Statistical model to predict COVID-19 resistance

 Researchers from Johns Hopkins Medicine and The Johns Hopkins University have created and preliminarily tested what they believe may be one of the first models for predicting who has the highest probability of being resistant to COVID-19 in spite of exposure to SARS-CoV-2, the virus that causes it.

The study is reported online today in the journal PLOS ONE.

"If we can identify which people are naturally able to avoid infection by SARS-CoV-2, we may be able to learn -- in addition to societal and behavioral factors -- which genetic and environmental differences influence their defense against the virus," says lead study author Karen (Kai-Wen) Yang, a biomedical engineering graduate student in the Translational Informatics Research and Innovation Lab at The Johns Hopkins University. "That insight could lead to new preventive measures and more highly targeted treatments."

For its study, the research team set out to determine if a machine-learning statistical model could use health characteristics stored in electronic health records -- providing patient data such as comorbidities (other medical conditions) and prescribed medications -- as a means to pinpoint people with a natural ability to avoid SARS-CoV-2 infection. Those persons, says Yang, could then be studied to better understand the factors enabling their resistance.

A machine-learning model is a computer program or system that uses mathematical algorithms to find statistical patterns, and then apply the patterns moving forward. This gives such systems the ability to imitate human thinking and reasoning, and similar to the brain, learn over time.

"Using a machine-learning system to recognize complex patterns in large numbers of people with COVID-19 enabled another team of Johns Hopkins Medicine researchers in 2021 to predict the course of an individual patient's case and determine the likelihood that it would become severe," says co-senior study author Stuart Ray, M.D., vice chair of medicine for data integrity and analytics, and professor of medicine at the Johns Hopkins University School of Medicine. "Based on their success, our team wondered if the same approach also might be applied to predicting who could be exposed to SARS-CoV-2 in close quarters and still not get infected."

To demonstrate the model's ability to predict COVID-19 resistance, the researchers first acquired data from a clinical registry called the Johns Hopkins COVID-19 Precision Medicine Analytics Platform Registry (JH-CROWN). The registry contains information for patients seen within the Johns Hopkins Health System who have been suspected of, or confirmed as, having a SARS-CoV-2 infection.

For their resistance study, the researchers only included individuals who received a COVID-19 test between June 10, 2020, and Dec. 15, 2020, and who reported "potential exposure to the virus" as the reason for testing.

The ending date was the point at which large-scale COVID-19 vaccination efforts started in the United States. Choosing this date, the researchers say, enabled them to avoid the effects on their findings of vaccines preventing infection rather than natural resistance.

The 8,536 study participants who reported exposure as their reason for getting COVID tested were divided into two groups: those who did not share a residence (called a "household" in this study) with any COVID-19 patients or their residence had 10 or more patients; and those who shared a residence with 10 or fewer people, with at least one being a COVID-19 patient. The first group, with 8,476 of the participants, was designated as the Training and Testing Set, while the second group, called the Household Index (HHI) Set, had 60 members, and was used as a separate testing set.

Keeping the household number to 10 or fewer, the researchers say, excluded people living in apartment complexes, dormitories and other higher-density, multi-unit living areas where exposure to a particular person positive for SARS-CoV-2 would be less intense.

To identify patterns and cluster participants so that those naturally resistant to SARS-CoV-2 stand out, both study sets were analyzed using the Maximal-frequent All-confident pattern Selection Pattern-based Clustering (MASPC) algorithm. MASPC is specifically designed for electronic health record data analysis that combines patient demographic information (age, sex and race), the International Statistical Classification of Diseases and Related Health Problems (ICD) medical diagnostic codes relevant to each case, outpatient medication orders and the number of comorbidities (other diseases) present.

"We hypothesized that MASPC would enable us to cluster patients with similar patterns in their data to define them as resistant and non-resistant to SARS-CoV-2, and with the hope that the algorithm would learn with each analysis how to improve the accuracy and reliability of future assignments," says Ray. "This initial study using JH-CROWN data was conducted to give life to that hypothesis, a proof-of-concept trial of our statistical model to show that resistance to COVID-19 might be predictable based a patient's clinical and demographic profile."

