Are brain-training games any better at improving your ability to think, remember and focus than regular computer games? Possibly not, if the latest study is anything to go by. Joseph Kable at the University of Pennsylvania and his colleagues have tested the popular Luminosity brain-training program from Lumos Labs in San Francisco, California, against other computer games and found no evidence that it is any better at improving your thinking skills. Brain-training is a booming market. It's based on the premise that our brains change in response to learning challenges.
Background: Stress is a contributing factor to many major health problems in the United States, such as heart disease, depression, and autoimmune diseases. Relaxation is often recommended in mental health treatment as a frontline strategy to reduce stress, thereby improving health conditions. Objective: The objective of our study was to understand how people express their feelings of stress and relaxation through Twitter messages. Methods: We first performed a qualitative content analysis of 1326 and 781 tweets containing the keywords "stress" and "relax", respectively. We then investigated the use of machine learning algorithms to automatically classify tweets as stress versus non stress and relaxation versus non relaxation. Finally, we applied these classifiers to sample datasets drawn from 4 cities with the goal of evaluating the extent of any correlation between our automatic classification of tweets and results from public stress surveys. Results: Content analysis showed that the most frequent topic of stress tweets was education, followed by work and social relationships. The most frequent topic of relaxation tweets was rest and vacation, followed by nature and water. When we applied the classifiers to the cities dataset, the proportion of stress tweets in New York and San Diego was substantially higher than that in Los Angeles and San Francisco. Conclusions: This content analysis and infodemiology study revealed that Twitter, when used in conjunction with natural language processing techniques, is a useful data source for understanding stress and stress management strategies, and can potentially supplement infrequently collected survey-based stress data.
While it may be beneficial for some, stepping on a scale on a daily basis isn't for everyone. Anyone who's ever tried to lose weight knows how difficult reaching your weight loss goals can be. But a new study found those who stepped on a scale on a daily basis were more likely to shed weight than those who did not. The study -- which was conducted by researchers from the University of Pittsburgh School of Nursing and the University of California, San Francisco School of Medicine and is slated to be presented at the American Heart Association's Scientific Sessions 2018 in Chicago later this week -- analyzed 1,042 adults with an average age of 47 over 12 months. During this time, participants -- using scales that were either WiFi or Bluetooth enabled, according to Health magazine -- weighed themselves "at home as they normally would, without interventions, guidance or weight-loss incentives from researchers," according to the American Heart Association's news release regarding the study.
Acute intracranial hemorrhage (ICH), sometimes referred to as a "brain bleed," shares symptoms with several other neurological conditions. Today, emergency departments rely on CT scans to detect this life-threatening condition--and even the most experienced radiologists can sometimes miss the subtle signs of the condition on such lower resolution images. Now, researchers from the University of California, San Francisco and the University of California, Berkeley have demonstrated that a deep learning artificial intelligence (AI) algorithm can provide "expert-level" detection of brain hemorrhage in a new study published in the Proceedings of the National Academy of Sciences--not only performing at the same standard as expert radiologists but finding tiny brain bleeds that those experts overlooked. The researchers used a single-stage, end-to-end, fully convolutional deep learning neural network in order to help identify what are usually very small abnormalities that must been detected on an image known for poor soft tissue contrast and low signal-to-noise issues. They trained the algorithm on a data set of over 4,000 CT exams where ICH abnormalities were manually highlighted at the pixel level.
Scientists at the University of California San Francisco have developed a new method to minimize the likelihood that a person's body will reject stem cells during a transplant. Using the CRISPR gene editing tools, the scientists managed to create stem cells that are effectively invisible to the body's immune system. Because transplanted stem cells are viewed by the human body as an unknown and potentially dangerous foreign organism, the immune system often kicks into high gear when the cells are detected. That can lead to transplant rejection. While there are some drugs that help to suppress the immune system's response, it also leaves the patient exposed to other diseases that can complicate matters.