will turn visions of scientific theory into real discoveries, said Pogge.

“We’re like paleontologists reconstructing not only the history of the universe we live in but the processes that govern it,” he said. “So helping to bring both of those pieces together into one picture has been enormously satisfying.”

Other members of Ohio State’s ISL team include Bruce Atwood, Tom O’Brien, Mark Johnson, Mark Derwent, Chris Colarosa, Jerry Mason, Daniel Pappalardo and Skip Shaller. This work was supported by the National Science Foundation, Tsinghua University, the National Natural Science Foundation of China, the Harvard-Smithsonian Center for Astrophysics, the China Manned Space Project, Polish National Agency for Academic Exchange and the National Research Foundation of Korea.

Now preclinical research by City of Hope^®, one of the largest and most advanced cancer research and treatment organizations in the United States and a leading research center for diabetes and other life-threatening illnesses, has uncovered the cellular culprit behind age-related abdominal fat, providing new insights into why our midsections widen with middle age. Published today in Science, the findings suggest a novel target for future therapies to prevent belly flab and extend our healthy lifespans.

“People often lose muscle and gain body fat as they age — even when their body weight remains the same,” saidQiong (Annabel) Wang, Ph.D., the study’s co-corresponding author and an associate professor of molecular and cellular endocrinology at City of Hope’sArthur Riggs Diabetes & Metabolism Research Institute, one of the world’s foremost scientific organizations dedicated to investigating the biology and treatment of diabetes. “We discovered aging triggers the arrival of a new type of adult stem cell and enhances the body’s massive production of new fat cells, especially around the belly.”

In collaboration with the UCLA laboratory co-corresponding author Xia Yang, Ph.D., the scientists conducted a series of mouse experiments later validated on human cells. Wang and her colleagues focused on white adipose tissue (WAT), the fatty tissue responsible for age-related weight gain.

While it’s well-known that fat cells grow larger with age, the scientists suspected that WAT also expanded by producing new fat cells, meaning it may have an unlimited potential to grow.

To test their hypothesis, the researchers focused on adipocyte progenitor cells (APCs), a group of stem cells in WAT that evolve into fat cells.

The City of Hope team first transplanted APCs from young and older mice into a second group of young mice. The APCs from the older animals rapidly generated a colossal amount of fat cells.

When the team transplanted APCs from young mice into the older mice, however, the stem cells did not manufacture many new fat cells. The results confirmed that older APCs are equipped to independently make new fat cells, regardless of their host’s age.

Using single-cell RNA sequencing, the scientists next compared APC gene activity in young and older mice. While barely active in young mice, APCs woke up with a vengeance in middle-aged mice and began pumping out new fat cells.

“While most adult stem cells’ capacity to grow wanes with age, the opposite holds true with APCs — aging unlocks these cells’ power to evolve and spread,” said Adolfo Garcia-Ocana, Ph.D., the Ruth B. & Robert K. Lanman Endowed Chair in Gene Regulation & Drug Discovery Research and chair of the Department of Molecular & Cellular Endocrinology at City of Hope. “This is the first evidence that our bellies expand with age due to the APCs’ high output of new fat cells.”

Aging also transformed the APCs into a new type of stem cell called committed preadipocytes, age-specific (CP-As). Arising in middle age, CP-A cells actively churn out new fat cells, explaining why older mice gain more weight.

A signaling pathway called leukemia inhibitory factor receptor (LIFR) proved critical for promoting these CP-A cells to multiply and evolve into fat cells.

“We discovered that the body’s fat-making process is driven by LIFR. While young mice don’t require this signal to make fat, older mice do,” explained Wang. “Our research indicates that LIFR plays a crucial role in triggering CP-As to create new fat cells and expand belly fat in older mice.”

Using single-cell RNA sequencing on samples from people of various ages, Wang and her colleagues next studied APCs from human tissue in the lab. Again, the team also identified similar CP-A cells that had an increased number in middle-aged people’s tissue. Their discovery also illustrates that CP-As in humans have high capacity in creating new fat cells.

“Our findings highlight the importance of controlling new fat-cell formation to address age-related obesity,” said Wang. “Understanding the role of CP-As in metabolic disorders and how these cells emerge during aging could lead to new medical solutions for reducing belly fat and improving health and longevity.”

Future research will focus on tracking CP-A cells in animal models, observing CP-A cells in humans and developing new strategies that eliminate or block the cells to prevent age-related fat gain.

The study’s first authors are City of Hope’s Guan Wang, Ph.D., and UCLA’s Gaoyan Li, Ph.D.

Exposomics explores how the complex interplay of environmental factors — from pollutants in our water and food to social and psychological stressors — shapes our biology. By studying these combined exposures, researchers can uncover how they collectively influence health, from metabolism and heart function to brain health and disease risk.

The Perspectives article is led by the Banbury Exposomics Consortium — an interdisciplinary group of scientists who gathered at Cold Spring Harbor’s Banbury Center in 2023 to define the core principles of this rapidly evolving field. Gary Miller, PhD, a foremost expert in exposomics and faculty member at Columbia University Mailman School of Public Health, was the lead organizer of the Consortium.