"In the Training and Testing Set, we identified 56 patterns of ICD codes split into two groups: associated with resistance or not associated," Yang says. "Statistical analyses of how well these patterns differentiated between resistance and non-resistance yielded five patterns that did the best job for our small and localized [Baltimore-Washington, D.C., metroplex] study population to define who was most likely exposed to SARS-CoV-2."

"Looking for these patterns in HHI Set -- the individuals most likely to have been exposed to SARS-CoV-2 in close quarters -- and then statistically analyzing the results, our model's best performance was 0.61," says Ray. "Since a score of 0.5 shows only chance association between the prediction and reality, and 1 is 100% association, this shows the model has promise as a tool for identifying people with COVID-19 resistance who can be further studied," says Ray.

Limitations to the study, says Ray, include potential bias from self-reporting of COVID-19 exposure by participants, the small number of participants in the HHI group, the possibility that participants tested for SARS-CoV-2 using home kits or at facilities outside the Johns Hopkins system (and therefore, the tests were not recorded in the JH-CROWN database), and the short timeframe of the study itself. He adds that future trails using national patient data are needed to validate the model's ability.

Along with Yang and Ray, the members of the study team from Johns Hopkins Medicine and Johns Hopkins University are graduate and undergraduate students Yijia Chen, Jacob Desman, Kevin Gorman, Chloé Paris, Ilia Rattsev, Tony Wei and Rebecca Yoo; and faculty co-senior authors Joseph Greenstein and Casey Overby Taylor.

The study authors report no conflicts of interest.

Journal Reference:

1. Kai-Wen K. Yang, Chloé F. Paris, Kevin T. Gorman, Ilia Rattsev, Rebecca H. Yoo, Yijia Chen, Jacob M. Desman, Tony Y. Wei, Joseph L. Greenstein, Casey Overby Taylor, Stuart C. Ray. Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learning. PLOS ONE, 2023; 18 (2): e0278466 DOI: 10.1371/journal.pone.0278466

https://www.sciencedaily.com/releases/2023/02/230222210542.htm

Skipping breakfast may compromise the immune system

 Fasting may be detrimental to fighting off infection, and could lead to an increased risk of heart disease, according to a new study by the Icahn School of Medicine at Mount Sinai. The research, which focused on mouse models, is among the first to show that skipping meals triggers a response in the brain that negatively affects immune cells. The results that focus on breakfast were published in the February 23 issue of Immunity, and could lead to a better understanding of how chronic fasting may affect the body long term.

"There is a growing awareness that fasting is healthy, and there is indeed abundant evidence for the benefits of fasting. Our study provides a word of caution as it suggests that there may also be a cost to fasting that carries a health risk," says lead author Filip Swirski, PhD, Director of the Cardiovascular Research Institute at Icahn Mount Sinai. "This is a mechanistic study delving into some of the fundamental biology relevant to fasting. The study shows that there is a conversation between the nervous and immune systems."

Researchers aimed to better understand how fasting -- from a relatively short fast of only a few hours to a more severe fast of 24 hours -- affects the immune system. They analyzed two groups of mice. One group ate breakfast right after waking up (breakfast is their largest meal of the day), and the other group had no breakfast. Researchers collected blood samples in both groups when mice woke up (baseline), then four hours later, and eight hours later.

When examining the blood work, researchers noticed a distinct difference in the fasting group. Specifically, the researchers saw a difference in the number of monocytes, which are white blood cells that are made in the bone marrow and travel through the body, where they play many critical roles, from fighting infections, to heart disease, to cancer.

At baseline, all mice had the same amount of monocytes. But after four hours, monocytes in mice from the fasting group were dramatically affected. Researchers found 90 percent of these cells disappeared from the bloodstream, and the number further declined at eight hours. Meanwhile monocytes in the non-fasting group were unaffected.

In fasting mice, researchers discovered the monocytes traveled back to the bone marrow to hibernate. Concurrently, production of new cells in the bone marrow diminished. The monocytes in the bone marrow -- which typically have a short lifespan -- significantly changed. They survived longer as a consequence of staying in the bone marrow, and aged differently than the monocytes that stayed in the blood.

