Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: health – Page 158

From the same mind whose research propelled the notion that “sitting too much is not the same as exercising too little,” comes a groundbreaking discovery set to turn a sedentary lifestyle on its ear: The soleus muscle in the calf, though only 1% of your body weight, can do big things to improve the metabolic health in the rest of your body if activated correctly.

And Marc Hamilton, professor of Health and Human Performance at the University of Houston, has discovered such an approach for optimal activation—he’s pioneering the “soleus pushup” (SPU) which effectively elevates muscle metabolism for hours, even while one is sitting. The soleus, one of 600 muscles in the , is a posterior leg muscle that runs from just below the knee to the heel.

Published in the journal iScience, Hamilton’s research suggests the soleus pushup’s ability to sustain an elevated oxidative metabolism to improve the regulation of blood glucose is more effective than any popular methods currently touted as a solution including exercise, weight loss and intermittent fasting. Oxidative metabolism is the process by which oxygen is used to burn metabolites like blood glucose or fats, but it depends, in part, on the immediate energy needs of the muscle when it’s working.

Read more | >

Maximizing Benefits Of The Life Sciences & Health Tech For All Americans — Dr. Andrew Hebbeler, Ph.D., Principal Assistant Director for Health and Life Sciences, Office of Science and Technology Policy, The White House.

Dr. Andrew Hebbeler, Ph.D., is Principal Assistant Director for Health and Life Sciences, Office of Science and Technology Policy at The White House (https://www.whitehouse.gov/ostp/ostps-teams/health-and-life-sciences/), and has extensive foreign affairs, national security, global health, and science and technology (S&T) policy experience.

Most recently, Dr. Hebbeler was Senior Director and Lead Scientist for Global Biological Policy and Programs at the non-profit Nuclear Threat Initiative and previous to that served in leadership positions at the State Department’s offices of Science and Technology Cooperation (OES/STC), the Science and Technology Adviser to the Secretary of State (E/STAS), and Cooperative Threat Reduction (ISN/CTR).

Continue reading “Dr. Andrew Hebbeler, Ph.D. — Office of Science and Technology Policy (OSTP) — The White House” | >

Corporate Venturing For Integrated Digital Healthcare Solutions — Bill Taranto, President, Global Health Innovation Fund, Merck

Bill Taranto is President of the Global Health Innovation Fund at Merck (https://www.merckghifund.com/taranto.html) and founding partner since inception in 2010.

Merck Global Health Innovation Fund (Merck GHI) is a corporate venture capital group utilizing a healthcare ecosystem strategy, investing globally in platform companies with proven technologies or business models where Merck’s expertise can accelerate revenue growth and enhance value creation to ultimately develop integrated healthcare solutions.

Continue reading “Bill Taranto — President, GHI Fund, Merck — Corporate Venturing For Integrated Healthcare Solutions” | >

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.

Read more | >

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.

Read more | >

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.

Read more | >

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

Ribonucleic acid (RNA) is a polymeric molecule similar to DNA that is essential in various biological roles in coding, decoding, regulation and expression of genes. Both are nucleic acids, but unlike DNA, RNA is single-stranded. An RNA strand has a backbone made of alternating sugar (ribose) and phosphate groups. Attached to each sugar is one of four bases—adenine (A), uracil (U), cytosine ©, or guanine (G). Different types of RNA exist in the cell: messenger RNA (mRNA), ribosomal RNA (rRNA), and transfer RNA (tRNA).

Read more | >

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.”

Read more | >

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).

Read more | >