A new study recently published in Neurology, the medical journal of the American Academy of Neurology, suggests that physical and mental activities, such as doing chores around the home, exercising, and visiting family and friends, may help reduce the risk of dementia. The research examined how these activities, together with mental activities and the use of electronic devices, affected individuals with and without increased hereditary risk for dementia.
“Many studies have identified potential risk factors for dementia, but we wanted to know more about a wide variety of lifestyle habits and their potential role in the prevention of dementia,” said study author Huan Song, MD, Ph.D., of Sichuan University in Chengdu, China. “Our study found that exercise, household chores, and social visits were linked to a reduced risk of various types of dementia.”
The study involved 501,376 people from a UK database without dementia. The participants had an average age of 36.
The loss of life would be equivalent to six planes, each carrying 200 passengers, killing everyone on board, every year.
Reducing air pollution from road transport will save thousands of lives and improve the health.
In our published research we evaluated the costs and benefits of a rapid transition. In one scenario, Australia matches the pace of transition of world leaders such as Norway. The modeling estimates this would save around 24,000 lives by 2042. Over time, the resulting greenhouse emission reductions would almost equal Australia’s current total annual emissions from all sources.
We also calculated the total costs and benefits through to 2042. Australia would be about 148 billion Australian dollars better off overall with a rapid transition.
The National Institutes of Health will invest $130 million over four years, pending the availability of funds, to accelerate the widespread use of artificial intelligence (AI) by the biomedical and behavioral research communities. The NIH Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) program is assembling team members from diverse disciplines and backgrounds to generate tools, resources, and richly detailed data that are responsive to AI approaches. At the same time, the program will ensure its tools and data do not perpetuate inequities or ethical problems that may occur during data collection and analysis. Through extensive collaboration across projects, Bridge2AI researchers will create guidance and standards for the development of ethically sourced, state-of-the-art, AI-ready data sets that have the potential to help solve some of the most pressing challenges in human health — such as uncovering how genetic, behavioral, and environmental factors influence a person’s physical condition throughout their life.
“Generating high-quality ethically sourced data sets is crucial for enabling the use of next-generation AI technologies that transform how we do research,” said Lawrence A. Tabak, D.D.S., Ph.D., Performing the Duties of the Director of NIH. “The solutions to long-standing challenges in human health are at our fingertips, and now is the time to connect researchers and AI technologies to tackle our most difficult research questions and ultimately help improve human health.”
AI is both a field of science and a set of technologies that enable computers to mimic how humans sense, learn, reason, and take action. Although AI is already used in biomedical research and healthcare, its widespread adoption has been limited in part due to challenges of applying AI technologies to diverse data types. This is because routinely collected biomedical and behavioral data sets are often insufficient, meaning they lack important contextual information about the data type, collection conditions, or other parameters. Without this information, AI technologies cannot accurately analyze and interpret data. AI technologies may also inadvertently incorporate bias or inequities unless careful attention is paid to the social and ethical contexts in which the data is collected.
Numerous short RNA sequences that code for microproteins and peptides have been identified, providing new opportunities for the study of diseases and the development of drugs…
Cancer is one of the major global public health problems and is caused by abnormal cell proliferation. A plant immune protein has recently been found to enable widespread anti-tumor responses by alleviating micro-RNA
In recent years, engineers and computer scientists have created a wide range of technological tools that can enhance fitness training experiences, including smart watches, fitness trackers, sweat-resistant earphones or headphones, smart home gym equipment and smartphone applications. New state-of-the-art computational models, particularly deep learning algorithms, have the potential to improve these tools further, so that they can better meet the needs of individual users.
Researchers at University of Brescia in Italy have recently developed a computer vision system for a smart mirror that could improve the effectiveness of fitness training both in home and gym environments. This system, introduced in a paper published by the International Society of Biomechanics in Sports, is based on a deep learning algorithm trained to recognize human gestures in video recordings.