The researchers continued to fast mice for up to 24 hours, and then reintroduced food. The cells hiding in the bone marrow surged back into the bloodstream within a few hours. This surge led to heightened level of inflammation. Instead of protecting against infection, these altered monocytes were more inflammatory, making the body less resistant to fighting infection.

This study is among the first to make the connection between the brain and these immune cells during fasting. Researchers found that specific regions in the brain controlled the monocyte response during fasting. This study demonstrated that fasting elicits a stress response in the brain -- that's what makes people "hangry" (feeling hungry and angry) -- and this instantly triggers a large-scale migration of these white blood cells from the blood to the bone marrow, and then back to the bloodstream shortly after food is reintroduced.

Dr. Swirski emphasized that while there is also evidence of the metabolic benefits of fasting, this new study is a useful advance in the full understanding of the body's mechanisms.

"The study shows that, on the one hand, fasting reduces the number of circulating monocytes, which one might think is a good thing, as these cells are important components of inflammation. On the other hand, reintroduction of food creates a surge of monocytes flooding back to the blood, which can be problematic. Fasting, therefore regulates this pool in ways that are not always beneficial to the body's capacity to respond to a challenge such as an infection," explains Dr. Swirski. "Because these cells are so important to other diseases like heart disease or cancer, understanding how their function is controlled is critical."

This study was funded by grants from the National Institutes of Health and the Cure Alzheimer"s Fund,

Journal Reference:

1. Henrike Janssen, Florian Kahles, Dan Liu, Jeffrey Downey, Laura L. Koekkoek, Vladimir Roudko, Darwin D’Souza, Cameron S. McAlpine, Lennard Halle, Wolfram C. Poller, Christopher T. Chan, Shun He, John E. Mindur, Máté G. Kiss, Sumnima Singh, Atsushi Anzai, Yoshiko Iwamoto, Rainer H. Kohler, Kashish Chetal, Ruslan I. Sadreyev, Ralph Weissleder, Seunghee Kim-Schulze, Miriam Merad, Matthias Nahrendorf, Filip K. Swirski. Monocytes re-enter the bone marrow during fasting and alter the host response to infection. Immunity, 2023; DOI: 10.1016/j.immuni.2023.01.024

https://www.sciencedaily.com/releases/2023/02/230223132816.htm

Hip muscle exercises could help amputees improve functional mobility

 Strengthening hip muscles could be key to improve mobility in people with a below-the-knee amputation, new research has shown.

Amputation presents significant mobility challenges to millions of people worldwide. Studies show that only 5 per cent of people fitted with a prosthetic limb use it for more than half of their waking hours. These lower levels of activity lead to muscle wasting, or atrophy, in the remaining part of the leg.

A study by researchers at the University of Birmingham and Imperial College London has shown that the knee extensor muscles, just above the knee, are particularly at risk of atrophy because of the natural inclination to protect the soft tissue around the amputation site.

The team identified muscles around the hips, called hip abductors, which could be strengthened to provide effective compensation for weaknesses in the knee extensor muscles. They tested their hypothesis in amputees across three activities essential for independent living: walking, getting up out of a chair, and climbing stairs.

Dr Ziyun Ding, of the University of Birmingham, led the research. She said: "Even with a prosthesis, there will be reduced mobility in the amputated limb. People will also use their sound limb more, and try to protect the soft tissue at the amputation site. All these factors combine to reduce muscle volume in the amputated limb. In addition, putting additional load on the intact limb can lead to further problems like osteoarthritis.

"It's inevitable that people with an amputation will try to protect those soft tissue areas, but the hip abductor muscle, a major muscle in the leg, is not part of the stump knee interface. By strengthening this muscle, the leg will work better, without overloading the knee extensor muscle."

In the study, the team worked with a group of eight military personnel who had had a lower limb amputation after being injured in combat. Those taking part in the study were at least 12 months post-operation and had had their prosthesis for at least six months.

