Lifeboat Foundation

As humans age, their brain function can progressively decline and they become more vulnerable to developing neurodegenerative diseases, such as dementia. Dementia and other progressive neurological conditions can significantly impair their memory, thinking skills and daily functioning, significantly reducing their quality of life.

Many psychology and neurological studies have tried to identify biological markers and lifestyle factors that can contribute to the development of dementia. Yet the contribution of psychological characteristics (e.g., traits, emotional well-being and cognitive resilience) to a decline in mental functions remains poorly understood.

Researchers at University of Barcelona, University College London (UCL), Normandy University and other institutes across Europe recently set out to fill this gap in the literature, by trying to determine whether specific sets of psychological characteristics relate to brain health in middle and late adulthood. Their paper, published in Nature Mental Health, identified three key psychological profiles that were linked to different cognitive and mental health trajectories after middle-age.

It has gotten to the point now that I find myself trying to make sense of them all. If you were to read them, what would you learn about me and my beliefs? Are there any coherent themes and patterns within these papers? I think there are and this is my attempt to hunt them out. I’m sure this will seem self-indulgent to some of you. I can only apologise. It is a deliberately self-indulgent exercise, but hopefully the thematic organisation is of interest to people other than myself, and some of the arguments may be intriguing or pique your curiosity. I’m going to keep this overview updated.

Reading note: There is some overlap in content between the sections below since some papers belonged to more than one theme. Also, clicking on the titles of the papers will take you directly to an open access version of them.

Summary: A study analyzing data from over 2 million veterans found that GLP-1 receptor agonists, popular weight-loss drugs like Ozempic and Wegovy, provide significant neurological and behavioral health benefits, including reduced risks of addiction, Alzheimer’s, and dementia. However, they also pose risks for pancreatitis and kidney conditions, emphasizing the need for careful monitoring.

The drugs mimic hormones that curb appetite and aid weight loss but may act on brain regions tied to impulse control and inflammation, explaining their broad effects. Researchers stress that while the benefits are modest, they may be impactful for conditions with limited treatment options, highlighting both their potential and limitations.

Scientists at Caltech and Princeton University have discovered that bacterial cells growing in a solution of polymers, such as mucus, form long cables that buckle and twist on each other, building a kind of “living Jell-O.”

The finding could be particularly important to the study and treatment of diseases such as cystic fibrosis, in which the mucus that lines the lungs becomes more concentrated, often causing bacterial infections that take hold in that mucus to become life threatening. This discovery could also have implications in studies of polymer-secreting conglomerations of bacteria known as biofilms—the slippery goo on river rocks, for example—and in industrial applications where they can cause equipment malfunctions and health hazards.

The work is described in a paper published on January 17 in the journal Science Advances.

Existing computer systems have separate data processing and storage devices, making them inefficient for processing complex data like AI. A KAIST research team has developed a memristor-based integrated system similar to the way our brain processes information. It is now ready for application in various devices, including smart security cameras, allowing them to recognize suspicious activity immediately without having to rely on remote cloud servers, and medical devices with which it can help analyze health data in real time.

The joint research team of Professor Shinhyun Choi and Professor Young-Gyu Yoon of the School of Electrical Engineering has developed the next-generation neuromorphic semiconductor-based ultra-small computing chip that can learn and correct errors on its own. The research is published in the journal Nature Electronics.

What is special about this computing chip is that it can learn and correct errors that occur due to non-ideal characteristics that were difficult to solve in existing neuromorphic devices. For example, when processing a video stream, the chip learns to automatically separate a moving object from the background, and it becomes better at this task over time.

An international team of researchers has made significant progress in understanding how gene expression is regulated across the human genome. In a recent study, they conducted a comprehensive analysis of cis-regulatory elements (CREs)—DNA sequences that control gene transcription. This research provides valuable insights into how CREs drive cell-specific gene expression and how mutations in these regions can impact health and contribute to disease.

CREs, such as enhancers and promoters, play a critical role in determining when and where genes are activated or silenced. Although their importance is well known, analyzing their activity on a large scale has been a longstanding challenge.

“The human genome contains a myriad of CREs, and mutations in these regions are thought to play a major role in human diseases and evolution,” explained Dr. Fumitaka Inoue, one of the co-first authors of the study. “However, it has been very difficult to comprehensively quantify their activity across the genome.”

Leading A Government-Wide Response To Long COVID — Dr. Ian Simon, Ph.D. — Director, Office of Long COVID Research and Practice, Office of the Assistant Secretary for Health (OASH), U.S. Department of Health and Human Services (HHS)

Dr. Ian Simon, Ph.D. is the Director for the Office of Long COVID Research and Practice (https://www.hhs.gov/longcovid/index.html), in the Office of Science and Medicine, in the Office of the Assistant Secretary for Health at the U.S. Department of Health \& Human Services.

Continue reading “Dr. Ian Simon, PhD — Director, Office of Long COVID Research and Practice, U.S. HHS” »

How can computer models help medical professionals combat antibiotic resistance? This is what a recent study published in PLOS Biology hopes to address as a team of researchers from the University of Virginia (UVA) developed computer models that can be used to target specific genes in bacteria to combat antimicrobial resistant (AMR) bacteria. This study has the potential to help scientists, medical professionals, and the public better understand innovative methods that can be used to combat AMR with bacterial diseases constantly posing a risk to global human health.

For the study, the researchers used computer models to produce an assemblage of genome-scale metabolic network reconstructions (GENREs) diseases to identify key genes in stomach diseases that can be targeted with antibiotics to circumvent AMR in these bacterial diseases. The researchers validated their findings with laboratory experiments involving microbial samples and found that a specific gene was responsible for producing stomach diseases, thus strengthening the argument for using targeted antibiotics to combat AMR.

“Using our computer models we found that the bacteria living in the stomach had unique properties,” said Emma Glass, who is a PhD Candidate in Biomedical Engineering at UVA and lead author of the study. “These properties can be used to guide design of targeted antibiotics, which could hopefully one day slow the emergence of resistant infections.”

By the end of 2024, artificial intelligence (AI) and machine learning (ML) had established themselves as the main transformative forces behind recent technological advancements in healthcare. A report by Silicon Valley Bank states that in 2024, the amount of VC investment in health AI in the U.S. was expected to reach $11.1 billion, the highest number since 2021.

In my experience, the main driver behind the AI investment and adoption craze is the measurable value technology offers healthcare providers. A 2023 National Bureau of Economic Research study indicates that integrating AI can save the U.S. healthcare system up to $360 billion annually. A 2023 survey by the AMA shows that physicians see AI as a way to reduce the administrative burden of documentation (54%) and improve workflow efficiency (69%).

But do these positive changes reflect on the quality of care, and do patients benefit from AI and ML-powered solutions? In this article, I share my take on the transformative potential of AI and ML in the modern care delivery process.