Publisher Health

As you read this article, touch receptors in your skin are sensing your environment. Your clothes and jewelry, the chair you’re sitting on, the computer keyboard or mobile device you’re using, even your fingers as they brush one another unintentionally — each touch activates collections of nerve cells. But, unless a stimulus is particularly unexpected or required to help you orient your own movements, your brain ignores many of these inputs.
Now, Salk researchers have discovered how neurons in a small area of the mammalian brain help filter distracting or disruptive signals — specifically from the hands — to coordinate dexterous movements. Their results, published in the journal Science on October 14, 2021, may hold lessons in how the brain filters other sensory information as well.
“These findings have implications not only for gaining a better understanding of how our nervous system interacts with the world, but also for teaching us how to build better prosthetics and robots, and how to more effectively repair neural circuitry after disease or injury,” says Eiman Azim, assistant professor in Salk’s Molecular Neurobiology Laboratory and the William Scandling Developmental Chair.
Scientists have long known that input from the hands is needed to coordinate dexterous movements, from throwing a ball to playing a musical instrument. In one classic experiment, volunteers with anesthetized, numb fingertips found it extremely difficult to pick up and light a match.
“There’s a common misconception that the brain sends a signal and you just perform the resulting movement,” says Azim. “But in reality, the brain is constantly incorporating feedback information about the state of your limbs and fingers and adjusting its output in response.”
If the brain responded to every signal from the body, it would quickly become overwhelmed — as happens with some sensory processing disorders. Azim and his colleagues wanted to identify exactly how a healthy brain manages to pick and choose which tactile signals to take into account to coordinate dexterous movements like manipulating objects.

The F.D.A. has been without a permanent chief since President Biden took office.WASHINGTON — The White House is considering nominating Dr. Robert M. Califf, a former Food and Drug Administration commissioner, to once again lead the agency, according to five people familiar with the search for a candidate.The F.D.A. has been without a permanent chief since President Biden took office, leading to frequent criticism from public health experts who have said the agency has lacked clear direction during an intense phase of the pandemic, while it has faced a crush of reviews of coronavirus vaccines, tests and treatments.The people familiar with the search process said that no final decision had been made. The acting F.D.A. commissioner, Dr. Janet Woodcock, a longtime drug regulator, is nearing the end of the term that acting officials are allowed to serve.Dr. Califf declined to comment. News of his possible nomination was first reported by The Washington Post.Dr. Califf, a cardiologist who teaches at the Duke University School of Medicine, served only briefly as F.D.A. commissioner, at the end of President Barack Obama’s second term. He was the agency’s deputy commissioner for medical products and tobacco before leading it from February 2016 to January 2017.He was confirmed by the Senate as commissioner in February 2016 in a vote of 89 to 4, after some lawmakers opposed his nomination over what they said was the agency’s poor record on prescription painkillers. His nomination faced its fiercest pushback from Democrats.Katie Rogers

The severe outbreak of COVID-19 in Delhi, India, in 2021 showed not only that the Delta variant of SARS-CoV2 is extremely transmissible but that it can infect individuals previously infected by a different variant of the coronavirus, say a team of international scientists writing in Science.
SARS-CoV-2 had spread widely throughout India in the first wave, with initial results from the Indian Council of Medical Research finding one in five (21%) adults and one in four (25%) 10 to 17 year old adolescents had been infected. The figures were much higher in Indian megacities: by February 2021, over a half (56%) of individuals in Delhi were thought to have been infected.
Since the first case of COVID-19 was detected in Delhi in March 2020, the city had experienced multiple outbreaks, in June, September and November 2020. After reaching a high of almost 9,000 cases daily in November 2020, new cases steadily declined, with very few new infections between December 2020 and March 2021.
The situation reversed dramatically in April 2021, going from approximately 2,000 daily cases to 20,000 between 31 March and 16 April. This was accompanied by a rapid rise in hospitalisations and ICU admissions, severely stressing the healthcare system, with daily deaths spiking to levels three-fold higher than previous waves.
In research published today, an international team of scientists used genomic and epidemiological data, together with mathematical modelling, to study the outbreak. The work was led by the National Centre of Disease Control and the CSIR Institute of Genomics and Integrative Biology, India, with collaborators from the University of Cambridge and Imperial College London, UK, and the University of Copenhagen, Denmark.
To determine whether SARS-CoV-2 variants were responsible for the April 2021 outbreak in Delhi, the team sequenced and analysed viral samples from Delhi from the previous outbreak in November 2020 until June 2021. They found that the 2020 outbreaks in Delhi were unrelated to any variant of concern. The Alpha variant (B.1.1.7) was identified only occasionally, primarily in foreign travellers, until January 2021. The Alpha variant increased in Delhi to about 40% of cases in March 2021, before it was displaced by a rapid increase in the Delta variant (B.1.617.2) in April.