Miller, Vice Dean of Research and Innovation and Professor of Environmental Health Sciences at Columbia Mailman School, co-leads the NIH-funded national coordinating center for exposomics, NEXUS. He also leads IndiPHARM, an ARPA-H-funded initiative using exposomics to predict drug interactions and enhance medication effectiveness.

Exposomics in Action

The young field is already proving its transformative potential. Researchers analyzing molecular evidence identified a specific industrial solvent as the culprit behind kidney disease clusters among factory workers. In another study, scientists merged satellite pollution mapping with residential location information to reveal how airborne particulates prematurely age the brain. Scientists analyzing thousands of circulating molecules pinpointed TMAO, a gut microbiome metabolite produced when eating red meat and dairy, as a previously overlooked major contributor to heart attack risk.

These discoveries are made possible by cutting-edge technologies and tools such as wearable sensors that track chemical exposures in real-time, satellite imagery that maps pollution down to city blocks, and ultra-sensitive mass spectrometers that detect compounds present at just one part per trillion.

A Wider Lens on Our Health

While genetics provides our biological blueprint, it explains only a fraction of chronic disease risk. The exposome captures everything that happens to us, from industrial chemicals to social stressors. Unlike traditional studies examining single exposures in isolation, exposomics integrates advanced tools to understand how environmental, social, and psychological factors collectively interact with our biology.

This approach synergizes powerfully with other “omics” sciences. When combined with genomics, proteomics, and metabolomics, exposomics creates the first complete picture of health determinants. The authors envision a future where all major disease studies incorporate exposome analysis as standard practice.

Systematically analyzing these complex interactions can improve drug development, uncover hidden drivers of disease, and address health disparities. The approach bridges precision medicine and population health.

The Way Forward

Miller and colleagues outline critical priorities for advancing exposomics. These include the development of more sensitive technologies, such as wearable or minimally invasive tools that measure an individual’s exposome; the creation of a human exposome reference to enable analysis and contextualization at the population scale; and the implementation of standardized protocols to enable AI-driven analysis of complex datasets. The field must also address ethical considerations around data privacy and the need for greater focus on the social determinants of health, the authors write.

Newly launched U.S. and European exposomics hubs now provide the infrastructure for worldwide collaboration, standardizing methods, harmonizing data, and training researchers in the cross-disciplinary skills needed to advance this field. These centers form the critical backbone for the future progress of exposomics.

“We’re now building the first systematic framework to measure how all exposures — from chemical to social — interact with biology across the lifespan. Our goal is to create actionable strategies for healthier lives,” says Miller.

Hand gestures such as gripping, pinching and grasping are driven by movements of muscles in our forearm. These movements generate small electrical signals that can be read by sensors on the skin, a technique called electromyography.

“Using sensors and machine learning, we can recognize gestures based on muscle activity,” said Jonathon Schofield, professor of mechanical and aerospace engineering at UC Davis and senior author on the paper.

EMG-based controls perform well in a lab setting and with limbs at rest. But there is a well-known problem of “position and load.” If you move your arm to a different position — say, shoulder height, or over your head — or grasp objects of different weights, the measurements change.

“In the real world, every time you move a limb and grasp something the measurement is going to change,” said graduate student Peyton Young, first author on the paper. “The neutral position (where the limb is held passively next to the body) is very different to moving around.”

Combining EMG and FMG

To address this, Young and Schofield experimented with a different type of measurement, alone and in combination with EMG. Force myography (FMG) measures how muscles in the arm bulge as they contract.

Young constructed a cuff that goes round the forearm and includes both EMG and FMG sensors. He used this device with a series of able-bodied volunteers in the lab who performed a series of arm gestures with while participants held different loads with different hand grasps. Data from the sensors was fed to a machine learning algorithm to classify the different movements into pinch, pick, fist and so on. The algorithm was trained on either EMG or FMG signals alone, or on a combination.

For each experiment, the algorithm was trained on some of the data and scored on its ability to accurately classify the rest.

“We train the classifier on data from the gestures, then score it on its ability to predict them,” Young said.

They found that position and loading did indeed affect the accuracy of classification of gestures. Overall, a combination of EMG and FMG gave over 97 percent classification accuracy, compared to 92 percent for FMG alone and 83 percent for EMG alone.

Young is now working on a combined FMG/EMG sensor and the team is working towards an experimental prosthetic limb that uses the technology.

The approach could have a wide range of applications for prosthetics and robotics as well as for virtual reality tools, Schofield said. The team benefits enormously from being able to collaborate with clinical prosthetics experts, surgeons and biologists across UC Davis, he said.

“We wouldn’t be able to do it without exposure to actual patients and clinicians,” Schofield said.

Additional authors on the paper are Kihun Hong, Eden Winslow, Giancarlo Sagastume, Marcus Battraw and Richard Whittle, all at UC Davis. Battraw is now on the faculty at California State University, Chico.