A hidden breathing problem may be behind chronic fatigue’s crushing exhaustion

 Chronic fatigue syndrome leaves many people completely drained of energy and struggling to think clearly, and their symptoms often worsen after mental or physical exertion -- a reaction known as post-exertional malaise. Researchers studying shortness of breath in people with chronic fatigue have now found that these patients are much more likely to experience dysfunctional breathing. This irregular breathing pattern may be linked to dysautonomia, a disorder involving abnormal nerve control of blood vessels and muscles. By focusing treatment on these breathing irregularities, scientists believe it may be possible to ease some of the debilitating symptoms.

"Nearly half of our chronic fatigue subjects had some disorder of breathing -- a totally unappreciated issue, probably involved in making symptoms worse," said Dr. Benjamin Natelson of the Icahn School of Medicine, senior author of the study published in Frontiers in Medicine. "Identifying these abnormalities will lead researchers to new strategies to treat them, with the ultimate goal of reducing symptoms."

Breathe easy

The study included 57 people diagnosed with chronic fatigue syndrome and 25 healthy individuals of similar age and activity level. All participants completed two days of cardiopulmonary exercise tests. During these sessions, the researchers monitored heart rate, blood pressure, oxygen uptake efficiency, blood oxygen saturation, and how much effort participants used to breathe. They also analyzed breathing rate and patterns to detect signs of hyperventilation and dysfunctional breathing.

Dysfunctional breathing is often seen in asthma patients, but it can develop for many different reasons. Typical features include frequent deep sighs, rapid breathing, forceful exhalation from the abdomen, or chest breathing without proper diaphragm use, which prevents the lungs from fully expanding. It can also involve a lack of coordination between chest and abdominal movements, meaning the muscles that support breathing are no longer working smoothly together.

"While we know the symptoms generated by hyperventilation, we remain unsure what symptoms may be worse with dysfunctional breathing," said Dr. Donna Mancini of the Icahn School of Medicine, first author of the study. "But we are sure patients can have dysfunctional breathing without being aware of it. Dysfunctional breathing can occur in a resting state."

Catching your breath

Results showed that people with chronic fatigue syndrome took in roughly the same amount of oxygen as the control group -- their peak VO2 max was similar. However, 71% of the chronic fatigue group showed breathing abnormalities, such as hyperventilation, dysfunctional breathing, or both.

Almost half of the chronic fatigue participants breathed irregularly during the tests, compared to only four people in the control group. About one-third of the fatigue patients hyperventilated, while just one person in the control group did. Nine patients had both hyperventilation and dysfunctional breathing, a combination not seen in any of the controls.Both of these breathing disorders can produce symptoms similar to those of chronic fatigue, including dizziness, difficulty concentrating, shortness of breath, and exhaustion. When both occur together, they can also cause chest pain, palpitations, fatigue, and (unsurprisingly) anxiety. The researchers believe that these breathing problems may worsen the effects of chronic fatigue or even play a direct role in post-exertional malaise."Possibly dysautonomia could trigger more rapid and irregular breathing," said Mancini. "It is well known that chronic fatigue syndrome patients often have dysautonomia in the form of orthostatic intolerance, which means you feel worse when upright and not moving. This raises the heart rate and leads to hyperventilation."

Pulmonary physiotherapy?

These findings suggest that addressing dysfunctional breathing could help relieve some symptoms of chronic fatigue. The researchers plan to continue investigating how dysfunctional breathing and hyperventilation interact. Although more studies are needed before any official treatments are recommended, they already have several promising ideas."Breathing exercises via yoga could potentially help, or gentle physical conditioning where breath control is important, as with swimming," suggested Natelson. "Or biofeedback, with assessment of breathing while encouraging gentle continuous breath use. If a patient is hyperventilating, this can be seen by a device that measures exhaled CO2. If this value is low, then the patient can try to reduce the depth of breathing to raise it to more normal values."

Source: ScienceDaily

Scientists discover surprising brain trigger behind high blood pressure

 Researchers have identified a specific part of the brain that may play a key role in high blood pressure.

This area, called the lateral parafacial region, is located in the brainstem, the oldest part of the brain responsible for automatic functions like breathing, digestion, and heart rate."The lateral parafacial region is recruited into action causing us to exhale during a laugh, exercise or coughing," says lead researcher Professor Julian Paton, director of Manaaki Manawa, Centre for Heart Research at Waipapa Taumata Rau, University of Auckland.

"These exhalations are what we call 'forced' and driven by our powerful abdominal muscles.

"In contrast, a normal exhalation does not need these muscles to contract, it happens because the lungs are elastic."

How Breathing and Blood Pressure Are Connected

The team found that this brain region is also linked to nerves that constrict blood vessels, which increases blood pressure.

"We've unearthed a new region of the brain that is causing high blood pressure. Yes, the brain is to blame for hypertension!" says Paton.

