An innovative use of skin cells could provide a route for gay couples or women with fertility problems to have children that both partners are genetically related to
Category: biotech/medical – Page 16
Kirigami parachute suitable for humanitarian missions stabilizes quickly and doesn’t pitch
A team of engineers from Polytechnique Montréal report a new and unique parachute concept inspired by the Japanese art of kirigami today in Nature. This simple, robust and low-cost approach has a wide variety of potential applications ranging from humanitarian aid to space exploration.
Kirigami is a technique that modifies the mechanical properties of a sheet of material by making precise folds and cuts to it. Children use it to make snowflakes out of paper, and engineers have used it to create extensible structures, flexible medical devices and deployable spatial structures. However, kirigami techniques have never been applied to parachute production.
The Polytechnique Montréal research team has now changed all that.
Outdoor air exposure to industrial solvent trichloroethylene may raise risk of Parkinson’s disease
Long-term exposure to the industrial solvent trichloroethylene (TCE) outdoors may be linked to an increased risk of Parkinson’s disease, according to a large nationwide study published in Neurology.
TCE is a chemical used in metal degreasing, dry cleaning and other industrial applications. Although TCE has been banned for certain uses, it remains in use today as an industrial solvent and is a persistent environmental pollutant in air, water and soil across the United States. The study does not prove that TCE exposure causes Parkinson’s disease, it only shows an association.
“In this nationwide study of older adults, long-term exposure to trichloroethylene in outdoor air was associated with a small but measurable increase in Parkinson’s risk,” said study author Brittany Krzyzanowski, Ph.D., of Barrow Neurological Institute in Phoenix. “These findings add to a growing body of evidence that environmental exposures may contribute to Parkinson’s disease.”
Combination of quantum and classical computing supports early diagnosis of breast cancer
Quantum computing is still in its early stages of development, but researchers have extensively explored its potential uses. A recent study conducted at São Paulo State University (UNESP) in Brazil proposed a hybrid quantum-classical model to support breast cancer diagnosis from medical images.
The work was published as part of the 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), organized by the Institute of Electrical and Electronics Engineers (IEEE). In the publication, the authors describe a hybrid neural network that combines quantum and classical layers using an approach known as a quanvolutional neural network (QNN). They applied the model to mammography and ultrasound images to classify lesions as benign or malignant.
“What we wanted to bring to this work was a very basic architecture that used quantum computing but contained a minimum of quantum and classical devices,” says Yasmin Rodrigues, the first author of the study. The work is part of her scientific initiation project, supervised by João Paulo Papa, full professor in the Department of Computing at the Bauru campus of UNESP. Papa also co-authored the article.
Heat-rechargeable design powers nanoscale molecular machines
Though it might seem like science fiction, scientists are working to build nanoscale molecular machines that can be designed for myriad applications, such as “smart” medicines and materials. But like all machines, these tiny devices need a source of power, the way electronic appliances use electricity or living cells use ATP (adenosine triphosphate, the universal biological energy source).
Researchers in the laboratory of Lulu Qian, Caltech professor of bioengineering, are developing nanoscale machines made out of synthetic DNA, taking advantage of DNA’s unique chemical bonding properties to build circuits that can process signals much like miniature computers. Operating at billionth-of-a-meter scales, these molecular machines can be designed to form DNA robots that sort cargos or to function like a neural network that can learn to recognize handwritten numerical digits.
One major challenge, however, has remained: how to design and power them for multiple uses.
New AI enhances the view inside fusion energy systems
Imagine watching a favorite movie when suddenly the sound stops. The data representing the audio is missing. All that’s left are images. What if artificial intelligence (AI) could analyze each frame of the video and provide the audio automatically based on the pictures, reading lips and noting each time a foot hits the ground?
That’s the general concept behind a new AI that fills in missing data about plasma, the fuel of fusion, according to Azarakhsh Jalalvand of Princeton University. Jalalvand is the lead author on a paper about the AI, known as Diag2Diag, that was recently published in Nature Communications.
“We have found a way to take the data from a bunch of sensors in a system and generate a synthetic version of the data for a different kind of sensor in that system,” he said. The synthetic data aligns with real-world data and is more detailed than what an actual sensor could provide. This could increase the robustness of control while reducing the complexity and cost of future fusion systems. “Diag2Diag could also have applications in other systems such as spacecraft and robotic surgery by enhancing detail and recovering data from failing or degraded sensors, ensuring reliability in critical environments.”
Dark matter detector succeeds in performing measurements with nearly no radioactive interference
In their search for dark matter, scientists from the XENON Collaboration are using one of the world’s most sensitive dark matter detectors, XENONnT at the Gran Sasso Laboratory of the National Institute of Nuclear Physics INFN in Italy, to detect extremely rare particle interactions. These could provide clues about the nature of dark matter. The problem, however, is that tiny amounts of natural radioactivity generate background events that can mask these weak signals.
The XENONnT experiment has made a breakthrough by significantly reducing one of the most problematic contaminants— radon, a radioactive gas. For the first time, the research team has succeeded in reducing the detector’s radon-induced radioactivity to a level a billion times lower than the very low natural radioactivity of the human body.
The underlying technology, which the XENONnT consortium reports in the current issue of the Physical Review X, was developed by a team led by particle physicist Prof Christian Weinheimer from the University of Münster.
Most effective digital interventions to stop smoking identified
Smoking remains one of the most deleterious habits for human health, as it is known to increase the risk of several life-threatening diseases, including lung and throat cancers, heart disease and strokes. While most smokers are well aware of its associated health risks, ceasing this habit can be a very difficult process.
Moreover, conventional programs for smoking cessation, such as those based on psychotherapy or nicotine replacement therapy, are not financially or physically accessible for all individuals who wish to stop smoking. In recent years, behavioral scientists and psychologists have been working with engineers to create digital interventions that support people in their efforts to quit this unhealthy habit.
Researchers at Sichuan University in China have carried out a systematic review and meta-analysis of past research studies investigating the effectiveness of various digital interventions for smoking cessation. The results of their analyses, presented in a paper published in Nature Human Behavior, suggest that personalized and group-customized technology-based programs could be particularly beneficial for smokers who wish to quit, with middle-aged individuals responding better than younger populations.
