Publisher Health

Exposure to pollution during pregnancy can have many adverse effects in infants and children that can even extend into adulthood. For example, air pollution exposure is associated with increased risk of low birth weight, preterm birth and risk for developing asthma later in life. Much of this is due to the fast pace of fetal growth and development; however, the exact ways pollutants have these effects and the roles of genes related to immune function and stress response are not fully understood.
In a study published in the journal Antioxidants, researchers from Texas A&M University and the University of Florida worked to clarify how a gene related to oxidant response known as Nrf2 affects fetal development in an experimental model. Natalie Johnson, PhD, associate professor at the Texas A&M School of Public Health, along with Carmen Lau, DVM, Jonathan Behlen and others exposed animal models modified to lack the Nrf2 gene and unmodified animal models to particulate pollution like that found in diesel exhaust. They then evaluated the effects on litter size, birth weight and immune markers found in the lung and liver tissue of newborn offspring.
Particulate matter pollution is divided into three categories based on particle size: coarse particles, fine particles and ultrafine particles. Fine particles less than 2.5 microns in diameter and ultrafine particles less than one-tenth of a micron across are of greatest concern. Researchers have found associations between fine particulate pollution and increased odds of respiratory diseases, but less work has been done on ultrafine pollutants, and no health standards currently exist for this smallest category. The tiny size of ultrafine particles means they can work deeper into airways, possibly making them an even bigger health risk than fine particles.
The gene Nrf2 is known to affect immune function and stress response in adults, but research on the effects of this gene in infants and children has been explored less. To better understand the role of Nrf2 during development and clarify how ultrafine particles affect health, researchers exposed both unmodified animal models and those that have had the Nrf2 genes knocked out to fresh, filtered air and air containing ultrafine particles like those found in diesel exhaust, a common pollutant in urban areas. The researchers monitored weight gain in pregnant animal models in all four groups and recorded litter sizes and birth weights of the offspring.
There were no statistically significant differences in weight gain in the animal models in the four groups during pregnancy. Similarly, there were not notable differences in litter sizes. However, the Nrf2-deficient offspring had lower birth weights than their unmodified counterparts, with the greatest effects in Nrf2-deficient animal models exposed to pollution. Exposure to pollution had no notable effects in unmodified animal models, which may indicate Nrf2 playing some protective role during pregnancy.
The researchers also analyzed lung and liver tissue from the offpsring to measure differences in certain immune markers and expression of genes related to oxidative stress response. They found significant differences in immune markers in Nrf2-deficient offpsring, indicating a change in immune function in those models. These findings point to the lack of a functioning Nrf2 gene being a main contributor to the differences between the groups.
These results are in line with other studies that have found associations between Nrf2 deficiency and some chronic diseases. For example, previous research found that adult Nrf2-deficient animal models were more likely to develop autoimmune diseases. Although more work lies ahead, this study demonstrates that the absence of a functioning Nrf2 gene affects prenatal growth of animal models, especially when exposed to ultrafine particulate air pollution in utero.
These findings could point to a possible mechanism through which ultrafine particulate matter can affect placental function and prenatal health. This highlights a need for further research into the roles of genes on immune and stress response and how those genes interact with environmental factors. The research also reinforces the importance of establishing health standards for ultrafine particulate matter pollution, which appear to have serious effects on prenatal and neonatal health and development.
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Materials provided by Texas A&M University. Original written by Rae Lynn Mitchell. Note: Content may be edited for style and length.