A new study has found that genes play a significant role in how our bodies respond to exercise and has identified a number of specific genes that influence the outcomes of different kinds of physical activity.
The research, published in the journal PLOS ONE and led by experts from the Cambridge Centre for Sport & Exercise Sciences at Anglia Ruskin University (ARU) in England, found that up to 72% of the difference between people in performance outcome following a specific exercise can be due to genetic differences.
The scientists analysed results from 3,012 adults aged between 18-55 — who had not previously taken part in exercise training — to determine how our genes can affect three important types of physical exercise.
Muscle strength, cardiovascular fitness, and anaerobic power are all key factors in shaping an individual’s fitness, wellbeing, and quality of life, and all participants showed improvements following their exercise training, but to varying degrees, even when performing exactly the same exercise training.
By combining data from 24 separate studies, the researchers discovered that genetic differences are responsible for 72% of the variation in outcomes for people following identical exercises designed to improve muscle strength.
Meanwhile, genetic variations caused 44% of the differences seen following cardiovascular fitness exercises, measured through V?O2max testing, and 10% of the differences in outcomes following exercises to improve anaerobic power, which is key for movement and agility. The remaining variations are influenced by other factors such as diet and nutrition, recovery, and injuries.

COVID-19 is not the flu. The disease caused by the novel SARS-CoV-2 virus is more transmissible, and deadlier, than most influenza epidemics we’ve encountered in our lifetimes, and scientists and physicians are still learning new things about the disease and its long-term effects. But COVID-19 and the flu do have a few things in common — they are both caused by viruses that primarily infect the upper respiratory system, and both are spread by droplets, fomites and contact.
For Ishanu Chattopadhyay, PhD, it therefore made sense to consider whether or not these similarities could be used to help predict the spread of COVID-19. Chattopadhyay, a professor of medicine at the University of Chicago, and his postdoctoral scholar Yi Huang, PhD, drew on their previous experience modeling epidemics and expertise in machine learning to analyze years of past influenza epidemics. The new risk measure they developed — denoted as the Universal Influenza-like Transmission (UnIT) score — has proven to be better at predicting weekly case count forecasts than the best models currently described. The work was published October 14 in PLoS Computational Biology.
“Even before COVID-19, we were working on modeling pandemics in general,” said Chattopadhyay, senior author on the paper. “There are multiple challenges presented by a pandemic. One is thinking about how a new strain emerges, and how the virus came to be — and the other is predicting the case counts. It’s very important to understand where the disease came from, but once it’s a pandemic, it’s important to be able to predict how it’s going to move through the population and through cities in order to develop public health policies.”
Chattopadhyay and Huang jumped at the chance to use their skills to help model the pandemic nationwide, and when they looked at the existing models, they noticed a glaring absence.
“Almost every approach you could think of was already being used,” said Chattopadhyay. “But one of the things it seemed like people should be looking at, but hadn’t, was: Is there a similarity between COVID-19 and seasonal influenza trends? These are different diseases, but there are similarities in how they are transmitted. One indication of this is how the measures we took to curb the spread of COVID-19 also curbed the spread of the flu. So, the question is, can we actually use patterns of how flu spreads in the U.S. to inform this modeling of how COVID-19 spreads?”
