This week’s edition features AI-boosted ECGs that can successfully identify hypertrophic cardiomyopathy, a drug combo for the down-hearted, a link between pollution particulate and heart attacks, and more.
Publishing in the JACC, this team developed a convolutional neural network (CNN) that was validated using 12-lead electrocardiograms (ECGs) from 2,448 patients with verified diagnoses of hypertrophic cardiomyopathy (as well as more than 51,000 age- and sex-matched cohorts without hypertrophic cardiomyopathy). According to the results of this study, the area under curve (AUC) of the CNN in the validation dataset was 0.95 (95% CI, 0.94 to 0.97) at the optimal probability threshold of 11% for having hypertrophic cardiomyopathy. When the 11% probability threshhold was applied to the testing dataset, the AUC for the CNN was 0.96, indicating a strong ability to detect patients with hypertrophic cardiomyopathy. “The good performance in patients with a normal ECG is fascinating,” Peter Noseworthy. MD, a study author and cardiologist at the Mayo Clinic, said in a press release. “It’s interesting to see that even a normal ECG can look abnormal to a convolutional neural network. This supports the concept that these networks find patterns that are hiding in plain sight.”
This research team conducted a randomized clinical trial of 85 recently bereaved participants (73 for spouses and 12 for parents; 55 females and 30 males; average age, 66 years) to determine whether a beta-blocker (metropolol 25 mg) and aspirin (100 mg) were able to reduce cardiovascular risk markers and anxiety without interfering with or affecting bereavement intensity. Participants were assessed within two weeks of bereavement and randomized to six weeks of treatment with either placebo or daily treatment with the metropolol/aspirin combination therapy. “Our finding on the potentially protective benefit of this treatment is also a good reminder for clinicians to consider the well-being of the bereaved,” said co-author Tom Buckley, of the University of Sydney Susan Wakil School of Nursing and Midwifery, in a press release.
This study focused on a patient population in Augsburg, Germany. The research team, investigating the relationship between different particulate measurements (such as particle number [PNC], particle length [PLC], and surface area [PSC]) and myocardial infarction (MI) on an hourly timescale. collected pollution data from background monitoring sites, as will as hourly nonfatal MI cases from a registry in Augsburg, for a ten-year period between 2005 and 2015, conducting a time-stratified case-crossover analysis to tease out any association between hourly particle metrics and cases of MI. They also conducted a separate analysis looking at independent effects of certain metrics (including different sizes and types) of the particles. “This study confirms something that has long been suspected—air pollution’s tiny particles can play a role in serious heart disease,” first author Kai Chen, PhD, assistant professor at Yale School of Public Health, said in a press release. “This is particularly true within the first few hours of exposure. Elevated levels of UFP are a serious public health concern.”
Publishing in Acta Biomaterialia, this paper described how the research team was able to cultivate human cells to get extracellular matrix deposits high in collagen. The team then cut these extracellular matrix sheets to form a “yarn” from them, which can then be sewn or braided or knitted into any number of forms. The upshot, one of the authors explained, is that the matrix sheets can be used to repair or replace blood vessels. “By combining this truly “bio” material with a textile-based assembly, this original tissue engineering approach is highly versatile and can produce a variety of strong human textiles that can be readily integrated in the body,” co-author and Inserm researcher Nicolas L’Heureux, said in a press release.
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