Bacteria promote cancer metastasis by bolstering the strength of host cells against mechanical stress in the bloodstream, promoting cell survival during tumor progression, researchers report April 7th in the journal Cell.
“Our study reveals that the cancer cell’s behavior is also controlled by the microbes hiding inside tumors, the majority of which were originally thought to be sterile,” says senior author Shang Cai of the Westlake Laboratory of Life Sciences and Biomedicine. “This microbial involvement is distinct from the genetic, epigenetic, and metabolic components that most cancer drugs target.”
“However, our study does not mean that using antibiotics during cancer treatment will benefit patients,” he says. “Therefore, it is still an important scientific question of how to manage the intratumor bacteria to improve cancer treatment in the future.”
Microbes play a critical role in affecting cancer susceptibility and tumor progression, particularly in colorectal cancers. However, emerging evidence suggests that they are also integral components of the tumor tissue itself in in a broad range of cancer types, such as pancreatic cancer, lung cancer, and breast cancer. Microbial features are linked to cancer risk, prognosis, and treatment responses, yet the biological functions of tumor-resident microbes in tumor progression remain unclear.
Whether these microbes are passengers or drivers of tumor progression has been an intriguing question. “Tumor cells hijacked by microbes could be more common than previously thought, which underscores the broad clinical value of understanding the exact role of the tumor-resident microbial community in cancer progression,” Cai says.
To address this gap, Cai and his collaborators used a mouse model of breast cancer with significant amounts of bacteria inside cells, similar to human breast cancer. They found that the microbes can travel through the circulatory system with the cancer cells and play critical roles in tumor metastasis. Specifically, these passenger bacteria are able to modulate the cellular actin network and promoted cell survival against mechanical stress in circulation.
“We were surprised initially at the fact that such a low abundance of bacteria could exert such a crucial role in cancer metastasis. What is even more astonishing is that only one shot of bacteria injection into the breast tumor can cause a tumor that originally rarely metastasizes to start to metastasize,” Cai says. “Intracellular microbiota could be a potential target for preventing metastasis in broad cancer types at an early stage, which is much better than to have to treat it later on.”
Although the study revealed a clear role of intratumor bacteria in promoting cancer cell metastatic colonization, the authors did not exclude the possibility that the gut microbiome and immune system may act together with intratumor bacteria to determine cancer progression. In the future, further in-depth analysis of how bacteria invade tumor cells, how intracellular bacteria are integrated into the host cell system, and how bacteria-containing tumor cells interact with the immune system will provide insights on how to properly implement antibiotics for cancer therapeutics in the clinic.
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Materials provided by Cell Press. Note: Content may be edited for style and length.

A new artificial intelligence-based approach can predict, significantly more accurately than a doctor, if and when a patient could die of cardiac arrest. The technology, built on raw images of patient’s diseased hearts and patient backgrounds, stands to revolutionize clinical decision making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine’s deadliest and most puzzling conditions.
The work, led by Johns Hopkins University researchers, is detailed today in Nature Cardiovascular Research.
“Sudden cardiac death caused by arrhythmia accounts for as many as 20 percent of all deaths worldwide and we know little about why it’s happening or how to tell who’s at risk,” said senior author Natalia Trayanova, the Murray B. Sachs professor of Biomedical Engineering and Medicine. “There are patients who may be at low risk of sudden cardiac death getting defibrillators that they might not need and then there are high-risk patients that aren’t getting the treatment they need and could die in the prime of their life. What our algorithm can do is determine who is at risk for cardiac death and when it will occur, allowing doctors to decide exactly what needs to be done.”
The team is the first to use neural networks to build a personalized survival assessment for each patient with heart disease. These risk measures provide with high accuracy the chance for a sudden cardiac death over 10 years, and when it’s most likely to happen.
The deep learning technology is called Survival Study of Cardiac Arrhythmia Risk (SSCAR). The name alludes to cardiac scarring caused by heart disease that often results in lethal arrhythmias, and the key to the algorithm’s predictions.
The team used contrast-enhanced cardiac imagesthat visualize scar distribution from hundreds of real patients at Johns Hopkins Hospital with cardiac scarring to train an algorithm to detect patterns and relationships not visible to the naked eye. Current clinical cardiac image analysis extracts only simple scar features like volume and mass, severely underutilizing what’s demonstrated in this work to be critical data.

Almost one third of babies born with hypoplastic left heart syndrome, or HLHS, die from heart failure before their first birthday. By uncovering cellular processes that drive heart failure in these young patients, a new study may hold the answers to identifying and treating those at highest risk of early death.
Published today in Cell Stem Cellby scientists at the University of Pittsburgh School of Medicine, the study found that two commonly used medications, Viagra and an over-the-counter drug called tauroursodeoxycholic acid (TUDCA), restored processes that drive heart failure in cells derived from patients, opening potential avenues for new HLHS treatments.
“HLHS is one of the most lethal types of congenital heart disease,” said Cecilia Lo, Ph.D., chair of the Department of Developmental Biology at Pitt, and senior author of the study. “What causes heart failure in HLHS patients who die before one year of age is unknown, and the only treatment option is a heart transplant, which often is not possible. If we can find the cause, then there is hope for therapy.”
To get to the heart of severe HLHS causes, Lo and Xinxiu (Cindy) Xu, Ph.D., first author of the study and a postdoctoral researcher in Lo’s lab, collected skin cells from three healthy people and 10 HLHS patients, who had either milder disease, surviving past age five without a transplant, or severe HLHS, meaning they died or required a heart transplant in their first year of life.
First, Xu reprogrammed patient skin cells into so-called induced pluripotent stem cells, which can become any type of cell. Next, she added a mixture of growth factors and nutrients that prompt the stem cells to develop into heart cells.
By observing the heart cells under a microscope, the researchers noticed clear differences among cells from different patient groups. Just as a living heart squeezes and releases to pump blood, so too do heart cells in a dish, even without blood to circulate. Cells from patients with milder HLHS looked and behaved similarly to those from healthy people, pulsing quickly and steadily. In contrast, cells from the severe group throbbed in a more languid manner, eerily reminiscent of what doctors see in the hearts of many living HLHS patients.

