Comment

Comment by Dr. Krishna Kumari Challa on March 4, 2025 at 11:13am

Melting Antarctic ice sheets are slowing Earth's strongest ocean current, research reveals

Melting ice sheets are slowing the Antarctic Circumpolar Current (ACC), the world's strongest ocean current, researchers have found. This melting has implications for global climate indicators, including sea level rise, ocean warming and viability of marine ecosystems.

Researchers have shown the current slowing by around 20% by 2050 in a high carbon emissions scenario. The work is published in the journal Environmental Research Letters.

This influx of fresh water into the Southern Ocean is expected to change the properties, such as density (salinity), of the ocean and its circulation patterns.

The ocean is extremely complex and finely balanced. If this current 'engine' breaks down, there could be severe consequences, including more climate variability, with greater extremes in certain regions, and accelerated global warming due to a reduction in the ocean's capacity to act as a carbon sink.

 Taimoor Sohail et al, Decline of antarctic circumpolar current due to polar ocean freshening, Environmental Research Letters (2025). DOI: 10.1088/1748-9326/adb31c

Comment by Dr. Krishna Kumari Challa on March 4, 2025 at 10:57am

The researchers' discovery was unexpected when they were investigating the function of a gene called Plvap in certain liver cells in mice. The team knew from previous studies that humans born without this gene have problems with their lipid metabolism, a connection the research team set out to investigate.

It turned out that the Plvap gene enables the body's metabolic shift from burning sugar to fat when fasting. And when Plvap is turned off—as the researchers did in their laboratory mice—the liver does not recognize that the body is fasting and continues burning sugar.
In other words, the research team has found an entirely new way in which the liver's metabolism is regulated, which may have medical applications.
Beyond the intriguing ability of Plvap knockout to "trick" the liver into thinking it is not fasting, the researchers made several other important observations in their study:

The signal that triggers metabolic changes during fasting comes from the liver's stellate cells rather than hepatocytes, the liver's most abundant cells responsible for carrying out metabolic processes. This suggests that stellate cells play a previously unknown role in controlling liver metabolism by directing other cell types, introducing a new mode of cell-to-cell communication.
Although fat was redirected to the muscles instead of the liver, the mice showed no negative effects. In fact, they experienced improved insulin sensitivity and lower blood sugar levels.
This discovery could have far-reaching implications—not just for obesity treatments, but also for improving our understanding of how fat and sugar are processed in metabolic diseases. In the long run, it may open new avenues for treating conditions like type 2 diabetes and steatotic liver disease.

 Hepatic stellate cells regulate liver fatty acid utilization via plasmalemma vesicle2 associated protein, Cell Metabolism (2025). DOI: 10.1016/j.cmet.2025.01.022. www.cell.com/cell-metabolism/f … 1550-4131(25)00022-1

Part 2

Comment by Dr. Krishna Kumari Challa on March 4, 2025 at 10:53am

How to trick the body's metabolism: Study reveals new path for weight-loss and diabetes treatments

Many people who have tried to lose weight by cutting calories are familiar with this frustrating reality: At some point, the body stops shedding pounds. It senses the reduced calorie intake and responds by slowing down metabolism, causing it to burn fewer calories than before the diet.

This happens because the body perceives a potential starvation threat and adapts by conserving energy while still carrying out essential functions. 

Now, a new study has identified a possible way to maintain calorie burning even when consuming fewer calories. The work appears in Cell Metabolism.

This discovery could be particularly important for patients using weight-loss or diabetes medicines like Wegovy and Ozempic. Many people taking these medications find that their weight loss plateaus after losing about 20–25% of their body weight. This stall is likely due to the body's natural response.

If we could develop a medication that helps maintain fat or sugar burning at its original high level alongside weight-loss treatments, people could continue losing weight beyond the usual plateau.

However,  the findings are currently based on mouse models, meaning human trials are still a long way off, and potential treatments even further down the line.

Part 1

Comment by Dr. Krishna Kumari Challa on March 4, 2025 at 7:53am

The researchers found that T cell receptor sequences provided the most relevant information about lupus and type 1 diabetes while B cell receptor sequences were most informative in identifying HIV or SARS-CoV-2 infection or recent influenza vaccination. In every case, however, combining the T and B cell results increased the algorithm's ability to accurately categorize people by their disease state regardless of sex, age or race.
Although the researchers developed Mal-ID on just six immunological states, they envision the algorithm could quickly be adapted to identify immunological signatures specific to many other diseases and conditions. They are particularly interested in autoimmune diseases like lupus, which can be difficult to diagnose and treat effectively.
Mal-ID may also help researchers identify new therapeutic targets for many conditions.

Maxim E. Zaslavsky et al, Disease diagnostics using machine learning of B cell and T cell receptor sequences, Science (2025). DOI: 10.1126/science.adp2407

Part 3

Comment by Dr. Krishna Kumari Challa on March 4, 2025 at 7:51am

The sequences of these immune receptors are highly variable.

