A new study found that people who were exposed to higher levels of noise from aircraft were more likely to have a higher body mass index, an indicator for obesity that can lead to stroke or hypertension. The findings highlight how the environment — and environmental injustices — can shape health outcomes.
Research has shown that noise from airplanes and helicopters flying overhead are far more bothersome to people than noise from other modes of transportation, and a growing body of research suggests that aircraft noise is also contributing to negative health outcomes.
One of the latest studies, led by Boston University School of Public Health (BUSPH) and Oregon State University (OSU) indicates that airplane noise may increase one’s risk of developing cardiometabolic diseases, a cluster of conditions such as heart attack, stroke, diabetes, and hypertension.
Published in the journal Environment International, the study found that people who were exposed to airplane noise levels at 45 dB or more were more likely to have higher self-reported body mass index (BMI), with the highest BMI measures linked to aircraft noise levels at 55 dB or above. Airplane noise exposure at 45 dB or above was also associated with having higher BMI in middle to late adulthood from early adulthood. For comparison, the sound of a whisper is 30 dB, a library setting is 40 dB, and a typical conversation at home is 50 dB.
BMI is an indicator of general obesity, which can lead to cardiometabolic diseases, as well as a range of other health issues. The study is the first to explore a connection between aircraft noise exposure and obesity nationwide in the United States; past studies on this subject have focused on European populations, and results have varied.
“Prior research has shown that aircraft noise can elevate stress responses and disturb sleep, but there has been mixed evidence of any links with body mass index,” says study lead and corresponding author Dr. Matthew Bozigar, assistant professor of epidemiology at OSU. “We were surprised to see a fairly robust link between aircraft noise and higher body mass index among women across the US.”
These new findings underscore the role of the environment on one’s risk of chronic disease.

‘Obesity has become very stigmatized, but what is important to remember is that it is linked with poor cardiometabolic health outcomes, and that it has strong environmental drivers,” Dr. Bozigar says. “This is disheartening, but also promising, in the sense that we could potentially enact policies to mitigate these drivers of obesity.”
For the study, Dr. Bozigar and colleagues examined airplane noise exposure and self-reported BMI and other individual characteristics among nearly 75,000 participants living around 90 of the major US airports. The participants were selected from the Nurses’ Health Studies (NHS), ongoing, prospective studies of US female nurses who have completed biennial questionnaires since the 1970s and 1980s.
The team examined aircraft noise levels every five years from 1995 to 2010, using a day-night estimate (DNL) that captures the average noise level over a 24-hour period and applies a 10 dB adjustment for aircraft noise occurring at night, when background noise is low. The current policy-related threshold for significant noise impacts is above DNL 65 dB. The team assessed BMI measures at multiple thresholds below that (less than 45 dB; 45-54 dB; 55 dB and above; and continuous exposure at 45 dB or above) for the nurses’ geocoded residential addresses.
Although the team acknowledges that BMI is a suboptimal metric, the independent and strong association between more aircraft noise exposure and higher BMI that they observed is notable. There were also regional differences, with stronger associations among participants on the West Coast and those who live in arid conditions.
“We can only hypothesize about why we saw these regional variations, but one reason may relate to the era of regional development, building characteristics, and climate which may affect factors such as housing age, design, and level of insulation,” says study senior author Dr. Junenette Peters, associate professor of environmental health at BUSPH. “Regional differences in temperature and humidity may influence behaviors such as window opening, so perhaps study participants living in the West were more exposed to aircraft noise due to open windows or housing type, which allowed more noise to penetrate.”
Similarly, the stronger associations observed in arid climates, many of which are also in the Western US, may relate to the way noise travels under various atmospheric conditions, Dr. Peters says.
Future research should explore this link between aircraft noise exposure and obesity further, as well as broader inequities in environmental noise exposure, particularly among other populations. Previous data suggest that Black, Hispanic, and low-income populations are disproportionately exposed to aircraft noise. The participants in the NHS study groups were primarily White and of mid-level socioeconomic status.
“We need to study the potential health impacts of environmental injustices in transportation noise exposures alongside other environmental drivers of poor health outcomes” Dr. Bozigar says. “There is a lot more to figure out, but this study adds evidence to a growing body of literature that noise negatively impacts health.”