"We discovered that, in conditions of high blood pressure, the lateral parafacial region is activated and, when our team inactivated this region, blood pressure fell to normal levels."

These findings suggest that certain breathing patterns, particularly those involving strong abdominal muscle use, can contribute to elevated blood pressure. Identifying abdominal breathing in people with hypertension may help pinpoint the cause and guide more targeted treatment.

The study was recently published in the journal Circulation Research.

A Potential New Treatment Target

'Can we target this brainstem region?'

The researchers then explored whether this part of the brain could be treated with medication.

"Targeting the brain with drugs is tricky because they act on the entire brain and not a selected region such as the parafacial nucleus," says Paton.

A key breakthrough came when the team discovered that this region is activated by signals originating outside the brain. These signals come from the carotid bodies, small clusters of cells in the neck near the carotid artery that monitor oxygen levels in the blood.

Because the carotid bodies can be safely targeted with medication, they offer a promising alternative approach.

"Our goal is to target the carotid bodies, and we are importing a new drug that is being repurposed by us to quench carotid body activity and inactivate 'remotely' the lateral parafacial region safely, i.e., without needing to use a drug that penetrates the brain."

This discovery could lead to new ways to treat high blood pressure, especially in people with sleep apnoea, where carotid body activity increases when breathing stops during sleep.

Source: Sciencedaily

How to use AI for discovery -- without leading science astray

 Over the past decade, AI has permeated nearly every corner of science: Machine learning models have been used to predict protein structures, estimate the fraction of the Amazon rainforest that has been lost to deforestation and even classify faraway galaxies that might be home to exoplanets.

But while AI can be used to speed scientific discovery -- helping researchers make predictions about phenomena that may be difficult or costly to study in the real world -- it can also lead scientists astray. In the same way that chatbots sometimes "hallucinate," or make things up, machine learning models can sometimes present misleading or downright false results.In a paper published online today (Thursday, Nov. 9) in Science, researchers at the University of California, Berkeley, present a new statistical technique for safely using the predictions obtained from machine learning models to test scientific hypotheses.

The technique, called prediction-powered inference (PPI), uses a small amount of real-world data to correct the output of large, general models -- such as AlphaFold, which predicts protein structures -- in the context of specific scientific questions.

"These models are meant to be general: They can answer many questions, but we don't know which questions they answer well and which questions they answer badly -- and if you use them naively, without knowing which case you're in, you can get bad answers," said study author Michael Jordan, the Pehong Chen Distinguished Professor of electrical engineering and computer science and of statistics at UC Berkeley. "With PPI, you're able to use the model, but correct for possible errors, even when you don't know the nature of those errors at the outset."

The risk of hidden biases

When scientists conduct experiments, they're not just looking for a single answer -- they want to obtain a range of plausible answers. This is done by calculating a "confidence interval," which, in the simplest case, can be found by repeating an experiment many times and seeing how the results vary.

In most science studies, a confidence interval usually refers to a summary or combined statistic, not individual data points. Unfortunately, machine learning systems focus on individual data points, and thus do not provide scientists with the kinds of uncertainty assessments that they care about. For instance, AlphaFold predicts the structure of a single protein, but it doesn't provide a notion of confidence for that structure, nor a way to obtain confidence intervals that refer to general properties of proteins.

Scientists may be tempted to use the predictions from AlphaFold as if they were data to compute classical confidence intervals, ignoring the fact that these predictions are not data. The problem with this approach is that machine learning systems have many hidden biases that can skew the results. These biases arise, in part, from the data on which they are trained, which are generally existing scientific research that may not have had the same focus as the current study.

Source: ScienceDaily

Google and ChatGPT have mixed results in medical informatiom queries

 When you need accurate information about a serious illness, should you go to Google or ChatGPT?

An interdisciplinary study led by University of California, Riverside, computer scientists found that both internet information gathering services have strengths and weaknesses for people seeking information about Alzheimer's disease and other forms of dementia. The team included clinical scientists from the University of Alabama and Florida International University.Google provides the most current information, but query results are skewed by service and product providers seeking customers, the researchers found. ChatGPT, meanwhile, provides more objective information, but it can be outdated and lacks the sources of its information in its narrative responses.

"If you pick the best features of both, you can build a better system, and I think that this is what will happen in the next couple of years," said Vagelis Hristidis, a professor of computer science and engineering in UCR's Bourns College of Engineering.

In their study, Hristidis and his co-authors submitted 60 queries to both Google and ChatGPT that would be typical submissions from people living with dementia and their families.

The researchers focused on dementia because more than 6 million Americans are impacted by Alzheimer's disease or a related condition, said study co-author Nicole Ruggiano, a professor of social work at the University of Alabama.