The researchers used 10 years of data on influenza hospitalizations nationwide to examine week-to-week trends in patients with the flu, allowing them to determine where infection clusters began and how they spread across the country each year. Using this data, they were able to produce the UnIT score. Combined with other variables known to be important in the spread of diseases like COVID-19, such as demographic details within a community, the model produced forecasting results that were more accurate on average than any of the other models listed on the CDC modeling hub.
“Our model is relatively simple, with far fewer variables than many of the other models being used to predict case counts and deaths. And yet we beat out those other, more complicated models on average over the entire pandemic timeline,” said first author Huang, now an associate research scientist at Brookhaven National Laboratory. “This shows us that we can learn something valuable from things we already know, like influenza epidemics, and can combine that knowledge of history with principles in statistics to come up with a new and meaningful way to predict something truly unknown.”
The results are important not just for understanding the ongoing COVID-19 pandemic, but can be extended to help predict future pandemics.
“If we see such accuracy here, then any respiratory illness that spreads in this manner — any similar pandemics we see in the future, we can probably apply this same tool,” said Chattopadhyay. “With expanding populations and environmental changes leading to more animal/human contact, many experts think that pandemic events like this may become more common. It’s important to be able to model how epidemics spread and what their path will look like, particularly when we are implementing interventions like vaccines and social distancing. Being able to extract information from the data we already have is incredibly useful — it makes us more prepared for the next pandemic.”
The algorithm developed by the research team has been shared on the CDC COVID-19 Forecast Hub, where it can be accessed by other scientists and is used as part of the CDC’s prediction modeling for COVID-19. The researchers hope that future research can incorporate global data trends to determine whether COVID-19 trends are similar around the world, or if there are differences based on population and climate.
The study, “Universal Risk Phenotype Of US Counties For Flu-like Transmission To Improve County-specific COVID-19 Incidence Forecasts,” was supported in part by the Defense Sciences Office of the Defense Advanced Research Projects Agency.
Story Source:
Materials provided by University of Chicago Medical Center. Original written by Alison Caldwell, PhD. Note: Content may be edited for style and length.

Studies have shown air pollution is a major risk factor for respiratory infection — the leading cause of death among children under five — but bad air’s specific impacts on developing bodies have remained somewhat of a mystery.
A Stanford-led study reveals a link between tiny airborne particles and child health in South Asia, a region beset with air pollution and more than 40 percent of global pneumonia cases. The analysis, published in Environmental Pollution, estimates the effect of increased particulate on child pneumonia hospitalizations is about twice as much as previously thought, and indicates a particular industry may play an outsized role in the problem.
The findings could help public health officials and policymakers better target emissions reduction programs to improve child health.
“Everybody wants to protect kids’ health,” said study lead author Allison Sherris, a postdoctoral research fellow in Earth system science at Stanford’s School of Earth, Energy & Environmental Sciences. “Now, we have evidence of a clear health benefit to children from reducing ambient PM2.5 emissions in Dhaka.”
For many of the 21 million residents of Dhaka, Bangladesh — the study’s focus area — air pollution is an all-too-regular part of life, especially in winter, when coal-burning brick kilns around the city operate. Of special concern is PM2.5, airborne particles 2.5 micrometers wide or smaller. The larger of these particles are about one-thirtieth the width of a human hair, small enough to inhale deep into the lungs.
Once inside the lungs, these particles can cause inflammation and impair the body’s ability to fight infection. But particles from different sources can have different shape, size and chemical composition, and it’s not clear what specific components of PM2.5 might be most harmful.