Researchers who had been using Fitbit data to help predict surgical outcomes have a new method to more accurately gauge how patients may recover from spine surgery.
Using machine learning techniques developed at the AI for Health Institute at Washington University in St. Louis, Chenyang Lu, the Fullgraf Professor in the university’s McKelvey School of Engineering, collaborated with Jacob Greenberg, MD, assistant professor of neurosurgery at the School of Medicine, to develop a way to predict recovery more accurately from lumbar spine surgery.
The results published this month in the journal Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, show that their model outperforms previous models to predict spine surgery outcomes. This is important because in lower back surgery and many other types of orthopedic operations, the outcomes vary widely depending on the patient’s structural disease but also varying physical and mental health characteristics across patients.
Surgical recovery is influenced by both preoperative physical and mental health. Some people may have catastrophizing, or excessive worry, in the face of pain that can make pain and recovery worse. Others may suffer from physiological problems that cause worse pain. If physicians can get a heads-up on the various pitfalls for each patient, that will allow for better individualized treatment plans.
“By predicting the outcomes before the surgery, we can help establish some expectations and help with early interventions and identify high risk factors,” said Ziqi Xu, a PhD student in Lu’s lab and first author on the paper.
Previous work in predicting surgery outcomes typically used patient questionnaires given once or twice in clinics that capture only one static slice of time.
“It failed to capture the long-term dynamics of physical and psychological patterns of the patients,” Xu said. Prior work training machine learning algorithms focus on just one aspect of surgery outcome “but ignore the inherent multidimensional nature of surgery recovery,” she added.

Researchers have used mobile health data from Fitbit devices to monitor and measure recovery and compare activity levels over time but this research has shown that activity data, plus longitudinal assessment data, is more accurate in predicting how the patient will do after surgery, Greenberg said.
The current work offers a “proof of principle” showing, with the multimodal machine learning, doctors can see a much more accurate “big picture” of all the interrelated factors that affect recovery. Proceeding this work, the team first laid out the statistical methods and protocol to ensure they were feeding the AI the right balanced diet of data.
Prior to the current publication, the team published an initial proof of principle in Neurosurgery showing that patient-reported and objective wearable measurements improve predictions of early recovery compared to traditional patient assessments. In addition to Greenberg and Xu, Madelynn Frumkin, a PhD psychological and brain sciences student in Thomas Rodebaugh’s laboratory in Arts & Sciences, was co-first author on that work. Wilson “Zack” Ray, MD, the Henry G. and Edith R. Schwartz Professor of neurosurgery in the School of Medicine, was co-senior author, along with Rodebaugh and Lu. Rodebaugh is now at the University of North Carolina at Chapel Hill.
In that research, they show that Fitbit data can be correlated with multiple surveys that assess a person’s social and emotional state. They collected that data via “ecological momentary assessments” (EMAs) that employ smart phones to give patients frequent prompts to assess mood, pain levels and behavior multiple times throughout day.
“We combine wearables, EMA -and clinical records to capture a broad range of information about the patients, from physical activities to subjective reports of pain and mental health, and to clinical characteristics,” Lu said.
Greenberg added that state-of-the-art statistical tools that Rodebaugh and Frumkin have helped advance, such as “Dynamic Structural Equation Modeling,” were key in analyzing the complex, longitudinal EMA data.

For the most recent study they then took all those factors and developed a new machine learning technique of “Multi-Modal Multi-Task Learning (M3TL)” to effectively combine these different types of data to predict multiple recovery outcomes.
In this approach, the AI learns to weigh the relatedness among the outcomes while capturing their differences from the multimodal data, Lu adds.
This method takes shared information on interrelated tasks of predicting different outcomes and then leverages the shared information to help the model understand how to make an accurate prediction, according to Xu.
It all comes together in the final package producing a predicted change for each patient’s post-operative pain interference and physical function score.
Greenberg says the study is ongoing as they continue to fine tune their models so they can take these more detailed assessments, predict outcomes and, most notably, “understand what types of factors can potentially be modified to improve longer term outcomes.”
This study was funded by grants from AO Spine North America, the Cervical Spine Research Society, the Scoliosis Research Society, the Foundation for Barnes-Jewish Hospital, Washington University/BJC Healthcare Big Ideas Competition, the Fullgraf Foundation, and the National Institute of Mental Health (1F31MH124291-01A).

