Lifeboat Foundation

Elucidating human contact networks could help predict and prevent the transmission of SARS-CoV-2 and future pandemic threats. A new study from Scripps Research scientists and collaborators points to which public health protocols worked to mitigate the spread of COVID-19—and which ones didn’t.

In the study, published online in Cell on December 14, 2023, the Scripps Research-led team of scientists investigated the efficacy of different mandates—including stay-at-home measures, social distancing and travel restrictions —at preventing local and regional transmission during different phases of the COVID-19 pandemic.

They found that local transmission was driven by the amount of travel between locations, not by how geographically nearby they were. The study also revealed that the partial closure of the U.S.-Mexico border was ineffective at preventing cross-border transmission of the virus. These findings, in combination with ongoing genomic surveillance, could help guide public health policy to prevent future pandemics and mitigate the new “endemic” phase of COVID-19.

In a novel study, researchers from the Icahn School of Medicine at Mount Sinai have introduced LoGoFunc, an advanced computational tool that predicts pathogenic gain and loss-of-function variants across the genome.

Unlike current methods that predominantly focus on loss of function, LoGoFunc distinguishes among different types of harmful mutations, offering potentially valuable insights into diverse disease outcomes. The findings are described in Genome Medicine.

Genetic variations can alter protein function, with some mutations boosting activity or introducing new functions (gain of function), while others diminish or eliminate function (loss of function). These changes can have significant implications for human health and the treatment of disease.

A study published in the journal Physical Review Letters by researchers in Japan solves a long-standing problem in quantum physics by redefining the uncertainty principle.

Werner Heisenberg’s uncertainty principle is a key and surprising feature of quantum mechanics, and he can thank his hay fever for it. Miserable in Berlin in the summer of 1925, the young German physicist vacationed on the remote, rocky island of Helgoland, in the North Sea off the northern German coast. His allergies improved, and he was able to continue his work trying to understand the intricacies of Bohr’s model of the atom, developing tables of internal atomic properties, such as energy, position and momentum.

Continue reading “Extending the uncertainty principle by using an unbounded operator” »

This Perspective reviews large-scale genomics and longitudinal phenomics efforts and the insights they can provide into wellness. The authors describe their vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

Twenty-four years ago, Ray Kurzweil predicted computers would reach human-level intelligence by 2029. This was met with great concern and criticism. In the past six months technology experts have come around to agree with him. According to Kurzweil, over the next two decades, AI is going to change what it means to be human. We are going to invent new means of expression that will soar past human language, art, and science of today. All of the concepts that we rely on to give meaning to our lives, including death itself, will be transformed.\
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Speakers:\
Ray Kurzweil\
Inventor, Futurist \& Best-selling author of ‘The Singularity is Near’\
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Reinhard Scholl\
Deputy Director, Telecommunication Standardization Bureau\
International Telecommunication Union (ITU)\
Co-founder and Managing Director, AI for Good\
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The AI for Good Global Summit is the leading action-oriented United Nations platform promoting AI to advance health, climate, gender, inclusive prosperity, sustainable infrastructure, and other global development priorities. AI for Good is organized by the International Telecommunication Union (ITU) – the UN specialized agency for information and communication technology – in partnership with 40 UN sister agencies and co-convened with the government of Switzerland.\
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Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.\
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What is AI for Good?\
We have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets.\
More than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals.\
AI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland.\
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Disclaimer:\
The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.

A stroke happens when blood flow is lost to part of the brain. Your brain cells cannot get the oxygen and nutrients they need from blood, and they start to die in a few minutes. This can cause lasting brain damage, long-term disability, or even death.

A stroke can occur when an obstruction such as a blood clot travels from another part of the body and lodges inside an artery in the brain.

When an arterial wall becomes damaged, various types of emboli, or obstructions, can form. Emboli can be made up of various substances such as platelets, elements in the blood that help it clot, blood clots that form elsewhere and pass to the damaged area, cholesterol, or a combination of things.

Continue reading “Stroke — Health Video: MedlinePlus Medical Encyclopedia” »

Worldwide, more than one billion people are obese. Obesity is a risk factor for cardiovascular disease, diabetes, and some cancers. But permanently losing weight isn’t easy: complex interactions between body systems such as gut physiology, hormones, and the brain are known to work against it. One method for weight loss is intermittent energy restriction (IER), where days of relative fasting alternate with days of eating normally.

“Here we show that an IER diet changes the human brain-gut-microbiome axis. The observed changes in the gut microbiome and in the activity in addition-related brain regions during and after weight loss are highly dynamic and coupled over time,” said last author Dr. Qiang Zeng, a researcher at the Health Management Institute of the PLA General Hospital in Beijing. The study has been published in Frontiers in Cellular and Infection Microbiology.

The authors used metagenomics on stool samples, blood measurements, and functional magnetic resonance imaging (fMRI) to study changes in the composition of the gut microbiome, physiological parameters and serum composition, and brain activity in 25 obese Chinese women and men on an IER diet. Participants were on average 27 years old, with a BMI between 28 and 45.

Conditioning the lungs with interferon-gamma, a natural immune system protein (cytokine) best known for fighting bacterial infections, appears to be a strong antiviral for SARS-CoV-2, according to National Institutes of Health scientists and colleagues. Their new study, published in Nature Communications, shows in two different mouse models that when a bacterial infection triggers the release of interferon-gamma in the lungs, those animals subsequently are protected from infection by SARS-CoV-2, the virus that causes COVID-19. The investigators further report that using recombinant interferon-gamma in the nose of study mice at the time of viral exposure substantially reduces SARS-CoV-2 infection and COVID disease.

The lead project scientists suggest testing interferon-gamma further, alone and in combination with other treatments, to limit early SARS-CoV-2 infection in people. They also hypothesize that people with prior bacterial infections that naturally release interferon-gamma in their lungs may be less susceptible to COVID-19.

NIH’s National Institute of Allergy and Infectious Diseases (NIAID) led the project with collaborators at Malaghan Institute of Medical Research in New Zealand.

Researchers at UT Southwestern Medical Center have developed a novel artificial intelligence (AI) model that analyzes the spatial arrangement of cells in tissue samples. This innovative approach, detailed in Nature Communications, has accurately predicted outcomes for cancer patients, marking a significant advancement in utilizing AI for cancer prognosis and personalized treatment strategies.

“Cell spatial organization is like a complex jigsaw puzzle where each cell serves as a unique piece, fitting together meticulously to form a cohesive tissue or organ structure. This research showcases the remarkable ability of AI to grasp these intricate spatial relationships among cells within tissues, extracting subtle information previously beyond human comprehension while predicting patient outcomes,” said study leader Guanghua Xiao, Ph.D., Professor in the Peter O’Donnell Jr. School of Public Health, Biomedical Engineering, and the Lyda Hill Department of Bioinformatics at UT Southwestern. Dr. Xiao is a member of the Harold C. Simmons Comprehensive Cancer Center at UTSW.

Tissue samples are routinely collected from patients and placed on slides for interpretation by pathologists, who analyze them to make diagnoses. However, Dr. Xiao explained, this process is time-consuming, and interpretations can vary among pathologists. In addition, the human brain can miss subtle features present in pathology images that might provide important clues to a patient’s condition.