Many people who recover from COVID-19 experience long-term symptoms, such as brain fog or heart problems. Increasing evidence suggests that the virus can also impair fertility. Now, researchers reporting in ACS Omega have analyzed protein levels in semen of men who have recovered from COVID-19. The pilot study suggests that even mild or moderate illness could change the levels of proteins related to male reproductive function, the researchers say.
Although SARS-CoV-2 mainly affects the respiratory system, the virus — and the body’s response to it — also damages other tissues. Recent evidence indicates that COVID-19 infection can reduce male fertility, and the virus has been detected in male reproductive organs. Firuza Parikh and Rajesh Parikh at Jaslok Hospital, Sanjeeva Srivastava at the Indian Institute of Technology and colleagues wondered if COVID-19 infection could have long-term impacts on the male reproductive system. To find out, they decided to compare levels of proteins in the semen of healthy men and those who previously had mild or moderate cases of COVID-19.
The researchers analyzed semen samples from 10 healthy men and 17 men who had recently recovered from COVID-19. None of the men, who ranged in age from 20 to 45, had a prior history of infertility. The team found that the recovered men had significantly reduced sperm count and motility, and fewer normally shaped sperm, than men who hadn’t had COVID-19. When the researchers analyzed semen proteins using liquid chromatography-tandem mass spectrometry, they found 27 proteins at higher levels and 21 proteins at lower levels in COVID-19-recovered men compared with the control group. Many of the proteins were involved in reproductive function. Two of the fertility-related proteins, semenogelin 1 and prosaposin, were present at less than half their levels in the semen of the COVID-19-recovered group than in the semen of controls. These findings suggest that SARS-CoV-2 has direct or indirect effects on male reproductive health that linger after recovery, the researchers say. The work might also reveal insights into the pathophysiology of human reproduction in recovered men, they add. However, they note that larger studies should be done to confirm these findings, and a control group of men who recently recovered from other flu-like illnesses should be included to ensure that the findings are specific for COVID-19.
The authors acknowledge funding from Jaslok Hospital.
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Materials provided by American Chemical Society. Note: Content may be edited for style and length.

Australian researchers have discovered a new neurodevelopmental disorder after uncovering its link to a tumor suppressor gene.
The international research collaboration, led by the Murdoch Children’s Research Institute (MCRI) and published in the American Journal of Human Genetics, has linked a recognised tumor suppressor gene to a new neurodevelopmental syndrome, ending the diagnostic journey for 32 families around the world.
The study found variations in the FBXW7 gene were associated with the newly identified condition, which causes mild to severe developmental delay, intellectual disability, hypotonia and gastrointestinal issues.
Murdoch Children’s researcher Dr Sarah Stephenson said because the FBXW7 gene regulated the life-cycle of cells, cell growth and survival, the research team speculated that abnormal cell proliferation during brain development may underpin the broad spectrum of brain abnormalities identified in this new disorder.
“FBXW7 now joins a steeply increasing number of intellectual disability/autism spectrum disorder genes that have been implicated in disorders that affect nervous system development, leading to atypical brain function, affecting emotion, learning ability, self-control and memory,” she said.
The study used cutting-edge diagnostic tools, genomic sequencing and global data sharing platforms to identify 35 people, aged 2-44 years, from 32 families in seven countries harbouring the FBXW7 gene, which had variants that were associated with the never-before described neurodevelopmental syndrome.