"Research also shows that caregivers of people living with dementia are among the most engaged stakeholders in pursuing health information, since they often are tasked with making decisions for their loved one's care," Ruggiano said.

Half of the queries submitted by the researchers sought information about the disease processes, while the other half sought information on services that could assist patients and their families.

The results were mixed.

"Google has more up-to-date information, and covers everything," Hristidis said. "Whereas ChatGPT is trained every few months. So, it is behind. Let's say there's some new medicine that just came out last week, you will not find it on ChatGPT."

While dated, ChatGPT provided more reliable and accurate information than Google. This is because the ChatGPT creators at OpenAI choose the most reliable websites when they train ChatGPT through computationally intensive machine learning. Yet, users are left in dark about specific sources of information because the resulting narratives are void of references.Google, however, has a reliability problem because it essentially "covers everything from the reliable sources to advertisements," Hristidis said.

In fact, advertisers pay Google for their website links to appear at the top of search result pages. So, users often first see links to websites of for-profit companies trying to sell them care-related services and products. Finding reliable information from Google searches thus requires a level of user skill and experience, Hristidis said.

Co-author Ellen Brown, an associate professor of nursing at the Florida International University, pointed out that families need timely information about Alzheimer's. .

"Although there is no cure for the disease, many clinical trials are underway and recently a promising treatment for early stage Alzheimer's disease was approved by the FDA," Brown said. "Therefore, up-to-date information is important for families looking to learn about recent discoveries and available treatments."

The authors of the study write that "the addition of both the source and the date of health-related information and availability in other languages may increase the value of these platforms for both non-medical and medical professionals." It was published in the Journal of Medical Internet Research under the title "ChatGPT vs Google for Queries Related to Dementia and Other Cognitive Decline: Comparison of Results."

Google and ChatGPT both scored low for readability scores, which makes it difficult for people with lower levels of education and low health literacy skills.

"My prediction is that the readability is the easier thing to improve because there are already some tools, some AI methods, that can read and paraphrase text," Hristidis said. "In terms of improving reliability, accuracy, and so on, that's much harder. Don't forget that it took scientists many decades of AI research to build ChatGPT. It is going to be slow improvements from where we are now."

Source: ScienceDaily

AIs are irrational, but not in the same way that humans are

 Large Language Models behind popular generative AI platforms like ChatGPT gave different answers when asked to respond to the same reasoning test and didn't improve when given additional context, finds a new study from researchers at UCL.

The study, published in Royal Society Open Science, tested the most advanced Large Language Models (LLMs) using cognitive psychology tests to gauge their capacity for reasoning. The results highlight the importance of understanding how these AIs 'think' before entrusting them with tasks, particularly those involving decision-making.

In recent years, the LLMs that power generative AI apps like ChatGPT have become increasingly sophisticated. Their ability to produce realistic text, images, audio and video has prompted concern about their capacity to steal jobs, influence elections and commit crime.Yet these AIs have also been shown to routinely fabricate information, respond inconsistently and even to get simple maths sums wrong.

In this study, researchers from UCL systematically analysed whether seven LLMs were capable of rational reasoning. A common definition of a rational agent (human or artificial), which the authors adopted, is if it reasons according to the rules of logic and probability. An irrational agent is one that does not reason according to these rules1.

The LLMs were given a battery of 12 common tests from cognitive psychology to evaluate reasoning, including the Wason task, the Linda problem and the Monty Hall problem2. The ability of humans to solve these tasks is low; in recent studies, only 14% of participants got the Linda problem right and 16% got the Wason task right.

The models exhibited irrationality in many of their answers, such as providing varying responses when asked the same question 10 times. They were prone to making simple mistakes, including basic addition errors and mistaking consonants for vowels, which led them to provide incorrect answers.

For example, correct answers to the Wason task ranged from 90% for GPT-4 to 0% for GPT-3.5 and Google Bard. Llama 2 70b, which answered correctly 10% of the time, mistook the letter K for a vowel and so answered incorrectly.

While most humans would also fail to answer the Wason task correctly, it is unlikely that this would be because they didn't know what a vowel was.

Olivia Macmillan-Scott, first author of the study from UCL Computer Science, said: "Based on the results of our study and other research on Large Language Models, it's safe to say that these models do not 'think' like humans yet.

"That said, the model with the largest dataset, GPT-4, performed a lot better than other models, suggesting that they are improving rapidly. However, it is difficult to say how this particular model reasons because it is a closed system. I suspect there are other tools in use that you wouldn't have found in its predecessor GPT-3.5."

Source: ScienceDaily

Thinking AI models emit 50x more CO2—and often for nothing

 No matter which questions we ask an AI, the model will come up with an answer. To produce this information - regardless of whether than answer is correct or not - the model uses tokens. Tokens are words or parts of words that are converted into a string of numbers that can be processed by the LLM.