Obeldesivir (GS-5245), a novel investigational small molecule oral antiviral, represents a new tool in the ongoing effort to prepare for future pandemics.
Several researchers at the University of North Carolina at Chapel Hill’s Gillings School of Global Public Health are co-authors of a new study published online May 22 by the journal Science Translational Medicine.
The study shares findings from an academic-corporate partnership between biopharmaceutical company Gilead Sciences and the Sheahan and Baric Labs at the Gillings School.
This is the same partnership that previously investigated remdesivir (sold under the brand name Veklury®). In 2020, remdesivir was authorized for emergency use and then fully approved during the COVID-19 pandemic. The drug works by stopping the SARS-CoV-2 virus from replicating. Remdesivir helps shorten time to recovery and reduces disease progression and mortality, but patients must visit a health care setting for IV administration.
Since the early days of the COVID-19 pandemic, researchers have been working on an oral antiviral drug of the parent nucleoside of remdesivir that could stop replication of the virus.
“Oral bioavailability means that you can take the medicine by mouth and do not need to visit a health care setting to receive treatment,” says Timothy Sheahan, PhD, an expert virologist and assistant professor of epidemiology at the Gillings School. “That is a potential limitation for remdesivir, which is an IV drug. With a prescription, you could take an oral antiviral at home just like you would take Tylenol.”
In the new study, oral obeldesivir was shown to reduce disease severity in mice infected with one of several different coronaviruses, including SARS-CoV-2 (which causes COVID-19), SARS-CoV and MERS-CoV. Researchers observed a dose-dependent reduction in viral replication, weight loss, lung injury and loss of lung function.

In addition, combining obeldesivir with the antiviral nirmatrelvir (an active component of Paxlovid) further improved outcomes in COVID-19-infected mice.
These results support further development of obeldesivir as a potential broadly effective anticoronaviral drug.
Gilead Sciences recently completed a Phase 3 clinical trial of the therapeutic in more than 2,000 people who tested positive for COVID-19 but did not have risk factors for developing more severe disease and were not hospitalized.
“While obeldesivir did not meet its primary clinical endpoint of reducing time to symptom alleviation in a standard risk population in the Phase 3 OAKTREE trial, it is a promising drug and no safety concerns were identified,” Sheahan says. “I think the issue is that currently, most people have stronger immunity to the virus that causes COVID-19 and the disease severity has greatly diminished since the pandemic. Early on, many people were ending up in the hospital and dying, which made the differences between placebo and treated groups stark and easily measurable. With obeldesivir, resarchers weren’t looking at prevention of hospitalization and death, but rather wanted to see how much it shortened the time for symptoms to resolve in people who are not at high risk for severe COVID-19. It’s harder to observe those subtler differences in the background of milder disease.”
Ultimately, the clinical trial assessed the safety profile of obeldesivir for use in a diverse population. Like remdesivir, obeldesivir could continue to be tested in humans for effectiveness and, if appropriate, rapidly deployed against susceptible novel coronaviruses that might emerge.
The research team also remains hopeful about the potential use of this broad-spectrum antiviral medicine against other RNA viruses.

In disease research, it’s important to know gene expression and where in a tissue the expression is happening, but marrying the two sets of information can be challenging.
“Single-cell technologies, especially in the emerging field of spatial transcriptomics, help scientists see where in a tissue the genes are turned on or off. It combines information about gene activity with the exact locations within the disease tissues,” explains Fan Zhang, PhD, assistant professor of medicine with a secondary appointment in the Department of Biomedical Informatics at the University of Colorado School of Medicine.
“This is really valuable because it lets physicians and researchers see not just which genes are active, but also where they are active, which can give key insights into how different cells behave and interact in diseased conditions,” she continues.
Effectively combining location and genetic information has been a tough obstacle for researchers — until now.
Zhang and her lab developed a new computational machine learning method — called Spatial Transcriptomic multi-viEW, or “STew” for short — that enables the joint analysis of spatial variation and gene expression changes in a scalable way that can handle large amounts of cells.
This new technology may help researchers learn more about the spatial biology behind many different diseases and lead them to better treatment therapies.
A path toward an accurate target for effective treatment
The new technology is accurate in finding significant patterns that show where specific cell activities happen, which is important for understanding how cells work and how clinical tissues are structured in diseases. Zhang’s lab has already successfully applied STew on human tissues, including human brains, skin with inflammation, and breast cancer tumors.

