University of Washington (UW) researchers have developed the thermal earring, a new wearable they say outperforms smartwatches in measuring a wearer’s skin temperature during periods of rest. The earring has also shown promise for monitoring signs of stress, eating, exercise, and ovulation, according to a statement. UW’s prototype smart earring—which is not currently commercially available—is about the size and weight of a small paper clip and boasts a 28-day battery life. A magnetic clip attaches one temperature sensor to a wearer’s ear, while a second sensor dangles below it and estimates room temperature. The second sensor can include fashion designs made of resin or gemstones, without negatively affecting its accuracy. The earring contains a Bluetooth chip, a battery, two temperature sensors, and an antenna. After reading and sending a person’s temperature, it goes into deep sleep to save power. “I wear a smartwatch to track my personal health, but I’ve found that a lot of people think smartwatches are unfashionable or bulky and uncomfortable,” said Qiuyue (Shirley) Xue, a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering, in a statement. “I also like to wear earrings, so we started thinking about what unique things we can get from the earlobe,” added Xue, who coauthored a study on the thermal earring published earlier this year in a technology journal. “We found that sensing the skin temperature on the lobe, instead of a hand or wrist, was much more accurate. It also gave us the option to have part of part of the sensor dangle to separate ambient room temperature from skin temperature.” Xue’s statement said that “current wearables like Apple Watch and Fitbit have temperature sensors, but they provide only an average temperature for the day, and their temperature readings from wrists and hands are too noisy to track ovulation.” Eventually, Xue said, she hoped to develop a full jewelry set for health monitoring. “The earrings would sense activity and health metrics such as temperature and heart rate,” she explained, “while a necklace might serve as an electrocardiogram monitor for more effective heart health data.”
Using a type of artificial intelligence known as deep learning, MIT researchers have discovered a class of compounds that can kill a drug-resistant bacterium that causes more than 10,000 deaths in the United States every year. In a study appearing today in Nature, the researchers showed that these compounds could kill methicillin-resistant Staphylococcus aureus (MRSA) grown in a lab dish and in two mouse models of MRSA infection. The compounds also show very low toxicity against human cells, making them particularly good drug candidates. A key innovation of the new study is that the researchers were also able to figure out what kinds of information the deep-learning model was using to make its antibiotic potency predictions. This knowledge could help researchers to design additional drugs that might work even better than the ones identified by the model. “The insight here was that we could see what was being learned by the models to make their predictions that certain molecules would make for good antibiotics. Our work provides a framework that is time-efficient, resource-efficient, and mechanistically insightful, from a chemical-structure standpoint, in ways that we haven’t had to date,” says James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. Felix Wong, a postdoc at IMES and the Broad Institute of MIT and Harvard, and Erica Zheng, a former Harvard Medical School graduate student who was advised by Collins, are the lead authors of the study, which is part of the Antibiotics-AI Project at MIT. The mission of this project, led by Collins, is to discover new classes of antibiotics against seven types of deadly bacteria, over seven years. MRSA, which infects more than 80,000 people in the United States every year, often causes skin infections or pneumonia. Severe cases can lead to sepsis, a potentially fatal bloodstream infection. Over the past several years, Collins and his colleagues in MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) have begun using deep learning to try to find new antibiotics. Their work has yielded potential drugs against Acinetobacter baumannii, a bacterium that is often found in hospitals, and many other drug-resistant bacteria. These compounds were identified using deep learning models that can learn to identify chemical structures that are associated with antimicrobial activity. These models then sift through millions of other compounds, generating predictions of which ones may have strong antimicrobial activity. Experiments revealed that the compounds appear to kill bacteria by disrupting their ability to maintain an electrochemical gradient across their cell membranes. This gradient is needed for many critical cell functions, including the ability to produce ATP (molecules that cells use to store energy). An antibiotic candidate that Collins’ lab discovered in 2020, halicin, appears to work by a similar mechanism but is specific to Gram-negative bacteria (bacteria with thin cell walls). MRSA is a Gram-positive bacterium, with thicker cell walls. “We have pretty strong evidence that this new structural class is active against Gram-positive pathogens by selectively dissipating the proton motive force in bacteria,” Wong says. “The molecules are attacking bacterial cell membranes selectively, in a way that does not incur substantial damage in human cell membranes. Our substantially augmented deep learning approach allowed us to predict this new structural class of antibiotics and enabled the finding that it is not toxic against human cells.” The researchers have shared their findings with Phare Bio, a nonprofit started by Collins and others as part of the Antibiotics-AI Project. The nonprofit now plans to do more detailed analysis of the chemical properties and potential clinical use of these compounds. Meanwhile, Collins’ lab is working on designing additional drug candidates based on the findings of the new study, as well as using the models to seek compounds that can kill other types of bacteria.
People are being maimed by unauthorized fat-dissolving injections meant to tighten up double chins and dissipate flab along the arms, thighs and stomach, the U.S. Food and Drug Administration warns. The shots are supposed to break down fat cells and reduce fat deposits in the areas around the injection sites. But adverse reactions from the unapproved injections are causing scarring, skin deformities, cysts, painful knots, and serious infections, the FDA said in an agency news release. The unapproved injections are being marketed under brand names like Aqualyx, Lipodissolve, Lipo Lab and Kabelline, the FDA said. Common ingredients in the injections include phosphatidylcholine (PPC) and sodium deoxycholate (DC). These ingredients have been used alone or together and are sometimes referred to as "PCDC injections." The FDA has received reports of consumers harmed by injections they received at clinics or med spas from attendants who might not have been properly licensed to give the shots. The agency also has heard from some consumers who bought the unapproved shots online and injected the drugs themselves. The FDA has approved only one injectable drug for dissolving fat, a prescription medication called Kybella, the agency said. The drug, which is deozycholic acid, is FDA-approved to treat double chins in adults. Deozycholic acid is a bile acid naturally produced by intestinal bacteria to help break down fats during digestion. The FDA warns that improper or unsafe injection practices can increase the risk of scarring, skin infections and serious complications. Safe and effective use of a fat-dissolving agent involves calculating the correct number and location of the injections, placing the needles properly, and administering the shots in a safe and sterile manner, the FDA said. Kybella's label notes that the shots should be administered only by a health care professional, the agency noted. People shouldn't buy fat-dissolving products from websites, much less attempt to inject them, the FDA added. Instead, they should consult their doctor about FDA-approved treatments like Kybella. Folks who have received these injections and are experiencing side effects should seek medical care. They also should report their case to the FDA's MedWatch program, which helps the agency track medication safety issues.