This conversion, as well as other computing processes, produce CO2 emissions. Many users, however, are unaware of the substantial carbon footprint associated with these technologies. Now, researchers in Germany measured and compared CO2 emissions of different, already trained, LLMs using a set of standardized questions."The environmental impact of questioning trained LLMs is strongly determined by their reasoning approach, with explicit reasoning processes significantly driving up energy consumption and carbon emissions," said first author Maximilian Dauner, a researcher at Hochschule München University of Applied Sciences and first author of the Frontiers in Communication study. "We found that reasoning-enabled models produced up to 50 times more CO2 emissions than concise response models."

'Thinking' AI causes most emissions

The researchers evaluated 14 LLMs ranging from seven to 72 billion parameters on 1,000 benchmark questions across diverse subjects. Parameters determine how LLMs learn and process information.

Reasoning models, on average, created 543.5 'thinking' tokens per questions, whereas concise models required just 37.7 tokens per question. Thinking tokens are additional tokens that reasoning LLMs generate before producing an answer. A higher token footprint always means higher CO2 emissions. It doesn't, however, necessarily mean the resulting answers are more correct, as elaborate detail that is not always essential for correctness.

The most accurate model was the reasoning-enabled Cogito model with 70 billion parameters, reaching 84.9% accuracy. The model produced three times more CO2 emissions than similar sized models that generated concise answers. "Currently, we see a clear accuracy-sustainability trade-off inherent in LLM technologies," said Dauner. "None of the models that kept emissions below 500 grams of CO2 equivalent achieved higher than 80% accuracy on answering the 1,000 questions correctly." CO2 equivalent is the unit used to measure the climate impact of various greenhouse gases.

Subject matter also resulted in significantly different levels of CO2 emissions. Questions that required lengthy reasoning processes, for example abstract algebra or philosophy, led to up to six times higher emissions than more straightforward subjects, like high school history.

Practicing thoughtful use

The researchers said they hope their work will cause people to make more informed decisions about their own AI use. "Users can significantly reduce emissions by prompting AI to generate concise answers or limiting the use of high-capacity models to tasks that genuinely require that power," Dauner pointed out.

Choice of model, for instance, can make a significant difference in CO2 emissions. For example, having DeepSeek R1 (70 billion parameters) answer 600,000 questions would create CO2 emissions equal to a round-trip flight from London to New York. Meanwhile, Qwen 2.5 (72 billion parameters) can answer more than three times as many questions (about 1.9 million) with similar accuracy rates while generating the same emissions.

The researchers said that their results may be impacted by the choice of hardware used in the study, an emission factor that may vary regionally depending on local energy grid mixes, and the examined models. These factors may limit the generalizability of the results.

"If users know the exact CO2 cost of their AI-generated outputs, such as casually turning themselves into an action figure, they might be more selective and thoughtful about when and how they use these technologies," Dauner concluded.

Source: ScienceDaily

Study finds ChatGPT gets science wrong more often than you think

 Washington State University professor Mesut Cicek and his research team repeatedly tested ChatGPT by giving it hypotheses taken from scientific papers. The goal was to see if the AI could correctly determine whether each claim was supported by research or not -- in other words, whether it was true or false.

In total, the team evaluated more than 700 hypotheses and asked the same question 10 times for each one to measure consistency.

Accuracy Results and Limits of AI Performance

When the experiment was first conducted in 2024, ChatGPT answered correctly 76.5% of the time. In a follow-up test in 2025, accuracy rose slightly to 80%. However, once the researchers adjusted for random guessing, the results looked far less impressive. The AI performed only about 60% better than chance, a level closer to a low D than to strong reliability.

The system had the most difficulty identifying false statements, correctly labeling them only 16.4% of the time. It also showed notable inconsistency. Even when given the exact same prompt 10 times, ChatGPT produced consistent answers only about 73% of the time.

Inconsistent Answers Raise Concerns

"We're not just talking about accuracy, we're talking about inconsistency, because if you ask the same question again and again, you come up with different answers," said Cicek, an associate professor in the Department of Marketing and International Business in WSU's Carson College of Business and lead author of the new publication.

"We used 10 prompts with the same exact question. Everything was identical. It would answer true. Next, it says it's false. It's true, it's false, false, true. There were several cases where there were five true, five false."

AI Fluency vs. Real Understanding

The findings, published in the Rutgers Business Review, highlight the importance of using caution when relying on AI for important decisions, especially those that require nuanced or complex reasoning. While generative AI can produce smooth, convincing language, it does not yet demonstrate the same level of conceptual understanding.

According to Cicek, these results suggest that artificial general intelligence capable of truly "thinking" may still be further away than many expect.

"Current AI tools don't understand the world the way we do -- they don't have a 'brain,'" Cicek said. "They just memorize, and they can give you some insight, but they don't understand what they're talking about."