For Zhang, who studies inflammatory diseases using computational AI tools and translational approaches, finding a good target for treatment is often a challenge, but STew could help change that.
“With inflamed joints, for example, the genes causing inflammation could be closer to the blood vessel through interacting with mesenchymal structures, or they could be farther away, but knowing that exact location and cell-cell communication patterns helps us better understand the underlying mechanisms,” she says.
By merging spatial biology and molecular diversity, STew gives researchers a new dimension in classifying patient heterogeneity.
“If you only use gene expression to classify patients, you don’t have the full picture,” Zhang says. “Once you add in spatial information, you have a more comprehensive understanding.”
“We expect STew to be effective in uncovering critical molecular and cellular signals in various clinical conditions, like different types of tumors and autoimmune disorders, opening new avenues for dysregulated immune pathways for therapeutic intervention for theses disease,” she continues.
A novel software-driven route to empowering collaboration
There’s another perk that comes with the development of STew: collaboration. Scientific discoveries often benefit from experts from different fields working together.

Because STew has a wide application, Zhang says the software will bring researchers together in new and exciting ways that will ultimately benefit the field of medicine and offer promise to patients in need of treatments.
“We want to encourage researchers across specialties, skillsets, and even departments, to collaborate in ways that they previously might not have been able to do,” Zhang says. “We can accomplish more together, so it’s important to boost data-driven and AI tool-motivated collaboration in a way that is meaningful.”

Dr. Fauci is testifying for a House panel investigating Covid’s origins. The panel found emails suggesting his aides were skirting public records laws.Dr. Anthony S. Fauci, the former government scientist both celebrated and despised for his work on Covid, on Monday forcefully denied Republican allegations that he sought to cover up the possibility that the pandemic originated in a laboratory, calling the accusation “absolutely false and simply preposterous.”In a tense appearance before the House Select Subcommittee on the Coronavirus Pandemic, Dr. Fauci read out an email from February 2020 in which he encouraged a scientist worried about the possibility of a lab leak to report his concerns to the F.B.I.“It is inconceivable that anyone who reads this email could conclude that I was trying to cover up the possibility of a laboratory leak,” Dr. Fauci testified.Republicans on the panel have spent 15 months rooting through emails, Slack messages and research proposals for evidence against Dr. Fauci. In half a million pages of documents and more than 100 hours of closed-door testimony, the panel has so far found nothing linking the 83-year-old immunologist to the beginnings of the Covid outbreak in China.But the panel has turned up emails suggesting that Dr. Fauci’s former aides were trying to evade public records laws at the medical research agency he ran for 38 years until his retirement in December 2022.Some of those emails paint Dr. Fauci as being preoccupied with his public image; one April 2021 message from an aide said that while Dr. Fauci “prides himself on being like teflon,” he appeared to be “getting worried about the brown stuff hitting the fan” over questions about research funded by his agency, the National Institute of Allergy and Infectious Diseases.We are having trouble retrieving the article content.Please enable JavaScript in your browser settings.Thank you for your patience while we verify access. If you are in Reader mode please exit and log into your Times account, or subscribe for all of The Times.Thank you for your patience while we verify access.Already a subscriber? Log in.Want all of The Times? Subscribe.

Treatment resistance caused by cancer cell plasticity constitutes a major challenge in the treatment of prostate cancer. Published in Nucleic Acids Research, a recent study from the University of Eastern Finland Institute of Biomedicine suggests that the SIX2 protein may be a possible factor underlying increased plasticity of prostate cancer cells and treatment resistance.
Prostate cancer is the most common cancer in men and the second most common cause of cancer mortality in Western countries. Prostate cancer growth is promoted by androgens and can be treated with androgen receptor inhibition therapies, especially as regards aggressive or advanced prostate cancer. However, cancer cells can develop resistance to these therapies, resulting in castration-resistant prostate cancer.
One mechanism underlying treatment resistance may be the plasticity of cancer cells: they can change their degree of differentiation and revert to a stem cell-like state, which helps them avoid the effects of hormonal therapies. However, factors contributing to cell plasticity and the development of treatment resistance remain unclear.
“It is important to identify the key factors contributing to treatment resistance in prostate cancer and how cancer cells change their degree of differentiation to find new targets for therapies. This could even lead to the discovery of a cure for these currently lethal types of cancer,” Academy Research Fellow, Adjunct Professor (Docent) Kirsi Ketola of the University of Eastern Finland says.
Inhibition of the androgen receptor opens up new genomic regions
The new study by the Ketola Lab explored new potential factors contributing to treatment resistance in prostate cancer.
In cells, DNA is packed into chromatin. In regions where gene expression is active, this packing is looser, meaning that chromatin is more open. The Ketola Lab studied chromatin openness in androgen-dependent prostate cancer cells, which were treated with enzalutamide, an inhibitor of the androgen receptor used for treating prostate cancer. The researchers found that following exposure to enzalutamide, the number of new opened chromatin sites was greater than that of new closed chromatin sites. These new opened sites occurred especially in DNA regions containing binding site of the SIX2 protein. The increased activity of the SIX2 protein may contribute to the increased plasticity of cells following drug therapy.