The researchers took high resolution MRI measurements to get an accurate picture of how the muscle volume in the amputated limb had changed. They also captured motion data from the three activities. In addition, researchers used computational modelling to understand the internal loading, such as muscle force and bone on bone contact, which cannot be measured using imaging techniques.

Through these techniques, the team was able to get a clear picture of the biomechanics involved in each activity. This led them to identify the hip abductor muscle as key to improving functional mobility by working to strengthen it post amputation. This could be via targeted exercise activities, or through electrical stimulation, using techniques similar to those already employed for stroke patients.

The research was funded by the Royal British Legion and undertaken with the support of the Royal British Legion Centre for Blast Injury Studies at Imperial College London. Further work will seek to increase the size of the cohort being studied, and also examine how factors such as age, type of amputation and cause might also affect muscle atrophy.

Journal Reference:

1. Ziyun Ding, David P. Henson, Biranavan Sivapuratharasu, Alison H. McGregor, Anthony M.J. Bull. The effect of muscle atrophy in people with unilateral transtibial amputation for three activities: Gait alone does not tell the whole story. Journal of Biomechanics, 2023; 149: 111484 DOI: 10.1016/j.jbiomech.2023.111484

https://www.sciencedaily.com/releases/2023/02/230221113037.htm

Expression of brain serotonin receptors across menstrual cycle provides clues on premenstrual dysphoric disorder

 A new study in Biological Psychiatry, published by Elsevier, explores the interplay between the serotonin system and estradiol in the brain, showing that the central nervous system in patients with premenstrual dysphoric disorder (PMDD) seems to increase serotonin transporter density from the periovulatory phase (when estradiol levels are high) to premenstrual cycle phase (when both estradiol and progesterone are decreasing). The findings have the potential to advance the clinical treatment of PMDD.

Premenstrual syndrome (PMS), which can include physical symptoms as well as depression and anxiety, affects about half of menstruating individuals a few days before the onset of menstruation. About 3 to 8% of people who menstruate experience PMDD, a far less recognized diagnosis. PMDD is also associated with mood swings, depression, and anxiety, but its symptoms are more severe and can last for up to two weeks at a time. The lifetime toll of PMDD is comparable to that for people with major depressive disorder.

Previous studies that compared fluctuations in ovarian hormones between women with PMDD and healthy women interestingly found no substantial differences, suggesting that dysregulated hormones in the periphery are not the underlying cause of the disorder. An alternative idea is that the brain's response to normal endogenous hormonal changes differs in patients with PMDD, although how that happens remains unclear. Treatment of PMDD with selective serotonin reuptake inhibitors, or SSRIs, results in remarkably rapid alleviation of symptoms -- on the order of hours or days, rather than weeks as in treatment for depression.

In the current study, led by Julia Sacher, MD, PhD, from the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, examined 30 patients with PMDD and 29 unaffected control women over the course of two menstrual cycles. The researchers used positron emission tomography (PET) imaging to visualize availability of the serotonin transporter protein in the brain throughout the cycle, reflecting short-term changes in its expression.

"We found a significant increase of serotonin transporter from periovulatory to premenstrual phase in patients with PMDD -- an 18% change in the midbrain, a brain region with the richest serotonin transporter expression. This increase was associated with the severity of depressed mood premenstrually," said Dr. Sacher.

Unexpectedly, Dr. Sacher and colleagues also found a decrease in midbrain serotonin transporter density in healthy women, which could point to a protective mechanism of the healthy female brain in the midst of a changing hormonal environment.

"Typically, it is assumed that serotonin transporter density is an individual trait, with only an approximately 10% change over a decade as the human brain ages, rather than a state that changes within shorter periods of time. However, studies in patients with seasonal affective disorder (SAD) show seasonal changes of serotonin transporter," Dr. Sacher explained. "Although the reports of serotonin transporter availability in depression have been mixed, this may be due to the heterogeneity of that disease. In more homogenous types of affective disorders, such PMDD or SAD, relatively rapid dynamics of serotonin transporter availability seem to play an important role."