Source: ScienceDaily

Just 20 minutes of physical activity may benefit your memory

 

• A study suggests that 20 minutes of moderate cycling increases brain activity in the hippocampus, a region critical for learning and memory.
• This increased hippocampal activity may support memory consolidation, potentially helping the brain process and store information.
• Higher exercise intensity was associated with stronger brain activity, suggesting that exercise levels may influence the magnitude of the brain’s memory-related response.

Many different strategies and techniques exist to help maintain or improve a person’s memory. Often, many of these methods emphasize keeping the brain active.

These brain-training activities often focus on stimulating specific regions of the brain involved in memory, such as the hippocampus. In particular, the hippocampus plays an important role in memory consolidation, the process by which newly formed memories are strengthened into long-term memories.

Previously, neuroscientists have documented “ripples” of brain activity relevant to memory in mice and rats. However, they had been unable to confirm this link in humans.

Now, a new study, published in Brain Communications, suggests that brief sessions of physical exercise could alter human brain activity, triggering waves of ‘ripples’ that may support the brain to process and store information more effectively.

These findings provide some of the first direct evidence explaining how exercise benefits cognition in humans at the neural level.

Exercise sparks ‘memory ripples’ in the brain

Researchers have long known that physical activity is associated with improvements in memory and other cognitive functions. However, due to difficulties recording human brain activity, much of the previous evidence comes from behavioral studies or indirect imaging methods.

However, in this study, researchers directly recorded brain activity using intracranial electroencephalography (iEEG). Typically used for monitoring severe epilepsy, this technique involves implanting electrodes in the brain to observe neural signals with high precision.

Co-lead author Juan Ramirez-Villegas, PhD, a tenure-track research scientist at the Neuroscience Institute Alicante of the Spanish Research Council and the Miguel Hernández University of Elche spoke to Medical News Today about the advantages of this technique.

“These electrodes can record electrical signals produced by groups of neurons which are active together. This allows us to detect brief events of coordinated neural activity such as ripples, which are thought to play an important role in memory,” said Ramirez-Villegas.

“Because these recordings capture the brain’s electrical activity directly, they provide a level of detail that brain scans cannot. They allow us to observe the rapid dynamics of neural circuits in real time, giving us a much clearer window into how the brain processes information,” he added.

What the study involved

The team studied 14 participants aged 17 to 50 years who had electrodes implanted as part of epilepsy treatment. The 14 individuals completed a 20-minute session on a stationary bike, cycling at a comfortable pace.

The researchers chose this protocol as it was short and feasible to implement in a clinical setting. The team then measured brain activity both before and after the exercise session.

After exercising, the research team observed a significant increase in high-frequency ripple activity originating in the hippocampus. These ripples then spread toward other brain regions involved in processing and recalling information.

“Ripples are very brief bursts of highly synchronized electrical activity in the brain’s memory center, the hippocampus,” Ramirez-Villegas explained to us.

“In animals, they are known to play a key role in stabilizing memories after an experience. You can think of them as moments when the brain rapidly ‘reviews’ information, helping convert recent experiences into lasting memories,” he illustrated.

Source: Medical News Today

Vitamin D may help keep long COVID at bay

 

• About every six in 100 people who have COVID-19 go on to develop post-COVID-19 condition, dubbed ‘long COVID.’
• There is currently no cure for long COVID and anyone who is infected with the coronavirus that causes COVID-19 can develop it.
• Past studies show that certain lifestyle choices, such as following a healthy diet, may help reduce a person’s long COVID risk.
• A new study found that while vitamin D supplements may not reduce the severity of COVID-19, they may help reduce a person’s risk of developing long COVID.

According to the World Health Organization (WHO), about every six in 100 peopleTrusted Source who have COVID-19 go on to develop post-COVID-19 condition, also known as long COVID.

Previous research shows that long COVID symptomsTrusted Source can last from 3 months to as long as a few years. Common symptoms of long COVID include constant fatigue, respiratory problems like a chronic cough or shortness of breath, “brain fog,” sleeping issues, headache, digestive issues like diarrhea and stomach painTrusted Source, joint pain, depression, and anxiety.

There is currently no cure for long COVID. Symptoms are managed through medications and treatments for specific symptoms, such as physical therapy, cognitive rehabilitation, pulmonary rehabilitation, and mental health interventions.

Anyone who is infected with the coronavirus that causes COVID-19 can develop long COVID. However, older adults, those with severe COVID-19, and people with underlying health conditions are generally at a higher riskTrusted Source of developing long COVID.

Past studies show that certain lifestyle choices, such as maintaining a healthy body weight, being physically active, prioritizing sleep, and avoiding smoking, may help reduce a person’s long COVID risk.

Now, a new study published in The Journal of Nutrition has found that, while vitamin D supplements may not reduce the severity of a COVID-19 infection, they may help reduce a person’s risk of developing long COVID.