Using data collected by the National Health Interview Survey from 2004 to 2018, researchers found that the proportion of people who reported sleeping fewer than seven hours per day increased significantly over the 15-year period, and it was significantly higher among Black people.
The study was published on April 7 in JAMA Network Open.
“As an indicator of sleep health, adequate sleep duration is essential for achieving and maintaining a healthy life,” said lead author César Caraballo-Cordovez, MD, postdoctoral associate in the Yale-based Center for Outcomes Research and Evaluation (CORE). “The current expert consensus is that most adults should get between 7 and 9 hours of sleep in a 24-hour period; and we found that Black people, on average, were persistently less likely to report sleeping such recommended duration. Specifically, we found that over the 15 years we analyzed, Black people had the highest prevalence of both short sleep duration [fewer than seven hours] and long sleep duration [more than nine hours].”
The Yale-led team reported that, in 2018, the percentage of people reporting short sleep duration was 11 points higher among Black people when compared with white people. The same disparity was 7.5 points in 2004. They investigated how these findings varied by sex and household income and found that the disparities were the highest for Black women and Black individuals with middle or high income.
There were also differences between racial and ethnic groups when sleep duration was analyzed by age. For instance, they found that the disparities were the highest for young and middle-aged Black adults, slightly narrowing among the elderly. “This suggests that factors related to working or employment conditions are disproportionally preventing Black individuals from having adequate sleep,” said Caraballo-Cordovez.
Sleep is closely related to overall physical and mental health, said senior author Harlan M. Krumholz, MD, SM, Harold H. Hines Jr. Professor of Medicine at Yale and director of CORE.
“Both short sleep and long sleep duration are associated with higher risk of suffering adverse medical events, including higher risk of death,” he said. “Thus, the persistent sleep disparities for Black people may be contributing to the persistent average worse health status among Black people There should be renewed efforts to eliminate the socioeconomic and health conditions that prevent minoritized racial and ethnic individuals from achieving adequate sleep-including racism.”
The study team included Shiwani Mahajan, MBBS, MHS; Javier Valero-Elizondo, MD, MPH; Yuan Lu, ScD; Daisy Massey, Amarnath R. Annapureddy, MD; Brita Roy, MD, MPH, MHS; Carley Riley, MD, MPP, MHS; Karthik Murugiah, MBBS; Johanna Elumn, MSW, PhD; Marcella Nunez-Smith, MD, MHS; Howard P. Forman, MD, MBA; Chandra Jackson, PhD, MS; Khurram Nasir, MD, SM; and Jeph Herrin, PhD.
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Materials provided by Yale University. Original written by Elisabeth Reitman. Note: Content may be edited for style and length.

University of Alberta researchers have trained a machine learning model to identify people with post-traumatic stress disorder with 80 per cent accuracy by analyzing text data. The model could one day serve as an accessible and inexpensive screening tool to support health professionals in detecting and diagnosing PTSD or other mental health disorders through telehealth platforms.
Psychiatry PhD candidate Jeff Sawalha, who led the project, performed a sentiment analysis of text from a dataset created by Jonathan Gratch at USC’s Institute for Creative Technologies. Sentiment analysis involves taking a large body of data, such as the contents of a series of tweets, and categorizing them — for example, seeing how many are expressing positive thoughts and how many are expressing negative thoughts.
“We wanted to strictly look at the sentiment analysis from this dataset to see if we could properly identify or distinguish individuals with PTSD just using the emotional content of these interviews,” said Sawalha.
The text in the USC dataset was gathered through 250 semi-structured interviews conducted by an artificial character, Ellie, over video conferencing calls with 188 people without PTSD and 87 with PTSD.
Sawalha and his team were able to identify individuals with PTSD through scores indicating that their speech featured mainly neutral or negative responses.
“This is in line with a lot of the literature around emotion and PTSD. Some people tend to be neutral, numbing their emotions and maybe not saying too much. And then there are others who express their negative emotions.”
The process is undoubtedly complex. For example, even a simple phrase like “I didn’t hate that” could prove challenging to categorize, explained Russ Greiner, study co-author, professor in the Department of Computing Science and founding scientific director of the Alberta Machine Intelligence Institute. However, the fact that Sawalha was able to glean information about which individuals had PTSD from the text data alone opens the door to the possibility of applying similar models to other datasets with other mental health disorders in mind.
“Text data is so ubiquitous, it’s so available, you have so much of it,” Sawalha said. “From a machine learning perspective, with this much data, it may be better able to learn some of the intricate patterns that help differentiate people who have a particular mental illness.”
Next steps involve partnering with collaborators at the U of A to see whether integrating other types of data, such as speech or motion, could help enrich the model. Additionally, some neurological disorders like Alzheimer’s as well as some mental health disorders like schizophrenia have a strong language component, Sawalha explained, making them another potential area to analyze.
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Materials provided by University of Alberta. Original written by Adrianna MacPherson. Note: Content may be edited for style and length.