John Krystal, MD, editor of Biological Psychiatry, said of the work, "This technically demanding study identifies a new potential mechanism contributing to negative premenstrual mood states in some women. It also supports the use of SSRIs to treat premenstrual dysphoric mood."

The findings provide evidence that individuals with PMDD experience short-term changes in serotonin transporter density throughout the menstrual cycle, which suggests that patients might benefit from taking SSRIs at specific times during the cycle to best target these changes.

Journal Reference:

1. Julia Sacher, Rachel G. Zsido, Claudia Barth, Franziska Zientek, Michael Rullmann, Julia Luthardt, Marianne Patt, Georg A. Becker, Pablo Rusjan, A. Veronica Witte, Ralf Regenthal, Abhay Koushik, Juergen Kratzsch, Beate Decker, Petra Jogschies, Arno Villringer, Swen Hesse, Osama Sabri. Increase in serotonin transporter binding in patients with premenstrual dysphoric disorder across the menstrual cycle: a case-control longitudinal neuroreceptor ligand PET imaging study. Biological Psychiatry, 2023; DOI: 10.1016/j.biopsych.2022.12.023

https://www.sciencedaily.com/releases/2023/02/230223132837.htm

'Digital markers near-perfect for predicting dementia in older drivers'

 Using ensemble learning techniques and longitudinal data from a large naturalistic driving study, researchers at Columbia University's Mailman School of Public Health, Fu Foundation School of Engineering and Applied Science, and Vagelos College of Physicians and Surgeons have developed a novel, interpretable and highly accurate algorithm for predicting mild cognitive impairment and dementia in older drivers. Digital markers refer to variables generated from data captured through recording devices in the real-world setting. These data could be processed to measure driving behavior, performance and tempo-spatial pattern in exceptional detail. The study is published in the journal Artificial Intelligence in Medicine.

The researchers used an interaction-based classification method for selecting predictive variables in the dataset. This learning model has achieved an accuracy of 96 percent in predicting mild cognitive impairment and dementia, outperforming traditional machine learning models such as logistic regression and random forests -- a statistical technique widely used in AI for classifying disease status. "Our new ensemble learning model based on digital markers and basic demographic characteristics could predict mild cognitive impairment and dementia in older drivers with excellent accuracy," said Sharon Di, associate professor of civil engineering and engineering mechanics at Columbia Engineering and the study's lead author.

The investigators constructed 200 variable modules using the naturalistic driving data on the driver, the vehicle and the environment captured by in-vehicle recording devices for 2977 drivers participating in the Longitudinal Research on Aging Drivers (LongROAD) project, a prospective cohort study conducted in five sites across the contiguous United States and sponsored by the AAA Foundation for Traffic Safety. At the time of enrollment, the participants were active drivers aged 65-79 years who were cognitively intact. Data used in this study came from the first three years of follow-up, spanning from August 2015 through March 2019. During the follow-up, 36 participants were diagnosed with mild cognitive impairment, 8 with Alzheimer's disease, and 17 with other or unspecified dementia.

The researchers performed a series of computer modeling experiments and found that the new ensemble learning model is 6-10 percent more accurate than random forests and logistic regression models in predicting mild cognitive impairment and dementia. The two most influential driving variables are the right to left turn ratio and the number of hard braking events (defined as maneuvers with deceleration rates ≥ 0.4 g). "With advancing age, drivers make relatively fewer left turns and more right turns because left turns are riskier," noted Di.

"About 85 percent of older adults in the United States are licensed drivers. As the most preferred mode of personal transportation, driving plays an important role in maintaining independence, self-control, social connection, and quality of life. Safely operating a car requires essential cognitive and physical functions. Our study indicates that digital markers embedded in routinely collected driving data can be used through innovative machine learning techniques as valid and reliable artificial intelligence for predicting mild cognitive impairment and dementia," said Guohua Li, MD, DrPH, professor of epidemiology and anesthesiology at Columbia Mailman School of Public Health and Vagelos College of Physicians and Surgeons, and senior author. "Early detection of mild cognitive impairment and dementia could lead to timely evaluation, diagnosis, and interventions, which are especially salient in the absence of effective therapeutics."