Focusing on COVID-19 and vitamin D3 supplementation

For this study, researchers recruited more than 1,700 adults in the United States and Mongolia who had recently tested positive for COVID-19, as well as 277 members of their household who came in contact with the infected individual.

Participants were randomly selected to receive either a daily vitamin D3 supplement or placebo for 4 weeks. The average time between a participant’s positive COVID-19 test and start of the vitamin D3 supplement or placebo was 3 days.

“Long COVID continues to impact the lives and quality of life of millions of people worldwide,” JoAnn Manson, MD, DrPH, chief of the Division of Preventive Medicine at Mass General Brigham, Michael and Lee Bell Professor of Women’s Health at Harvard Medical School, and senior author of this study, 

Source:Medical News Today.

Scientists crack a 20-year nuclear mystery behind the creation of gold

 Gold cannot form until certain unstable atomic nuclei break apart. Exactly how those nuclear transformations unfold has long been difficult to determine. Now, nuclear physicists at the University of Tennessee (UT) report three discoveries in a single study that clarify important parts of this process. Their findings could help researchers build improved models of the stellar events that create heavy elements and better predict the behavior of exotic atomic nuclei.

Heavy elements such as gold and platinum are forged under extraordinary conditions, including when stars collapse, explode, or collide. These events trigger the rapid neutron capture process (or r-process for short). During this process, an atomic nucleus absorbs neutrons in rapid succession. As the nucleus grows heavier and more unstable, it eventually breaks down into lighter and more stable forms.

Along this pathway across the nuclide chart, a common sequence involves beta decay of the parent nucleus followed by the release of two neutrons. The atomic nuclei involved in these reactions are extremely rare and unstable, making them difficult or even impossible to study directly in experiments. Because of this, scientists rely heavily on theoretical models, which must be tested and refined using laboratory data.

Studying Rare Nuclei With CERN's ISOLDE Facility

To investigate the process more closely, UT researchers collaborated with scientists from several institutions. The team included UT Graduate Students Peter Dyszel and Jacob Gouge, Professor Robert Grzywacz, Associate Professor Miguel Madurga, and Research Associate Monika Piersa-Silkowska. Their work also built on data analysis methods developed by Research Assistant Professor Zhengyu Xu.

The researchers began with large quantities of the rare isotope indium-134.

"These nuclei are hard to make and require a lot of new technology to synthesize in sufficient quantities," Grzywacz explained.

The team carried out the experiments at the ISOLDE Decay Station at CERN, which produced abundant indium-134 nuclei and used advanced laser separation techniques to ensure their purity. When indium-134 undergoes decay, it generates excited forms of tin-134, tin-133, and tin-132.

Using a neutron detector funded through the National Science Foundation Major Research Instrumentation program and constructed at UT, the scientists uncovered three major findings. The most significant result was the first measurement of neutron energies associated with beta-delayed two-neutron emission.

"The two-neutron emission is the biggest deal," Grzywacz said.

Beta-delayed two-neutron emission occurs only in exotic nuclei, which are unstable and exist only briefly. The energy needed to separate two neutrons from the nucleus is extremely small, but in this experiment it was large enough to measure.

"The reason this is hard is because neutrons like to bounce around. It's hard to tell if it's one or two," Grzywacz explained. In earlier attempts, "no one measured energies," so this approach "opens a completely new field."

This research marks the first detailed study of two-neutron emission from a nucleus that lies along the r-process pathway. The results provide valuable insight for improving models that describe how stellar events create heavy elements such as gold.

A Long-Sought Neutron State in Tin

The team's second major discovery was the first observation of a long predicted single particle neutron state in tin-133. According to Grzywacz, the nucleus begins in an excited state and must release energy to stabilize.

"Tin is in an excited state. (It) has to cool off. It can spit out a neutron, or, with enough energy, it can spit out two neutrons. It should always spit two neutrons, but it doesn't."

Traditionally, scientists believed the tin nucleus simply released neutrons to cool down, effectively losing any trace of the earlier beta decay event. In that scenario the nucleus behaves like an "amnesiac nucleus," with no memory of how it was formed.

"We say the tin doesn't forget," Grzywacz said. "This 'shadow' of indium doesn't completely disappear. The memory is not erased."

Advanced neutron detectors allowed researchers to detect this elusive nuclear state. The observation suggests that current theoretical explanations are incomplete and that scientists need a more sophisticated framework to explain why some decays release one neutron while others release two.

Source: ScienceDaily

The surprising new ways bacteria spread without propellers

 New research from Arizona State University shows that bacteria can travel in unexpected ways even when their usual propulsion system fails. Normally, bacteria move using flagella, slender, whip-like structures that spin to push the cells forward. The new studies reveal that microbes can still spread across surfaces without these structures.