In other words, inhibiting the function of the androgen receptor alters the regulation of genes within cells, allowing for the expression of genes that are normally silenced and the alteration of the cell state.
SIX2 is necessary for embryogenesis, but increases cell plasticity and malignancy in prostate cancer cells
The SIX2 protein is normally active during embryogenesis, where it maintains cells as undifferentiated stem cells, preserving their ability to differentiate.
The study found that the SIX2 protein can regulate the degree of differentiation of even prostate cancer cells that do not have an androgen receptor. The activity of the SIX2 gene increased in cancer cells after exposure to enzalutamide. In particular, the expression of the SIX2 protein has increased in cancer cells that do not express the androgen receptor.
“Silencing of the SIX2 gene, on the other hand, significantly reduced the malignancy of cancer cells that are resistant to hormonal therapies,” Doctoral Researcher Noora Leppänen of the University of Eastern Finland notes.
The stem cell-like state of cancer cells that do not express the androgen receptor, as well as their ability to migrate, invade and metastasize, were significantly reduced following the silencing of the SIX2 gene. Reduced cell division and cancer spread were also observed in experiments conducted on zebrafish. Therefore, inhibiting the activity of the SIX2 protein could be a potential target for drug development to treat or prevent the development of metastatic, hormone therapy-resistant types of cancer.

Biological processes depend on puzzle pieces coming together and interacting. Under specific conditions, these interactions can create something new without external input. This is called self-organization, as seen in a school of fish or a flock of birds. Interestingly, the mammalian embryo develops similarly. In PNAS, David Brückner and Gašper Tkačik from the Institute of Science and Technology Austria (ISTA) introduce a mathematical framework that analyzes self-organization from a single cell to a multicellular organism.
When an embryo develops, many types of cells with different functions need to be generated. For example, some cells will become part of the eye and record visual stimuli, while others will be part of the gut and help digest food. To determine their roles, cells are constantly communicating with each other using chemical signals.
Thanks to this communication, during development, everything is well synchronized and coordinated, and yet there is no central control responsible for this. The cell collective is self-organized and orchestrated by the interactions between the individuals. Each cell reacts to signals of its neighbors. Based on such self-organization, the mammalian embryo develops from a single fertilized egg cell into a multicellular organism.
David Brückner and Gašper Tkačik from the Institute of Science and Technology Austria (ISTA) have now established a mathematical framework that helps analyze this process and predict its optimal parameters. Published in PNAS, this approach represents a unifying mathematical language to describe biological self-organization in embryonic development and beyond.
The self-assembling embryo
In nature, self-organization is all around us: we can observe it in fish schools, bird flocks, or insect collectives, and even in microscopic processes regulated by cells. NOMIS fellow and ISTA postdoc David Brückner is interested in getting a better understanding of these processes from a theoretical standpoint. His focus lies on embryonic development — a complex process governed by genetics and cells communicating with each other.
During embryonic development, a single fertilized cell turns into a multicellular embryo containing organs with lots of different features. “For many steps in this developmental process, the system has no extrinsic signal that directs it what to do. There is an intrinsic property of the system that allows it to establish patterns and structures,” says Brückner. “The intrinsic property is what is known as self-organization.” Even with unpredictable factors — which physicists call “noise” — the embryonic patterns are formed reliably and consistently. In recent years, scientists have gained a deeper understanding of the molecular details that drive this complex process. A mathematical framework to analyze and quantify its performance, however, was lacking. The language of information theory provides answers.