This variability helps the immune system detect virtually anything, but also makes it harder for us to interpret what these immune cells are targeting.

In this study, the researchers could decode the immune system's record of these disease encounters by interpreting this highly variable information with some new machine learning techniques.

B cells and T cells represent two separate arms of the immune system, but the way they make the proteins that recognize infectious agents or cells that need to be eliminated is similar. In short, specific segments of DNA in the cells' genomes are randomly mixed and matched—sometimes with an additional dash of extra mutations to spice things up—to create coding regions that, when the protein structures are assembled, can generate trillions of unique antibodies (in the case of B cells) or cell surface receptors (in the case of T cells).

The randomness of this process means that these antibodies or T cell receptors aren't tailored to recognize any specific molecules on the surface of invaders. But their dizzying diversity ensures that at least a few will bind to almost any foreign structure. (Auto-immunity, or an attack by the immune system on the body's own tissues, is typically—but not always—avoided by a conditioning process T and B cells go through early in development that eliminates problem cells.)

The act of binding stimulates the cell to make many more of itself to mount a full-scale attack; the subsequent increased prevalence of cells with receptors that match similar three-dimensional structures provides a biological fingerprint of what diseases or conditions the immune system has been targeting.

To test their theory, the researchers assembled a dataset of more than 16 million B cell receptor sequences and more than 25 million T cell receptor sequences from 593 people with one of six different immune states: healthy controls, people infected with SARS-CoV-2 (the virus that causes COVID-19) or with HIV, people who had recently received an influenza vaccine, and people with lupus or type 1 diabetes (both autoimmune diseases). Zaslavsky and his colleagues then used their machine-learning approach to look for commonalities between people with the same condition.

The researchers compared the frequencies of segment usage, the amino acid sequences of the resulting proteins and the way the model represented the 'language' of the receptors, among other characteristics.

Part 2

Comment by Dr. Krishna Kumari Challa on March 4, 2025 at 7:47am

Immune 'fingerprints' aid diagnosis of complex diseases

Your immune system harbors a lifetime's worth of information about threats it's encountered. Often the perpetrators are viruses and bacteria you've conquered; others are undercover agents like vaccines given to trigger protective immune responses or even red herrings in the form of healthy tissue caught in immunological crossfire.

Now researchers  have devised a way to mine this rich internal database to diagnose diseases as diverse as diabetes COVID-19 responses to influenza vaccines. Although they envision the approach as a way to screen for multiple diseases simultaneously, the machine-learning-based technique can also be optimized to detect complex, difficult-to-diagnose autoimmune diseases such as lupus.

In a study of nearly 600 people—some healthy, others with infections including COVID-19 or autoimmune diseases including lupus and type 1 diabetes—the algorithm the researchers developed, called Mal-ID for machine learning for immunological diagnosis, was remarkably successful in identifying who had what based only on their B and T cell receptor sequence and structures.

Combining information from the two main arms of the immune system gives us a more complete picture of the immune system's response to disease and the pathways to autoimmunity and vaccine response.

In addition to aiding the diagnosis of tricky diseases, Mal-ID could track responses to cancer immunotherapies and subcategorize disease states in ways that could help guide clinical decision making, the researchers think.

In a follow-the-dots approach, the scientists used machine learning techniques based on large language models those that underlie ChatGPT to home in on the threat-recognizing receptors on immune cells called T cells and the business ends of antibodies (also called receptors) made by another type of immune cell called B cells.

In the case of this study, the scientists applied a large language model trained on proteins, fed the model millions of sequences from B and T cell receptors, and used it to lump together receptors that share key characteristics—as determined by the model—that might suggest similar binding preferences.

Doing so might give a glimpse into what triggers caused a person's immune system to mobilize—churning out an army of T cells, B cells and other immune cells equipped to attack real and perceived threats.

Part 1

Comment by Dr. Krishna Kumari Challa on March 3, 2025 at 7:32am

Animals That Keep Their Whole Ecosystem Together

Comment by Dr. Krishna Kumari Challa on March 3, 2025 at 7:18am

 The Youngest Person Ever Diagnosed With Alzheimer's

In 2023, neurologists at a memory clinic in China diagnosed a 19-year-old with what they believed to be Alzheimer's disease, making him the youngest person ever to be diagnosed with the condition in the world.

The male teenager began experiencing memory decline around age 17, and the cognitive losses only worsened over the years.

Imaging of the patient's brain showed shrinkage in the hippocampus, which is involved in memory, and his cerebrospinal fluid hinted at common markers of this most common form of dementia.

Alzheimer's disease (AD) is often thought of as an old person's ailment, and yet early-onset cases, which include patients under the age of 65, account for up to 10 percent of all diagnoses.

Almost all patients under 30 years of age can have their Alzheimer's explained by pathological gene mutations, putting them into the category of familial Alzheimer's disease (FAD). The younger a person is when they receive a diagnosis, the more likely it is the result of a faulty gene they've inherited.