Movement is critical for bacteria. It allows them to gather into communities, explore new environments, and escape harmful conditions. Learning how bacteria move may help scientists design better strategies to prevent infections.

In the first study, researcher Navish Wadhwa and his team found that salmonella and E. coli can migrate across moist surfaces even when their flagella are disabled. The bacteria generate motion through their metabolism. When they ferment sugars, they create tiny outward flowing currents across the wet surface. These flows slowly push the bacterial colony outward, similar to leaves drifting along a thin stream.

Researchers named this newly identified movement "swashing." The discovery could help explain how disease causing microbes manage to colonize medical devices, wounds, and food processing equipment. By understanding how bacterial metabolism drives this type of motion, scientists may be able to slow or stop it by altering environmental conditions such as pH or sugar levels.

"We were amazed by the ability of these bacteria to migrate across surfaces without functional flagella. In fact, our collaborators originally designed this experiment as a 'negative control,' meaning that we expected (once rendered) flagella-less, the cells to not move," Wadhwa says. "But the bacteria migrated with abandon, as if nothing were amiss, setting us off on a multiyear quest to understand how they were doing it.

"It just goes to show that even when we think we've got something figured out, there are often surprises waiting just under the surface, or in this case, above it."

Wadhwa is a researcher with the Biodesign Center for Mechanisms of Evolution and assistant professor with the Department of Physics at ASU. The study appears in the Journal of Bacteriology and was selected as an Editor's Pick, highlighting its significance.

Sugar Fueled Swashing

The swashing effect begins when bacteria consume fermentable sugars such as glucose, maltose, or xylose. During fermentation, the microbes release acidic by products including acetate and formate. These compounds pull water toward the colony from the surrounding surface, creating tiny currents that push the cells outward.

Fermentable sugars are required for this movement. Without them, bacteria cannot produce the fluid flows needed for swashing. Sugar rich environments inside the body, such as mucus, could therefore make it easier for harmful bacteria to spread and trigger infections.

Scientists also tested what happens when surfactants, detergent-like molecules, are added to the colonies. These compounds stopped swashing completely. However, the same chemicals did not interfere with swarming, another type of bacterial movement powered by flagella that enables microbes to rapidly spread across wet surfaces. This difference suggests the two behaviors rely on separate physical mechanisms. It also hints that surfactants might someday be used to control bacterial movement depending on whether microbes are swashing or swarming.

The discovery that bacteria can colonize surfaces even when their normal swimming machinery fails has important health implications. Some microbes could spread across medical catheters, implants, or hospital equipment through swashing. Simply blocking flagella might not prevent that spread. Instead, treatments may need to target the metabolic processes that drive the fluid currents.

E. coli and salmonella are both well known causes of foodborne illness. Recognizing that these bacteria can spread through passive fluid flows may help improve sanitation strategies in food processing facilities. Because swashing depends on fermentation and acidic by products, altering factors such as surface pH or sugar levels could limit bacterial growth. The study found that even modest changes in acidity could influence how bacteria move.

Similar conditions may also exist inside the human body. Moist environments such as gut mucus, wound fluids, or the urinary tract provide surfaces where bacteria could spread through swashing, even when their flagella are not functioning effectively.

A Molecular Gear System for Bacterial Movement

A second study examined a different group of microbes called flavobacteria. Unlike E. coli, these bacteria do not swim. Instead, they travel along environmental and host surfaces using a specialized machine known as the type 9 secretion system, or T9SS. This system powers a molecular conveyor belt that moves along the surface of the cell.

Under normal conditions, the T9SS allows flavobacteria to glide across surfaces. The mechanism works by moving an adhesive coated belt around the outside of the cell, pulling the bacterium forward in a motion that resembles a microscopic snowmobile.

Source: ScienceDaily

Severe COVID or flu may raise lung cancer risk years later

 Serious cases of COVID-19 and influenza may do more than cause short term illness. New research from UVA Health's Beirne B. Carter Center for Immunology Research and the UVA Comprehensive Cancer Center suggests that severe viral infections can create conditions in the lungs that help cancer develop and progress more quickly. The study also found that vaccination can prevent many of these harmful effects.

Researchers led by UVA School of Medicine scientist Jie Sun, PhD, discovered that severe respiratory infections can alter immune cells in the lungs in ways that support tumor growth months or even years later. Based on these findings, the scientists recommend that doctors closely watch patients who recover from severe COVID, flu, or pneumonia so lung cancer can be detected early, when treatment is most effective.

"A bad case of COVID or flu can leave the lungs in a long-lasting 'inflamed' state that makes it easier for cancer to take hold later," said Sun, co-director of UVA's Carter Center and a member of UVA's Division of Infectious Diseases and International Health. "The encouraging news is that vaccination largely prevents those harmful changes for cancer growth in the lung."