Co-authors are Carolyn DiGuiseppi, Colorado School of Public Health; David W. Eby, University of Michigan Transportation Research Institute; Linda Hill, University of California San Diego School of Public Health; Thelma J. Mielenz, Columbia Mailman School of Public Health; David Strogatz, Bassett Research Institute; and Minjae Kim, Columbia Vagelos College of Physicians and Surgeons.

The study was supported in part by the AAA Foundation for Traffic Safety.

Journal Reference:

1. Xuan Di, Yiqiao Yin, Yongjie Fu, Zhaobin Mo, Shaw-Hwa Lo, Carolyn DiGuiseppi, David W. Eby, Linda Hill, Thelma J. Mielenz, David Strogatz, Minjae Kim, Guohua Li. Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score. Artificial Intelligence in Medicine, 2023; 102510 DOI: 10.1016/j.artmed.2023.102510

https://www.sciencedaily.com/releases/2023/02/230223132845.htm

Pentagon panel says military bases should stop selling guns to troops under 25 to prevent suicides

 The military should stop selling guns to troops under the age of 25, in a bid to help combat suicide, an independent Pentagon panel found.

The Suicide Prevention and Response Independent Review Committee (SPRIRC) was formed in March 2020 to address the rising rates of suicides by members of the armed forces.

“On DoD property, raise the minimum age for purchasing firearms and ammunition to 25 years,” the committee wrote in their report, which was released Friday, Fox News reported.

SPRIRC called the suggestion “high priority” and said the situation was something that “must change.”

https://nypost.com/2023/02/25/pentagon-panel-says-to-stop-selling-guns-to-troops-under-the-age-of-25-to-fight-suicides/

Yellen says World Bank nominee's credentials will overcome selection criticism

 U.S. Treasury Secretary Janet Yellen said on Saturday that she believes the strong qualifications of the U.S. nominee to lead the World Bank, ex-Mastercard CEO Ajay Banga, will overcome any criticism of the selection process.

In an interview, Yellen affirmed her support for the longstanding tradition of the United States choosing the World Bank's leader and Europe choosing the head of the International Monetary Fund.

But she said that privilege comes with a responsibility to "nominate the strongest possible candidate" for the job.

"We've taken this very seriously and tried to identify a candidate that we think brings the right skill set to this job," Yellen said. "And we hope that our candidate will be broadly accepted in both lending countries and borrowing countries."

Yellen said she was pleased so far with positive reviews from G20 finance officials for Banga, 63, an Indian-born U.S. citizen who has won accolades for his work transforming Mastercard and working to lift people in developing countries out of poverty.

But the swiftness with which President Joe Biden nominated Banga, in a surprise pick immediately after the World Bank's board began accepting nominations on Thursday, drew criticism from some non-profit groups, climate and development professionals that the United States never wanted an open contest for the job and sought quickly to deter challengers.

As the World Bank's largest shareholder with 16.35% of its voting power, the United States wields strong influence over the bank's policies, and the lender's president works closely with the Treasury Department.

"So much for a merit-based transparent process with female candidates strongly encouraged," said Claire Healy, Washington director for the E3G climate think tank, referring to the board's selection process announcement.

"Time is short and the stakes are high, so concerns about the process will likely be set aside to get the reforms done," Healy added.

Yellen is pressing the World Bank to refine a package of sweeping reforms aimed at vastly expanding its lending resource and mission to tackle climate change and other global challenges.

Banga will face a tough slate of issues around the institution's finances and capital structure from the start — thorny problems he must address as he reshapes the bank into a force for combating climate change on top of its traditional role as a poverty fighter.

"There's broad agreement that we need to mobilize private capital," Yellen said. "This is an individual who has a better chance of being able to accomplish that than anyone else I can honestly think of."

She added that his background "really is quite different" from past World Bank presidents, who were often picked from positions in government service.

"This is somebody who grew up in emerging markets, spent most of his career working in Africa, the Middle East, Asia, really deeply understands and has lived in countries that face development challenges," she said.

https://finance.yahoo.com/news/yellen-says-world-bank-nominees-170639889.html