Comment

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

Comment by Dr. Krishna Kumari Challa on February 28, 2025 at 10:43am

A brain that turned to glass: scientific explanation

A young man was lying in his bed when a viciously hot cloud of ash swept down from the erupting Mount Vesuvius and turned his brain to glass almost 2,000 years ago.

That is the theory Italian scientists proposed this week  to explain the strange case of the ancient Roman's brain, which they said is the only human tissue ever known to have naturally turned to glass.

This unique brain could rewrite the story of one of history's most famous natural disasters—and help protect people against this little-understood phenomenon during future volcanic eruptions, the scientists suggested.

When Mount Vesuvius—near the modern-day Italian city of Naples—erupted in 79 AD, the cities of Pompeii and Herculaneum were buried in a fast-moving blanket of rock and ash called a pyroclastic flow.

Thousands of bodies have been discovered at the sites effectively frozen in time, offering a glimpse into the daily life of ancient Rome.

In the 1960s, the charred remains of a man aged roughly 20 were found on a wooden bed in a Herculaneum building dedicated to worshiping the Roman Emperor Augustus.

Italian anthropologist Pier Paolo Petrone, a co-author of a new study, noticed something strange in 2018.

He saw that something was shimmery in the shattered skull! What was left of the man's brain had been transformed into fragments of shiny black glass!!

These "chips" are up to a centimeter wide, say the volcanologist Guido Giordano, the lead author of the new study in Scientific Reports.

When scientists studied the glass using an electron microscope, they discovered an "amazing, truly unexpected thing". 

Complex networks of neurons, axons and other identifiable parts of the man's brain and spinal cord were preserved in the glass, according to the study.

How this happened is something of a mystery.

Glass occurs rarely in nature because it requires extremely hot temperatures to cool very rapidly, leaving no time for crystallization. It is usually caused by meteorites, lightning or lava.

This is even more unlikely to happen to human tissues, because they are mostly made out of water.

The Roman's brain being preserved in glass is the "only such occurrence on Earth" ever documented for human or animal tissue, the study said.

The scientists determined that the brain must have been exposed to temperatures soaring above 510 degrees Celsius (950 Fahrenheit).

That is hotter than the pyroclastic flow that buried the city, which topped out at around 465C.

Then the brain needed to rapidly cool down—and all this had to happen before the flow arrived.

Part 1

Comment by Dr. Krishna Kumari Challa on February 28, 2025 at 9:51am

Brain creates 'summaries' while reading, unlike AI models that process full texts

Unlike artificial language models, which process long texts as a whole, the human brain creates a "summary" while reading, helping it understand what comes next.

In recent years, large language models (LLMs) like ChatGPT and Bard have revolutionized AI-driven text processing, enabling machines to generate text, translate languages, and analyze sentiment. These models are inspired by the human brain, but key differences remain.

A new study, published in Nature Communications, explores these differences by examining how the brain processes spoken texts.

The study analyzed fMRI brain scans of 219 participants while they listened to stories. Researchers compared the brain's activity to predictions made by existing LLMs. They found AI models accurately predicted brain activity for short texts (a few dozen words). However, for longer texts, AI models failed to predict brain activity accurately.

The reason? While both the human brain and LLMs process short texts in parallel (analyzing all words at once), the brain switches strategies for longer texts. Since the brain cannot process all words simultaneously, it stores a contextual summary—a kind of "knowledge reservoir"—which it uses to interpret upcoming words.

In contrast, AI models process all previously heard text at once, so they do not require this summarization mechanism. This fundamental difference explains why AI struggles to predict human brain activity when listening to long texts.

To test their theory, the researchers developed an improved AI model that mimics the brain's summarization process. Instead of processing the entire text at once, the model created dynamic summaries and used them to interpret future text. This significantly improved AI predictions of brain activity, supporting the idea that the human brain is constantly summarizing past information to make sense of new input.

This ability allows us to process vast amounts of information over time, whether in a lecture, a book, or a podcast. Further analysis mapped brain regions involved in both short-term and long-term text processing, highlighting the brain areas responsible for context accumulation, which enables us to understand ongoing narratives.

Refael Tikochinski et al, Incremental accumulation of linguistic context in artificial and biological neural networks, Nature Communications (2025). DOI: 10.1038/s41467-025-56162-9

Comment by Dr. Krishna Kumari Challa on February 28, 2025 at 9:29am

Robot Headbutting Woman Raises Safety Questions

Humanoid robots are supposed to be our loyal assistants, but we saw another side to them the other day.

Chinese robot manufacturer Unitree was demonstrating its latest H1 robots at a lantern festival in the city of Taishan, Guangdong province, when one walked up to the crowd barrier and seemed to lunge at an elderly woman, nearly headbutting her.

Experts are now demanding these things to reduce the danger:

Robots that looks sleek and can dance and flip are fun to watch, but how safe are the audiences?

Safe designs should consider everything from reducing cavities where fingers could get caught, to waterproofing internal components.

Protective barriers or exoskeletons should be added to further reduce unintended contact, while cushioning mechanisms could reduce the effect of an impact.

Robots should be designed to signal their intent through lights, sounds and gestures. For example, they should arguably make a noise when entering a room so as not to surprise anyone.

Operators should be trained well and the public should be educated appropriately.

Comment by Dr. Krishna Kumari Challa on February 28, 2025 at 8:56am

Experts call for ban on toxic chemicals in plastics

Researchers are calling for a substantial reduction in the number of chemicals used in plastics manufacturing, and a complete ban of chemicals known to be detrimental to both human health and the environment—in a new paper published by Cambridge University Press in Cambridge Prisms: Plastics.

Plastics pollution is a major threat to human well-being and planetary health. While plastics recycling is often presented as the answer to plastics pollution, the presence of toxic chemicals in plastics, which enter plastics at various stages of the manufacturing process intentionally and otherwise, means that this is not a viable solution. To address the plastics pollution crisis, the safety and sustainability of plastics manufacturing must be improved.

Policymakers need to make changes to global, regional, and national policies to reduce the toxicity present in the plastics life cycle and address chemicals at each stage of manufacturing.

The researchers identified five policy strategies to support a transition to safer, more sustainable plastics:

Improving reporting, transparency and traceability of chemicals in plastics throughout their full life cycle

Advocating for chemical simplification and group-based approaches to regulating hazardous chemicals

Implementing chemical monitoring, testing and quality control

Creating economic incentives that follow the waste hierarchy

Generating support for a just transition to protect people, including waste pickers, impacted throughout the plastics life cycle.

Plastics contain a vast range of chemicals, including monomers, polymers, processing agents, fillers, antioxidants, plasticizers, pigments, microbiocides and stabilizers—and plastics production has already reached levels that threaten the stability of the Earth's functions. Moreover, the amounts and types of chemicals in plastics products varies, and there are few requirements for transparency and reporting.

We need a compulsory, globally standardized mandate that ensures transparent reporting regarding the chemicals used in plastics, to facilitate a safer and more sustainable reuse, refill, repurpose and recycling market, say the experts.

 Bethanie Carney Almroth et al, Addressing the toxic chemicals problem in plastics recycling, Cambridge Prisms: Plastics (2025). DOI: 10.1017/plc.2025.1

• ‹ Previous
• 1
• …
• 84
• 85
• 86
• 87
• 88
• …
• 1111
• Next ›
• Page