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.

MIT engineers are hoping to help doctors tailor treatments to patients’ specific heart form and function, with a custom robotic heart. The team has developed a procedure to 3D print a soft and flexible replica of a patient’s heart. They can then control the replica’s action to mimic that patient’s blood-pumping ability.

The procedure involves first converting medical images of a patient’s heart into a three-dimensional computer model, which the researchers can then 3D print using a polymer-based ink. The result is a soft, flexible shell in the exact shape of the patient’s own heart. The team can also use this approach to print a patient’s aorta — the major artery that carries blood out of the heart to the rest of the body.

To mimic the heart’s pumping action, the team has fabricated sleeves similar to blood pressure cuffs that wrap around a printed heart and aorta. The underside of each sleeve resembles precisely patterned bubble wrap. When the sleeve is connected to a pneumatic system, researchers can tune the outflowing air to rhythmically inflate the sleeve’s bubbles and contract the heart, mimicking its pumping action.

The researchers can also inflate a separate sleeve surrounding a printed aorta to constrict the vessel. This constriction, they say, can be tuned to mimic aortic stenosis — a condition in which the aortic valve narrows, causing the heart to work harder to force blood through the body.

Doctors commonly treat aortic stenosis by surgically implanting a synthetic valve designed to widen the aorta’s natural valve. In the future, the team says that doctors could potentially use their new procedure to first print a patient’s heart and aorta, then implant a variety of valves into the printed model to see which design results in the best function and fit for that particular patient. The heart replicas could also be used by research labs and the medical device industry as realistic platforms for testing therapies for various types of heart disease.

“All hearts are different,” says Luca Rosalia, a graduate student in the MIT-Harvard Program in Health Sciences and Technology. “There are massive variations, especially when patients are sick. The advantage of our system is that we can recreate not just the form of a patient’s heart, but also its function in both physiology and disease.”

Rosalia and his colleagues report their results in a study appearing today in Science Robotics. MIT co-authors include Caglar Ozturk, Debkalpa Goswami, Jean Bonnemain, Sophie Wang, and Ellen Roche, along with Benjamin Bonner of Massachusetts General Hospital, James Weaver of Harvard University, and Christopher Nguyen, Rishi Puri, and Samir Kapadia at the Cleveland Clinic in Ohio.

Print and pump

In January 2020, team members, led by mechanical engineering professor Ellen Roche, developed a “biorobotic hybrid heart” — a general replica of a heart, made from synthetic muscle containing small, inflatable cylinders, which they could control to mimic the contractions of a real beating heart.

Shortly after those efforts, the Covid-19 pandemic forced Roche’s lab, along with most others on campus, to temporarily close. Undeterred, Rosalia continued tweaking the heart-pumping design at home.

“I recreated the whole system in my dorm room that March,” Rosalia recalls.

Months later, the lab reopened, and the team continued where it left off, working to improve the control of the heart-pumping sleeve, which they tested in animal and computational models. They then expanded their approach to develop sleeves and heart replicas that are specific to individual patients. For this, they turned to 3D printing.

“There is a lot of interest in the medical field in using 3D printing technology to accurately recreate patient anatomy for use in preprocedural planning and training,” notes Wang, who is a vascular surgery resident at Beth Israel Deaconess Medical Center in Boston.

An inclusive design

In the new study, the team took advantage of 3D printing to produce custom replicas of actual patients’ hearts. They used a polymer-based ink that, once printed and cured, can squeeze and stretch, similarly to a real beating heart.

As their source material, the researchers used medical scans of 15 patients diagnosed with aortic stenosis. The team converted each patient’s images into a three-dimensional computer model of the patient’s left ventricle (the main pumping chamber of the heart) and aorta. They fed this model into a 3D printer to generate a soft, anatomically accurate shell of both the ventricle and vessel.

The team also fabricated sleeves to wrap around the printed forms. They tailored each sleeve’s pockets such that, when wrapped around their respective forms and connected to a small air pumping system, the sleeves could be tuned separately to realistically contract and constrict the printed models.

The researchers showed that for each model heart, they could accurately recreate the same heart-pumping pressures and flows that were previously measured in each respective patient.

“Being able to match the patients’ flows and pressures was very encouraging,” Roche says. “We’re not only printing the heart’s anatomy, but also replicating its mechanics and physiology. That’s the part that we get excited about.”

Going a step further, the team aimed to replicate some of the interventions that a handful of the patients underwent, to see whether the printed heart and vessel responded in the same way. Some patients had received valve implants designed to widen the aorta. Roche and her colleagues implanted similar valves in the printed aortas modeled after each patient. When they activated the printed heart to pump, they observed that the implanted valve produced similarly improved flows as in actual patients following their surgical implants.

Finally, the team used an actuated printed heart to compare implants of different sizes, to see which would result in the best fit and flow — something they envision clinicians could potentially do for their patients in the future.