Bridging expertise
“Information theory is a universal language to quantify structure and regularity in statistical ensembles, which are a collection of replicates of the same process. Embryonic development can be seen as such a process that reproducibly generates functional organisms that are very similar but not identical,” says Gašper Tkačik, professor at ISTA and expert in this field. For a long time, Tkačik has been studying how information gets processed in biological systems, for instance in the fly embryo. “In the early fly embryo, patterns are not self-organized,” he continues. “The mother fly puts chemicals into the egg that instruct the cells on what actions to take.” As the Tkačik group had already developed a framework for this system, Brückner reached out to develop one for the mammalian embryo as well. “With Gašper’s expertise in information theory, we were able to put it together,” Brückner adds excitedly.
Beyond embryo development?
During embryonic development, cells exchange signals and are constantly subject to random, unpredictable fluctuations (noise). Therefore, cellular interactions must be robust. The new framework measures how these interactions are possibly optimized to withstand noise. Using computer simulations of interacting cells, the scientists explored the conditions under which a system can still have a stable final result despite introducing fluctuations.
Although the framework has proven to be successful on three different developmental models that all rely on chemical and mechanical signaling, additional work will be required to apply it to experimental recordings of developmental systems. “In the future, we want to study more complex models with more parameters and dimensions,” Tkačik says. “By quantifying more complex models, we could also apply our framework to experimentally measured patterns of chemical signals in developing embryos,” adds Brückner. For this purpose, the two theoretical scientists will team up with experimentalists.

A humorous remark at just the right time can go a long way. Benevolent humour helps medical assistants (MAs) cope positively with their stressful working day, according to a new study by the Martin Luther University Halle-Wittenberg (MLU) and the Federal Institute for Vocational Education and Training (BIBB). The researchers surveyed more than 600 MAs to find out how they experience their work and what style of humour they use in their daily working lives. They found that if the respondents preferred light, well-intended humour, they were more satisfied with their work and received more positive feedback. Dark humour, such as sarcasm, was more likely to have disadvantages. The study was recently published in the journal BMC Primary Care.
Medical assistants mostly work in primary health care, especially medical practices. In Germany, working as an MA requires a three-year vocational training. The daily work routine of MAs can be very demanding. They are responsible for administrative work and, for example, taking blood samples and applying wound dressings. “Medical assistants are in very close contact with patients for most of the day. They have a lot of responsibility and experience a lot of stress,” says Julia Raecke from BIBB, who is doing her doctorate at MLU. It has long been known that humour can help healthcare workers cope with stress. “However, little is known about the consequences of different humour styles. We set out to investigate those, as it should make a big difference, whether MAs use puns or sarcasm when dealing with patients. Talking to people that are potentially sick requires a lot of empathy and verbal dexterity,” explains Professor René Proyer, a psychologist at MLU.
The two researchers conducted an online survey of more than 600 MAs. The aim was to understand better the relationship between job satisfaction and different humour styles. In addition to the kind of humour they prefer, respondents also provided information about their well-being in the workplace and how competent they feel at work.
If the respondents preferred positive and benevolent humour, they were in general also more satisfied with their work. But not only that: “MAs with a preference for light humour stated that they received more positive feedback and were more likely to feel that they were making a difference at work,” says Julia Raecke. Surprisingly, presumably negative or dark humour did not score worse across the board. “Even though satire and irony are considered dark humour, we found no negative correlation with the respondents’ well-being,” adds Raecke. In contrast, cynicism and especially sarcasm had negative effects. Yet, this does not mean that sarcasm should be condemned completely. “A short sarcastic remark among colleagues might help to release frustration,” says René Proyer.
According to the researchers, humour is one of several factors that influence well-being at work. “Knowing about the effects of humour and different styles can help to make conversations with patients more pleasant. That said, waiting rooms are not supposed to become comedy clubs. It’s more about using humour consciously and appropriately,” concludes Proyer.
The results of the study could help to develop new training programmes. For example, Raecke is investigating whether the social and emotional skills of MAs can be improved with the help of online training.
The study was funded by the Federal Institute for Vocational Education and Training.

Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) that causes inflammation and ulcers (sores) in the digestive tract. Ulcerative colitis affects the innermost lining of the large intestine, also called the colon and rectum. At least 40,000 people are living with IBD in Ireland, and over 5 million globally.
In a new paper recently published in Computers in Biology and Medicine, researchers from CÚRAM at the University of Galway and collaborators at the University of Birmingham present a novel computational model simulating shear stress distribution in the colon with varying mucus thicknesses.
The colon, a vital part of our digestive system, relies on rhythmic contractions to move waste. These movements create mechanical forces on the colon’s mucus-lined surface. This mucus acts as a protective barrier, separating the inside of our body from the trillions of microbes in our gut.
The research team’s computational model uses real-world data on these movements, mucus behaviour and tissue characteristics to simulate the dynamic environment. Their study reveals how the mucus layer acts as a lubricant, significantly increasing faecal velocity and easing waste movement through the colon. This effect is diminished in UC, where the mucus layer is thinner, potentially contributing to constipation.
The model highlights the protective function of mucus, shielding the thin cell layer that performs the colonic processes from the mechanical forces.
When the research team, led by Dr Yury Rochev, School of Physics, University of Galway, investigated the mechanical distribution in the cell layer, they found that shear stress varies along the various functional zones, suggesting a potential role in regulating cell migration, differentiation, and immune responses. However, when this protection is compromised, such as in the UC, it could contribute to inflammation and tissue damage.
Dr Rochev said; “Our model demonstrates that mucus acts as a lubricant, significantly increasing faecal velocity and easing waste movement through the colon. This effect is diminished in ulcerative colitis (UC), where the mucus layer thins, potentially contributing to constipation.”
The model’s revelations do not stop there. It also highlights the protective function of mucus, shielding the delicate cells responsible for essential colonic processes from the mechanical forces generated by bowel movements.

Dr Rochev added: “When we investigated the mechanical distribution in the cell layer, we found that shear stress varies along the different functional zones, suggesting a potential role in regulating cell migration, differentiation, and immune responses. When this protection is compromised, as in UC, it could contribute to inflammation and tissue damage.”
The team is not relying solely on computer simulations. They are developing an experimental model using “organ-on-a-chip” technology to validate their findings.
Ibrahim Erbay, a researcher on the team, said: “We are using intestinal organoids to create a replica of the thin cell layer at the colonic surface. By actively flowing fluid with the organ-on-a-chip platform, we can simulate the mechanical forces similar to those experienced in the colon.”
By combining computational modelling with robust experimental validation, the researchers aim to gain a comprehensive understanding of how mechanical forces influence biological events in both health and disease. This holistic approach promises to improve our understanding of gut health and pave the way for new, targeted therapies for inflammatory bowel diseases and other digestive disorders.
Researcher Ibrahim Erbay added: “This research not only enhances our understanding of basic colon function at the cellular level, but also offers a powerful tool for developing new therapeutic approaches. We can now model various drug delivery systems and optimize them, potentially leading to more effective treatments for gut-related conditions.”

Understanding the connections between different brain areas could pave the way to better treatment strategies for conditions like Alzheimer’s, schizophrenia, and depression.
In 2019, as a postdoc in Cold Spring Harbor Laboratory’s (CSHL’s) Zador lab, Xiaoyin Chen helped develop a technique to map these connections. BARseq identifies cells in the brain by the genes they use and traces the connecting neural circuitry. Early versions of BARseq mapped gene expression across thousands of neural pathways, using “barcodes” or short snippets of RNA.
Chen is now an assistant investigator at the Allen Brain Institute. He recently reunited with CSHL Professor Anthony Zador to upgrade BARseq’s capabilities. What does that look like? Instead of thousands of neurons, BARseq can now map millions.
“We are focused on pushing BARseq forward. We want to make this easy for everybody to use, faster, more sensitive. Can we read out more information with it? With much higher scale, you can start to answer different questions,” says Chen.
The team began their search for answers in the brain’s visual cortex. Sight is one of the most common ways humans perceive the world. Information travels from the eyes to the visual cortex for processing. But what happens in the brain when the visual cortex’s neural inroads are cut or don’t form at all?
“People have known for a while that visual inputs are very important in shaping the brain,” Chen explains. “But we don’t know, at the exact cell-type resolution BARseq provides, what actually happens.”
The team used BARseq to map the brains of nine mice and traced gene expression in each mouse’s visual cortex. It’s the first time the technique has been used to map this many entire brains. Amazingly, the team found that if the mice went blind, the genes in the visual cortex started to look like those in neighboring cortical areas of the brain.
“The effects of losing vision were very broad,” Chen explains. “The visual cortex itself changes. It becomes more similar to the areas around it. There are still a lot of questions about how development controls this patterning.”
Chen is now working to expand BARseq’s capabilities even further. He and his team are using the technique to investigate how connections are wired in developing brains and how these connections evolve.
“Understanding how cortical areas are set up is the first step in understanding these connections,” he says. “But it’s not enough. We still need to discover how they progress during development. BARseq can bring us closer to that goal.”