The study, published in the Journal of the American Heart Association (JAHA), suggests middle-aged and older women are being diagnosed with broken heart syndrome more frequently — up to 10 times more often — than younger women or men of any age.
The research also suggests that the rare condition has become more common, and the incidence has been rising steadily since well before the COVID-19 pandemic.
“Although the global COVID-19 pandemic has posed many challenges and stressors for women, our research suggests the increase in Takotsubo diagnoses was rising well before the public health outbreak,” said Susan Cheng, MD, MPH, MMSc, director of the Institute for Research on Healthy Aging in the Department of Cardiology at the Smidt Heart Institute and senior author of the study. “This study further validates the vital role the heart-brain connection plays in overall health, especially for women.”
What the Data Shows
Cheng and her research team used national hospital data collected from more than 135,000 women and men who were diagnosed with Takotsubo syndrome between 2006 and 2017. While confirming that women are diagnosed more frequently than men, the results also revealed that diagnoses have been increasing at least six to 10 times more rapidly for women ages 50 to 74 than for any other demographic.
Additional findings include: Of the 135,463 documented cases of Takotsubo cardiomyopathy, the annual incidence increased steadily in both sexes, with women contributing most cases (83.3%), especially those over 50. In particular, researchers observed a significantly greater increase in incidence among middle-aged women and older women, compared to younger women. For every additional diagnosis of Takotsubo in younger women — or men of all age groups — there were 10 additional cases diagnosed for middle-aged women and six additional diagnoses for older women.Prior to this study, researchers only knew that women are more prone than men to developing Takotsubo syndrome. This latest study is the first to ask whether there are age-based sex differences and if case rates may be changing over time.

An Israeli nation-wide study of more than 600,000 births, led by a researcher at Bar-Ilan University’s Azrieli Faculty of Medicine, reveals consistent links between high and low temperatures and low fetal birth weight, particularly exposure to heat during the second and third trimesters.
Published in the journal Environmental Health Perspectives, the study evaluates critical windows of susceptibility to extreme low and high temperatures during pregnancy and fetal growth in a nationwide cohort of 624,940 singleton term births in Israel during the period 2010-2014.
The study was led by Dr. Keren Agay-Shay, Director of the Health & Environment Research (HER) Lab at the Azrieli Faculty of Medicine of Bar-Ilan University, together with researchers from Bar-Ilan’s Department of Geography and Environment, the University of Haifa, Ben-Gurion University, the Israel Meteorological Service, and ISGlobal, Universitat Pompeu Fabra amd CIBER Epidemiologia y Salud Publica, all located in Spain.
The researchers collected, mapped, modeled and analyzed data on all recorded live births, residential information at the time of birth, and outdoor temperatures throughout the pregnancy. They subsequently divided the country into three climatic zones and performed a statistical analysis comparing all the data during the days, weeks and trimesters throughout the pregnancy.
Recorded live births
Birth certificate data for all live births was obtained from the National Birth and Birth Defect Registry in the Ministry of Health. These data include infant birth outcomes, parental sociodemographic characteristics, and residential address at the time of birth.

Lizards can regrow severed tails, making them the closest relative to humans that can regenerate a lost appendage. But in lieu of the original tail that includes a spinal column and nerves, the replacement structure is an imperfect cartilage tube. Now, for the first time, a USC-led study in Nature Communications describes how stem cells can help lizards regenerate better tails.
“This is one of the only cases where the regeneration of an appendage has been significantly improved through stem cell-based therapy in any reptile, bird or mammal, and it informs efforts to improve wound healing in humans,” said the study’s corresponding author Thomas Lozito, an assistant professor of orthopaedic surgery and stem cell biology and regenerative medicine at the Keck School of Medicine of USC.
These new and improved lizard tails exhibit what is known as “dorsoventral patterning” — meaning they have skeletal and nerve tissue on the upper or dorsal side, and cartilage tissue on the lower or ventral side.
“Lizards have been around for more than 250 million years, and in all that time no lizard has ever regrown a tail with dorsoventral patterning, until now,” said Lozito. “My lab has created the first regenerated lizard tails with patterned skeletons.”
To achieve this, the team of scientists from the medical schools at USC and the University of Pittsburgh analyzed how lizard tails form during adult regeneration, compared to embryonic development. In both cases, neural stem cells or NSCs — the stem cells that build the nervous system — play a central role.
Adult NSCs produce a molecular signal that blocks skeletal and nerve formation and encourages cartilage growth, effectively “ventralizing” both sides of the tail. This results in the cartilage tube typical of regenerated tails.