“Patients would get their imaging done, which they do anyway, and we would use that to make this system, ideally within the day,” says co-author Nyugen. “Once it’s up and running, clinicians could test different valve types and sizes and see which works best, then use that to implant.”

Ultimately, Roche says the patient-specific replicas could help develop and identify ideal treatments for individuals with unique and challenging cardiac geometries.

“Designing inclusively for a large range of anatomies, and testing interventions across this range, may increase the addressable target population for minimally invasive procedures,” Roche says.

This research was supported, in part, by the National Science Foundation, the National Institutes of Health, and the National Heart Lung Blood Institute.

Scientific research in recent decades has confirmed that major changes in the global carbon cycle caused significant changes in the Earth’s atmosphere and oceans 135 million years ago, during the early Cretaceous Period. A range of questions remain about the details of climate change dynamics in that era. This new research, involving wide-ranging chemical and radioactivity-based analyses of rock strata in Utah’s Cedar Mountain Formation, helps fill in that knowledge gap by confirming that such carbon-cycle shifts were recorded on land in ancient North America.

The carbon cycle is one of Earth’s fundamental environmental phenomena, involving the ongoing transfer of carbon among the atmosphere, oceans and living organisms, as well as soils, sediments and rocks in the solid Earth. The cycle is crucial to biological processes for living things on land and sea. When large-scale changes in the cycle occur, they can produce major shifts in climate and the oceans’ biological conditions.

“We’re studying how the global carbon cycle has functioned in the past, how changes are recorded in the sedimentary rocks around the world,” said Joeckel, a professor in the School of Natural Resources at Nebraska. The environmental phenomena he and his colleagues analyzed “are exactly the kind of things we’re talking about today, as people increase the input of carbon dioxide into the atmosphere at a much-accelerated rate by burning fossil fuels.”

Joeckel, the Nebraska state geologist, headed the Utah fieldwork and organized the study, published as a peer-reviewed paper in a special February issue of the journal Geosciences.

Over the past two decades, Joeckel and several colleagues have studied a range of geological aspects of southeastern Utah’s Cedar Mountain Formation, known for its exceptional dinosaur fossils. The steep, 150-foot slope where the scientists conducted their recent carbon-cycle research is known as Utahraptor Ridge, named for the discovery there of raptors, the ferocious bipedal predators familiar to moviegoers through the Jurassic Park movies.

To determine whether carbon-cycle changes have occurred, scientists analyze the minute amounts of organic carbon held in rocks for major changes in two carbon isotopes. Carbon cycles are evident if scientists find that significant increases and decreases in isotope ratios occurred over time. Joeckel and his colleagues found evidence for two distinct peaks in a curve representing changes in a key isotope ratio during the early Cretaceous period. This discovery indicates that the Utah site, though on land, experienced the same major carbon-cycle change recorded in marine sedimentary rocks in Europe.

Many geologists refer to this ancient carbon-cycle phenomenon as the “Weissert Event,” which was driven by large, sustained volcanic eruptions in the Southern Hemisphere. These eruptions greatly increased carbon dioxide levels in the atmosphere, producing significant greenhouse climate effects over a prolonged time.

A central uncertainty has been whether carbon-cycle changes recorded in sediments in ancient oceans were also recorded by sediments on land. The work by Joeckel and his colleagues strongly suggests that happened.

Analyzing the Utah samples for these carbon isotope changes was a challenging scientific endeavor, said Joeckel, director of Nebraska’s state Conservation and Survey Division, which conducts a wide array of geological, geographic, water and soil research in the state and beyond.

“We’re talking about a minute amount of organic carbon that has to be very laboriously isolated from fist-sized samples of sedimentary rock,” he said. Such complex work is “like having to go through a whole pile of phone books by hand just to get to the point where you can generate numbers, and you have to generate a lot of them. There’s a lot of hard work in the laboratory that needs to be done.”

To accurately date the carbon-cycle changes, the scientists also analyzed microscopic crystals of the mineral zircon. These crystals “are important because they are a way to actually put an age date on the rocks,” Joeckel said. The volcanically produced zircon crystals are “nearly indestructible treasure troves of information that are spread all over the place” after an eruption.

Joeckel’s coauthors on the paper are Celina Suarez and Garrett Hatzell of the University of Arkansas; Noah McLean, Andreas Möller, Marina Suarez and Joseph Andrew of the University of Kansas; Gregory Ludvigson and Spencer Kiessling of the Kansas Geological Survey; and James Kirkland of the Utah Geological Survey.

The project, Joeckel said, illustrates how geology as a discipline continues to reveal new insights.

“In some ways, the past may be the key to the present, rather than vice versa, as geologists traditionally posited,” he said. “The better we understand the ancient carbon cycle and ancient global change, the more we can understand what happens today.”