Yet researchers at the Capital Medical University in Beijing couldn't find any of the usual mutations responsible for the early onset of memory loss, nor any suspect genes when they performed a genome-wide search.

Cases like the one in China pose something of a mystery. None of the 19-year-old's family had a history of Alzheimer's or dementia, making it hard to categorize as FAD, yet the teenager had no other diseases, infections, or head trauma that could explain his sudden cognitive decline either.

Two years before being referred to the memory clinic, the teenage patient began struggling to focus in class. Reading also became difficult and his short-term memory declined. Oftentimes, he couldn't remember events from the day before, and he was always misplacing his belongings.

Ultimately, the cognitive decline became so bad, the young man was unable to finish high school, although he could still live independently.

A year after being referred to the memory clinic, he showed losses in immediate recall, short-delay recall after three minutes, and long-delay recall after 30 minutes.

The patient's full-scale memory score was 82 percent lower than that of peers his own age, while his immediate memory score was 87 percent lower.

The case study, published in February 2023, just goes to show that Alzheimer's doesn't follow a single pathway, and is much more complex than we thought, emerging via numerous avenues with varying effects.

https://content.iospress.com/articles/journal-of-alzheimers-disease...

Comment by Dr. Krishna Kumari Challa on March 3, 2025 at 7:00am

AI too faces cognitive decline like living beings with brains

It's barely been two years since OpenAI's ChatGPT was released for public use.

Today, the famous large language model (LLM) is just one of several leading programs that appear convincingly human in their responses to basic queries. That uncanny resemblance may extend further than intended, with researchers from Israel now finding LLMs suffer a form of cognitive impairment similar to decline in humans, one that is more severe among earlier models.

The team applied a battery of cognitive assessments to publicly available 'chatbots': versions 4 and 4o of ChatGPT, two versions of Alphabet's Gemini, and version 3.5 of Anthropic's Claude.

Were the LLMs truly intelligent, the results would be concerning.

In their published paper, neurologists and  data scientists  describe a level of "cognitive decline that seems comparable to neurodegenerative processes in the human brain."

ChaptGPT 4o scored the highest on the assessment, with just 26 out of a possible 30 points, indicating mild cognitive impairment. This was followed by 25 points for ChatGPT 4 and Claude, and a mere 16 for Gemini – a score that would be suggestive of severe impairment in humans.

Digging into the results, all of the models performed poorly on visuospatial/executive function measures.

Similarly, a lack of empathy shown by all models in a feature of the Boston Diagnostic Aphasia Examination could be interpreted as a sign of frontotemporal dementia.

As might be expected, earlier versions of LLMs scored lower on the tests than more recent models, indicating each new generation of AI has found ways to overcome the cognitive shortcomings of its predecessors.

The authors acknowledge LLMs aren't human brains, making it impossible to 'diagnose' the models tested with any form of dementia. Yet the tests also challenge assumptions that we're on the verge of an AI revolution in clinical medicine, a field that often relies on interpreting complex visual scenes.

As the pace of innovation in artificial intelligence continues to accelerate, it's possible, even likely we'll see the first LLM score top marks on cognitive assessment tasks in future decades.

Until then, the advice of even the most advanced chatbots ought to be treated with a healthy dose of skepticism.

https://www.bmj.com/content/387/bmj-2024-081948

Comment by Dr. Krishna Kumari Challa on February 28, 2025 at 10:46am

The "only possible scenario" was that an ash cloud emitted by Vesuvius delivered an initial hot blast before quickly dissipating, the study said.

This theory is supported by a thin layer of ash that settled in the city shortly before it was smothered.

This would mean the people of Herculaneum were actually killed by the ash cloud—not the pyroclastic flow as had long been thought.
Giordano hoped the research would lead to more awareness about the threat posed by these hot ash clouds, which remain "very poorly studied" because they leave little trace behind.
And some of the 215 people killed during the 2018 eruption of Guatemala's Fuego volcano were also victims of this phenomenon.
There is a window of survivability" for these hot blasts, he emphasized, adding that fitting houses near volcanoes to withstand high heat could help.

But why did the man with the glass brain uniquely suffer this fate?

Unlike Pompeii, Herculaneum had some time to respond to the eruption. All the other bodies discovered there were clearly trying to flee into the Mediterranean Sea.
However the man, who is thought to have been the guardian of the Collegium building, stayed in bed in the middle of town, so was the first hit.

The answer to the question 'why' is blowing in the wind!

Guido Giordano, Unique formation of organic glass from a human brain in the Vesuvius eruption of 79 CE, Scientific Reports (2025). DOI: 10.1038/s41598-025-88894-5. www.nature.com/articles/s41598-025-88894-5

• ‹ Previous
• 1
• …
• 53
• 54
• 55
• 56
• 57
• …
• 1080
• Next ›
• Page