Severe Respiratory Infections and Long Term Lung Damage

Respiratory illnesses such as influenza and COVID are among the most common sources of lung injury. However, scientists have not fully understood how this type of damage might influence cancer risk years later. To explore this question, Sun and his team studied the effects of severe lung infections in both laboratory mice and human patients.

The findings were striking. Mice that experienced severe lung infections were more likely to develop lung cancer later and were also more likely to die from the disease. When the researchers analyzed patient data, they found a similar pattern. People who had previously been hospitalized with COVID-19 showed a higher rate of lung cancer diagnoses.

The analysis revealed a 1.24-fold increase in lung cancer incidence among patients who had been hospitalized for COVID-19. This elevated risk was seen regardless of whether the individuals smoked or had other medical conditions, which doctors refer to as "comorbidities."

"These findings have important immediate implications for how we monitor patients after severe respiratory viral infection," said Jeffrey Sturek, MD, PhD, a UVA physician-scientist who collaborated on the study. "We've known for a long time that things like smoking increase the risk for lung cancer. The results from this study suggest that we may need to think about severe respiratory viral infection similarly. For example, in some patients who are at high risk for lung cancer based on smoking history, we recommend close monitoring with routine screening CT scans of the lungs to catch cancer early. In future studies, we may want to consider a similar approach after severe respiratory viral infection."

Immune Changes That Create a Pro Tumor Environment

Experiments in mice helped the researchers uncover why severe infections may increase cancer risk. The team observed major changes in immune cells known as neutrophils and macrophages, which normally help defend the lungs.

After severe infection, some neutrophils began behaving abnormally and contributed to a persistent inflammatory environment described as "pro-tumor," meaning it supports cancer growth. The scientists also found significant changes in epithelial cells that line the lungs and the tiny air sacs responsible for breathing.

Vaccination May Protect the Lungs

The study also produced encouraging findings about prevention. Prior vaccination appeared to block many of the lung changes linked to cancer development. Vaccines help the immune system respond more effectively to infections, which reduces how severe the illness becomes.

The researchers observed the increased cancer risk mainly in people who had severe COVID-19. Individuals who experienced only mild infections did not show this elevated risk and actually had a slight decrease in lung cancer incidence.

Even so, the scientists warn that many people who survived severe COVID-19 or other serious respiratory infections could face a higher risk of lung cancer in the future.

"With tens of millions of people globally experiencing long-term pulmonary [COVID-19] sequelae, these findings carry significant implications for clinical care," the researchers wrote in their scientific paper. "Individuals recovering from severe viral pneumonia, particularly those with smoking history, may benefit from enhanced lung cancer surveillance, and preventing severe infection through vaccination may confer indirect cancer protection benefits."

Source: ScienceDaily

Indoor Plants, HEPA Air Purifiers, or Open Window Ventilation: Simple Clean Air Tips for Household PM2.5 Reduction

 Indoor air quality has become a growing concern as people spend more time indoors in tightly sealed homes. Indoor plants' air quality benefits, HEPA air purifiers, and open window ventilation are three of the most common strategies people use when looking for simple clean air tips. Each approach works differently, and understanding their strengths and limits helps households choose the best mix for healthier breathing.

What Affects Household Air Quality?

Household air can contain dust, pet dander, mold spores, smoke, and tiny particles known as PM2.5. These fine particles are small enough to enter deep into the lungs and are linked to respiratory and cardiovascular issues, so household PM2.5 reduction is a key goal.

Indoor sources such as cooking, candles, cleaning products, and damp areas can increase pollution levels, while outdoor air pollution can enter through leaks, doors, and windows.

Do Indoor Plants Really Improve Air Quality?

Do Indoor Plants Actually Improve Indoor Air Quality?

Plants can absorb certain gases and volatile organic compounds (VOCs) through their leaves and roots. In controlled lab conditions this can reduce specific pollutants over time, but in real homes the effect is usually modest, especially for very small particles like PM2.5.

Even so, indoor plants' air quality benefits go beyond chemistry. Leaves can trap some dust on their surfaces, and plants can slightly increase humidity, which may reduce airborne dust. Greenery also improves mood and perceived freshness, making indoor spaces feel more pleasant and relaxing.

Best Indoor Plants for Cleaner Air

Some species are especially popular for indoor plants' air quality goals because they are hardy and have large leaf surfaces. Common choices include snake plant, spider plant, pothos, peace lily, rubber plant, and ZZ plant.

A practical approach is placing one to three medium-sized plants in rooms where people spend the most time, such as the living room, bedroom, or home office. Indoor plants are best treated as a supporting measure rather than a primary solution for household PM2.5 reduction.

Limitations of Indoor Plants for PM2.5 Reduction

Indoor plants are not a fast or powerful tool for fine particles. Unlike mechanical filtration, they do not actively pull large volumes of air through a filter, so they cannot quickly clear a smoky or heavily polluted room.

Source: Medical Daily