“Our commercial partner ABHorizon invented the concept of a product that can guide and teach you during your personal fitness training,” Bernardo Lanza, one of the researchers who carried out the study, told TechXplore. “This device can show you the best way to train based on your specific needs. To develop this device further, they asked us to investigate the viability of an integrated vision system for exercise evaluation.”
The study population comprised 6,245,282 older adults (age ≥65 years) who had medical encounters with healthcare organizations between 2/2/2020–5/30/2021 and had no prior diagnosis of Alzheimer’s disease. The population was divided into two cohorts: 1) COVID-19 cohort (n = 410,748)— contracted COVID-19 between 2/2/2020–5/30/2021; 2) non-COVID-19 cohort (n = 5,834,534)— had no documented COVID-19 but had medical encounters with healthcare organizations between 2/2/2020–5/30/2021. The status of Alzheimer’s disease and COVID-19 were based on the International Classification of Diseases (ICD-10) diagnosis codes and laboratory tests (details in the Supplementary Material).
We examined risks for new diagnosis of Alzheimer’s disease in COVID-19 and non-COVID-19 cohorts in all older adults, three age groups (65–74, 75–84, ≥85), and three racial/ethnic groups (Black, White, and Hispanic). Cohorts were propensity-score matched (1:1 using a nearest neighbor greedy matching) for demographics, adverse socioeconomical determinants of health including problems with education, occupational exposure, physical, social and psychosocial environment, and known risk factors for Alzheimer’s disease [13] (details in the Supplementary Material). Kaplan-Meier analysis was used to estimate the probability of new diagnosis of Alzheimer’s disease within 360 days after the COVID-19 diagnosis. Cox’s proportional hazards model was used to compare matched cohorts using hazard ratios and 95% confidence intervals. All statistical tests were conducted within the TriNetX Advanced Analytics Platform at significance set at p < 0.05 (2-sided).
A team of researchers from Stanford University has constructed the first synthetic microbiome model, built entirely from scratch and encompassing more than 100 different bacterial species. It’s hoped the achievement will revolutionize gut microbiome research by offering scientists a consistent working model for future experiments.
It’s also mind-bendingly complex. No two people share exactly the same gut microbiome composition. And while researchers frequently home in on ways particular bacteria influence metabolic mechanisms, it has been difficult to translate these findings into actual clinical therapies for humans.
Researchers have created a way for artificial neuronal networks to communicate with biological neuronal networks. The new system converts artificial electrical spiking signals to a visual pattern than is then used to entrain the real neurons via optogenetic stimulation of the network. This advance will be important for future neuroprosthetic devices that replace damages neurons with artificial neuronal circuitry.
A prosthesis is an artificial device that replaces an injured or missing part of the body. You can easily imagine a stereotypical pirate with a wooden leg or Luke Skywalker’s famous robotic hand. Less dramatically, think of old-school prosthetics like glasses and contact lenses that replace the natural lenses in our eyes. Now try to imagine a prosthesis that replaces part of a damaged brain. What could artificial brain matter be like? How would it even work?
Creating neuroprosthetic technology is the goal of an international team led by by the Ikerbasque Researcher Paolo Bonifazi from Biocruces Health Research Institute (Bilbao, Spain), and Timothée Levi from Institute of Industrial Science, The University of Tokyo and from IMS lab, University of Bordeaux. Although several types of artificial neurons have been developed, none have been truly practical for neuroprostheses. One of the biggest problems is that neurons in the brain communicate very precisely, but electrical output from the typical electrical neural network is unable to target specific neurons. To overcome this problem, the team converted the electrical signals to light. As Levi explains, “advances in optogenetic technology allowed us to precisely target neurons in a very small area of our biological neuronal network.”
This can help engineer microbiome-based therapies in the future.
Researchers at Standford University have built from scratch the most complex and well-defined synthetic microbiome that will help us better understand the connections between the microbiome and human health, a university press release said.
The microbiome is a community of microorganisms that are found to cohabit in a given environment. The human gut has its own set of microorganisms that are markedly different from those on the skin.
The microbial community of over 100 bacterial species could help scientists learn more about the connections between the microbiome and human health.
