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  • This sun-powered sponge pulls drinking water straight from the ocean

    This sun-powered sponge pulls drinking water straight from the ocean

    Most of Earth’s water is in the oceans and too salty to drink. Desalination plants can make seawater drinkable, but they require large amounts of energy. Now, researchers reporting in ACS Energy Letters have developed a sponge-like material with long, microscopic air pockets that uses sunlight and a simple plastic cover to turn saltwater into freshwater. A proof-of-concept test outdoors successfully produced potable water in natural sunlight in a step toward low-energy, sustainable desalination.

    This isn’t the first time scientists have created spongy materials that use sunlight as a sustainable energy source for cleaning or desalinating water. For example, a loofah-inspired hydrogel with polymers inside its pores was tested on chromium-contaminated water and, when heated by the sun, the hydrogel quickly released a collectible, clean water vapor through evaporation. But while hydrogels are squishy and liquid-filled, aerogels are more rigid, containing solid pores that can transport liquid water or water vapor. Aerogels have been tested as a means of desalination, but they are limited by their evaporation performance, which declines as the size of the material increases. So, Xi Shen and colleagues wanted to design a porous desalination aerogel that maintained its efficiency at different sizes.

    The researchers made a paste containing carbon nanotubes and cellulose nanofibers and then 3D-printed it onto a frozen surface, allowing each layer to solidify before the next was added. This process formed a sponge-like material with evenly distributed tiny vertical holes, each around 20 micrometers wide. They tested square pieces of the material, ranging in size from 0.4 inches wide (1 centimeter) to about 3 inches wide (8 centimeters), and found that the larger pieces released water through evaporation at rates as efficient as the smaller ones.

    In an outdoor test, the researchers placed the material in a cup containing seawater, and it was covered by a curved, transparent plastic cover. Sunlight heated the top of the spongy material, evaporating just the water, not the salt, into water vapor. The vapor collected on the plastic cover as liquid, moving the now clean water to the edges, where it dripped into a funnel and container below the cup. After 6 hours in natural sunlight, the system generated about 3 tablespoons of potable water.

    “Our aerogel allows full-capacity desalination at any size,” Shen says, “which provides a simple, scalable solution for energy-free desalination to produce clean water.”

    The authors acknowledge funding from the National Natural Science Foundation of China, the Research Grants Council of Hong Kong SAR, the Environment and Conservation Fund of Hong Kong SAR, and the Hong Kong Polytechnic University.

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  • This Model Beats Docs at Predicting Sudden Cardiac Arrest

    This Model Beats Docs at Predicting Sudden Cardiac Arrest

    An artificial intelligence (AI) model has performed dramatically better than doctors using the latest clinical guidelines to predict the risk for sudden cardiac arrest in people with hypertrophic cardiomyopathy.

    The model, called Multimodal AI for ventricular Arrhythmia Risk Stratification (MAARS), is described in a paper published online on July 2 in Nature Cardiovascular Research. It predicts patients’ risk by analyzing a variety of medical data and records such as echocardiogram and radiology reports, as well as all the information contained in contrast-enhanced MRI (CMR) images of the patient’s heart.

    Natalia Trayanova, PhD, director of the Alliance for Cardiovascular Diagnostic and Treatment Innovation at Johns Hopkins University in Baltimore, led the development of the model. She said that while hypertrophic cardiomyopathy is one of the most common inherited heart diseases, affecting 1 in every 200-500 individuals worldwide, and is a leading cause of sudden cardiac death in young people and athletes, an individual’s risk for cardiac arrest remains difficult to predict.

    Current clinical guidelines from the American Heart Association and American College of Cardiology, and those from the European Society of Cardiology, identify the patients who go on to experience cardiac arrest in about half of cases.

    “The clinical guidelines are extremely inaccurate, little better than throwing dice,” Trayanova, who is also the Murray B. Sachs Professor in the Department of Biomedical Engineering at Johns Hopkins, told Medscape Medical News.

    Compared to the guidelines, MAARS was nearly twice as sensitive, achieving 89% accuracy across all patients and 93% accuracy for those 40-60 years old, the group of people with hypertrophic cardiomyopathy most at risk for sudden cardiac death.

    Building a Model

    MAARS was trained on data from 553 patients in The Johns Hopkins Hospital, Baltimore, hypertrophic cardiomyopathy registry. The researchers then tested the algorithm on an independent external cohort of 286 patients from the Sanger Heart & Vascular Institute hypertrophic cardiomyopathy registry in Charlotte, North Carolina.

    The model uses all of the data available from these patients, drawing on electronic health records, ECG readings, reports from radiologists and imaging technicians, and raw data from CMR.

    “All these different channels are fed into this multimodal AI predictor, which fuses it together and comes up with the risk for these particular patients,” Trayanova said.

    The inclusion of CMR data is particularly important, she said, because the imaging test can identify areas of scarring on the heart that characterize hypertrophic cardiomyopathy. But clinicians have yet to be able to make much use of those images because linking the fairly random patterns of scar tissue to clinical outcomes has been a challenge.

    But that is just the sort of task that deep neural networks  are particularly well-suited to tackle. “They can recognize patterns in the data that humans miss, then analyze and combine them with the other inputs into a single prediction,” Trayanova said.

    Clinical Benefits

    Better predictions of the risk for serious adverse outcomes will help improve care, by ensuring people get the right treatments to reduce their risk, and avoid the ones that are unnecessary, Trayanova said  The best way to protect against sudden cardiac arrest is with an implantable defibrillator — but the procedure carries potential risks that are best avoided unless truly needed.

    “More accurate risk prediction means fewer patients might undergo unnecessary ICD implantation, which carries risks such as infections, device malfunction, and inappropriate shocks,” said Antonis Armoundas, PhD, from the Cardiovascular Research Center at Massachusetts General Hospital in Boston.

    The model could also help personalize treatment for patients with hypertrophic cardiomyopathy, Trayanova said. “It’s able to drill down into each patient and predict which parameters are the most important to help influence the management of the condition,” she said.

    Robert Avram, MD, MSc, a cardiologist at the Montreal Heart Institute, Montreal, Quebec, Canada, said the results are encouraging. “I’m especially interested in how a tool like this could streamline risk stratification and ultimately improve patient outcomes,” he said.

    But it is not yet ready for widespread use in the clinic. “Before it can be adopted in routine care, however, we’ll need rigorous external validation across diverse institutions, harmonized variable definitions, and unified extraction pipelines for each modality, along with clear regulatory and workflow-integration strategies,” Avram said.

    Armoundas said he would like to see the model tested on larger sample sizes, with greater diversity in healthcare settings, geographical regions, and demographics, as well as prospective, randomized studies and comparisons against other AI predictive models.

    “Further validation in larger cohorts and assessment over longer follow-up periods are necessary for its full clinical integration,” he said.

    Armoundas, Avram, and Trayanova reported having no relevant financial conflicts of interest.

    Brian Owens is a freelance journalist based in New Brunswick, Canada.

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    Tom Lee Park / SCAPE + Studio Gang

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