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Comment by Dr. Krishna Kumari Challa on March 4, 2025 at 12:12pm

Polymers used in everyday products can degrade into toxic chemicals, study finds

The scientific community has long believed that polymers—very large molecules—are too big to migrate out of products into people and therefore pose no health risks. As a result, polymers have largely evaded regulation. For example, polymers are exempt from the major toxics acts. However, a study published recently in Nature Sustainability demonstrates that polymers used as flame retardants can break down into smaller harmful chemicals.

The study suggests polymers can act as a trojan horse for toxic chemicals.

They are added to products as inert large molecules, but over time they can degrade, exposing us to their harmful breakdown products.

The researchers tested two polymeric brominated flame retardants (polyBFRs) that were developed as "non-toxic" alternatives to banned flame retardants. They found that both polyBFRs broke down into dozens of types of smaller molecules. Toxicity testing of these smaller molecules in zebrafish showed significant potential for causing mitochondrial dysfunction and developmental and cardiovascular harm.

The scientists went on to search for these polymer break-down products in the environment and, further raising alarm, detected them in soil, air, and dust. The levels were highest near electronic waste recycling facilities and lessened moving away from the facilities. These results confirm that the use of polyBFRs in electronics leads to the release of toxic breakdown products into the environment with potential for human and wildlife exposure and harm.

Widespread use of these polyBFRs in electronics may result in exposures when these products are manufactured, when they're in our homes, and when they're discarded or recycled, the researchers say.

Xiaotu Liu et al, Environmental impacts of polymeric flame retardant breakdown, Nature Sustainability (2025). DOI: 10.1038/s41893-025-01513-z

Comment by Dr. Krishna Kumari Challa on March 4, 2025 at 12:03pm

One therapy that has proven effective is a fecal microbiota transplant, which is designed to restore a healthy balance of bacteria in the gut. However, it's not without risks.

To a certain extent, a fecal transplant is almost like going to the pharmacist where they take a little bit of everything off the shelf and put it into one pill, assuming that something will probably help. But we don't know 100% what's in there.The research team set out to identify C. difficile's "friends" and "foes;" in other words, those that tend to either co-occur with C. difficile or those that may reduce the growth of C. difficile. They gathered information on the human microbiome from 12 previously published studies, which included microbiome sequencing data and clinical diagnoses of C. difficile colonization.

They then used machine learning to home in on the key features of microorganisms that were positively and negatively associated with C. difficile.

Thirty-seven strains of bacteria were found to be negatively correlated with C. difficile. In other words, when these microorganisms were present, there was no C. difficile infection. Another 25 bacteria were positively correlated with C. difficile, meaning that they were present alongside C. difficile infection.
In the lab, the researchers then combined bacteria that appeared to repress C. difficile and developed a synthetic version of a fecal transplant.

When tested in vitro and given orally to mice, the synthetic microbiome therapy significantly reduced growth of C. difficile, resisted infection and was as effective as a traditional human fecal transplant. In mice, it was also shown to protect against severe disease, delay relapse and decrease the severity of recurrent infections caused by antibiotic use.

Through experiments, the researchers determined that just one bacterial strain was critical for suppressing C. difficile. Alone, it was just as effective as a human fecal transplant in preventing infection in a mouse model.
If you have this Peptostreptococcus strain, you don't have C. difficile. It's a very potent suppressor and is actually better than all 37 strains combined.

The team's approach to microbiome science could be used to understand complex host-microbial interactions in other conditions like inflammatory bowel disease with the potential to develop novel therapies.

The goal is to develop the microbes as targeted drugs and therapies.

A designed synthetic microbiota provides insight to community function in Clostridioides difficile resistance, Cell Host & Microbe (2025). DOI: 10.1016/j.chom.2025.02.007. www.cell.com/cell-host-microbe … 1931-3128(25)00055-1

Part 2

Comment by Dr. Krishna Kumari Challa on March 4, 2025 at 11:59am

Synthetic microbiome therapy suppresses bacterial infection without antibiotics

A synthetic microbiome therapy, tested in mice, protects against severe symptoms of a gut infection that is notoriously difficult-to-treat and potentially life-threatening in humans, according to a team of researchers who developed the treatment for Clostridioides difficile, or C. difficile, a bacterium that can cause severe diarrhea, abdominal pain and colon inflammation.

C. difficile can overgrow when the balance of the gut microbiome—the trillions of organisms that keep your body healthy—is disrupted. The team said their findings could lead to the development of new probiotic strategies for humans to treat C. difficile infections as an alternative to antibiotics and conventional fecal microbiota transplants.

While it draws on the idea of human fecal transplants, a medical procedure where bacteria from a healthy donor's stool is transferred to a patient's gastrointestinal tract to restore balance to the microbiome, the new approach doesn't require any fecal matter.

Instead, this microbiome therapy uses fewer but more precise bacteria strains that have been linked to C. difficile suppression. It was as effective as human fecal transplants in mice against C. difficile infection and with fewer safety concerns.

The findings were published in the journal Cell Host & Microbe and the researchers also filed a provisional application to patent the technology described in the paper.

Typically, the organisms in the microbiome keep each other in check. While many people carry C. difficile in their gut, it usually doesn't cause a problem. However, antibiotics can tip the scales, creating an environment where C. difficile can flourish by knocking out good bacteria along with harmful ones. C. difficile accounts for 15% to 25% of antibiotic-associated diarrhea. Infection can often set in after a visit to the hospital or other health care setting.

Treating these infections is challenging. Antibiotics aren't effective against C. difficile because the bacteria are drug-resistant. Antibiotics also further disrupt the gut microbiome, creating a positive feedback loop that leads to recurrent infections.

Part 1

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

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