Lifeboat Foundation

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.”

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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

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).

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.

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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.

Trillions of microbes live inside our guts. Perhaps one of the most significant discoveries in medical science over recent decades has been how deeply these microbes influence our general health. From affecting how well drugs we consume work, to modulating our immune systems, the gut microbiome plays a powerful role in all aspects of our health.

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.

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Lassa fever is like ebola and there is an outbreak in Nigeria. It is transmissible through inhalation.

The death of a patient in the UK suffering from Lassa fever has heightened concern around the illness after a third case was reported.

The UK Health Security Agency (UKHSA) said it was contacting individuals who had been in close contact with the infected patients after the death was confirmed last week.

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