A University of Georgia nanotechnology research groupentered the race to develop a rapid test for COVID-19 in August 2020, running experiments on a new sensor for an American manufacturing company. The group, led by Yiping Zhao and Ralph Tripp, tested nanotechnology-based optical sensors designed for COVID-19 detection and saw the potential for their home-grown technology.
In March 2022, the group filed a patent application and published its first paper on rapid detection of COVID-19, using a localized surface plasmon resonance (LSPR) virus sensor, developed based on human angiotensin-converting enzyme 2 protein (ACE2) functionalized silver nanotriangle arrays.
The sensor has high sensitivity and specificity to the spike protein RBD of SARS-CoV-2 as well as human coronavirus NL63.
“Right now, we already have rapid antigen test kits available on the market, though the big issue continues to be the high rate of false positives, around 60%,” said Yanjun Yang, doctoral student in the UGA College of Engineering and lead author on the new paper.
“Our technology, also in a rapid kit but using a spectrometer to do the detection, is much more accurate.”
The swab-based rapid test developed by the Zhao group uses a UV spectrometer for spike protein detection. The test will cover all COVID-19 variants, as well as any future variants.
“The method we developed shall have a much better sensing performance than the rapid test kits, very close to the PCR tests currently in use,” said Zhao, Distinguished Research Professor in the Franklin College of Arts and Sciences department of physics and astronomy. “The setup and the operation of the sensor is very simple, and the test time essentially will be less than 10 minutes.”
Zhao’s lab is developing a detector based on this work within $10 and the sensor will link to a smartphone app.
“The LSPR sensor has several advantages in rapid diagnostics of SARS-CoV-2 (CoV2). Highly sensitive, specific, and able to detect CoV2 at picomolar concentrations in saliva, it’s rapidity at less than 20 minutes is as good or better than current diagnostic platforms including RT-qPCR, also known as the ‘gold standard’,” said Tripp, professor and GRA Chair in Vaccine and Therapeutic Development in the College of Veterinary Medicine department of infectious diseases and co-author on the study. “In addition, this method of detection is highly reproducible. This platform is a significant leap forward in diagnostics.”
“Working with biologists and developing this technology was a great experience,” Yang said. “I’ve only done research in the lab, so I never knew how to make something become a product, so it’s a really good opportunity to understand how we can tie our work to the practical issues and make it commercialized.”
The study is published in Sensors and Actuators B: Chemical
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Materials provided by University of Georgia. Original written by Alan Flurry. Note: Content may be edited for style and length.

People may feel less vulnerable and take fewer safety precautions about COVID-19 when they are with, or even just think about, their friends instead of acquaintances or strangers, according to research published by the American Psychological Association.
During the two years of the COVID-19 pandemic, many people have become accustomed to spending time with their closest social circle, which may have unintended consequences, said study authors Hyunjung Crystal Lee, PhD, assistant professor of marketing, and Eline De Vries, PhD, associate professor of marketing, at the Universidad Carlos III de Madrid in Spain.
“Friends and family can provide a sense of comfort, but it’s irrational and dangerous to believe they will protect you from being infected by COVID-19,” Lee said. “This tendency that we call the ‘friend-shield effect’ could intensify a false sense of safety and contribute to future infections.”
De Vries and Lee conducted five online experiments with U.S. residents in a study that was published online in the Journal of Experimental Psychology: Applied.
The experiments found that individuals engaged in fewer health protection behaviors when the COVID-19 infection risk was associated with close friendships, including situations when people thought of a friend while reading COVID-19-related news, believed a friend was the source of a prior COVID-19 infection or noted a friend’s presence while dining at an indoor restaurant. Under such circumstances, study participants decided to purchase fewer health protection items, such as masks and hand sanitizers, and perceived less likelihood of infection, even when the infection risk could stem from strangers in crowds.
The friend-shield effect was more prominent among people who identified themselves as conservatives than those who said they were liberals, arguably because conservatives tend to have clearer boundaries between people whom they hold as close friends and those they regard as distant others.