A research team from Johns Hopkins Medicine and Johns Hopkins University has developed a machine-learning (ML) tool capable of predicting who has the highest probability of being naturally resistant to COVID-19 infection despite being exposed to SARS-CoV-2, the virus that causes it.
The study, published this week in PLOS One, aims to better understand the factors that influence COVID-19 resistance.
“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,” said Karen (Kai-Wen) Yang, lead study author and a biomedical engineering graduate student in the Translational Informatics Research and Innovation Lab at Johns Hopkins University, in the press release. “That insight could lead to new preventive measures and more highly targeted treatments.”
To develop their model, the researchers gathered data from the Johns Hopkins COVID-19 Precision Medicine Analytics Platform Registry (JH-CROWN), which contains information for patients with a suspected or confirmed SARS-CoV-2 infection seen within the Johns Hopkins Health System, the press release states.
From this information, the research team selected patients who had received a COVID-19 test between June 10, 2020, and Dec. 15, 2020, and reported “potential exposure to the virus” as the reason for testing. Dec. 15 was chosen as the end date because it was just before large-scale COVID-19 vaccination efforts began in the US, which allowed researchers to avoid the confounding effects of vaccines, rather than natural resistance, on preventing COVID-19 infection.
The final cohort comprised 8,536 study participants who were divided into two groups: those who either did not share a household with any COVID-19 patients or whose household 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, consisting of 8,476 participants, served as the training and initial testing test, while the remaining 60 participants were grouped into a Household Index (HHI) Set, which served as a separate testing set.
EHR data from the cohort was analyzed using the Maximal-frequent All-confident pattern Selection Pattern-based Clustering (MASPC) algorithm, which combines patient demographic information, the relevant International Statistical Classification of Diseases and Related Health Problems (ICD) medical diagnostic codes, outpatient medication orders, and the number of comorbidities present for each patient.
“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,” explained co-senior study author Stuart Ray, MD, vice chair of medicine for data integrity and analytics, and professor of medicine at the Johns Hopkins University School of Medicine, in the press release. “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 [on] a patient’s clinical and demographic profile.”
The researchers were able to identify 56 of these patterns, five of which captured who was most likely exposed to the virus.
“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.”
The researchers noted that the study has multiple limitations, such as potential bias from the self-reporting of COVID-19 exposure by participants, the small number of participants in the HHI group, the short timeframe of the study, and the possibility that participants may have taken tests for SARS-CoV-2 using home kits or at facilities outside the Johns Hopkins system, which would not have been recorded in the JH-CROWN database.
News
Baffling Scientists for Centuries: New Study Unravels Mystery of Static Electricity
ISTA physicists demonstrate that contact electrification depends on the contact history of materials. For centuries, static electricity has intrigued and perplexed scientists. Now, researchers from the Waitukaitis group at the Institute of Science and [...]
Tumor “Stickiness” – Scientists Develop Potential New Way To Predict Cancer’s Spread
UC San Diego researchers have developed a device that predicts breast cancer aggressiveness by measuring tumor cell adhesion. Weakly adherent cells indicate a higher risk of metastasis, especially in early-stage DCIS. This innovation could [...]
Scientists Just Watched Atoms Move for the First Time Using AI
Scientists have developed a groundbreaking AI-driven technique that reveals the hidden movements of nanoparticles, essential in materials science, pharmaceuticals, and electronics. By integrating artificial intelligence with electron microscopy, researchers can now visualize atomic-level changes that were [...]
Scientists Sound Alarm: “Safe” Antibiotic Has Led to an Almost Untreatable Superbug
A recent study reveals that an antibiotic used for liver disease patients may increase their risk of contracting a dangerous superbug. An international team of researchers has discovered that rifaximin, a commonly prescribed antibiotic [...]
Scientists Discover Natural Compound That Stops Cancer Progression
A discovery led by OHSU was made possible by years of study conducted by University of Portland undergraduates. Scientists have discovered a natural compound that can halt a key process involved in the progression [...]
Scientists Just Discovered an RNA That Repairs DNA Damage – And It’s a Game-Changer
Our DNA is constantly under threat — from cell division errors to external factors like sunlight and smoking. Fortunately, cells have intricate repair mechanisms to counteract this damage. Scientists have uncovered a surprising role played by [...]
What Scientists Just Discovered About COVID-19’s Hidden Death Toll
COVID-19 didn’t just claim lives directly—it reshaped mortality patterns worldwide. A major international study found that life expectancy plummeted across most of the 24 analyzed countries, with additional deaths from cardiovascular disease, substance abuse, and mental [...]
Self-Propelled Nanoparticles Improve Immunotherapy for Non-Invasive Bladder Cancer
A study led by Pohang University of Science and Technology (POSTECH) and the Institute for Bioengineering of Catalonia (IBEC) in South Korea details the creation of urea-powered nanomotors that enhance immunotherapy for bladder cancer. The nanomotors [...]
Scientists Develop New System That Produces Drinking Water From Thin Air
UT Austin researchers have developed a biodegradable, biomass-based hydrogel that efficiently extracts drinkable water from the air, offering a scalable, sustainable solution for water access in off-grid communities, emergency relief, and agriculture. Discarded food [...]
AI Unveils Hidden Nanoparticles – A Breakthrough in Early Disease Detection
Deep Nanometry (DNM) is an innovative technique combining high-speed optical detection with AI-driven noise reduction, allowing researchers to find rare nanoparticles like extracellular vesicles (EVs). Since EVs play a role in disease detection, DNM [...]
Inhalable nanoparticles could help treat chronic lung disease
Nanoparticles designed to release antibiotics deep inside the lungs reduced inflammation and improved lung function in mice with symptoms of chronic obstructive pulmonary disease By Grace Wade Delivering medication to the lungs with inhalable nanoparticles [...]
New MRI Study Uncovers Hidden Lung Abnormalities in Children With Long COVID
Long COVID is more than just lingering symptoms—it may have a hidden biological basis that standard medical tests fail to detect. A groundbreaking study using advanced MRI technology has uncovered significant lung abnormalities in [...]
AI Struggles with Abstract Thought: Study Reveals GPT-4’s Limits
While GPT-4 performs well in structured reasoning tasks, a new study shows that its ability to adapt to variations is weak—suggesting AI still lacks true abstract understanding and flexibility in decision-making. Artificial Intelligence (AI), [...]
Turning Off Nerve Signals: Scientists Develop Promising New Pancreatic Cancer Treatment
Pancreatic cancer reprograms nerve cells to fuel its growth, but blocking these connections can shrink tumors and boost treatment effectiveness. Pancreatic cancer is closely linked to the nervous system, according to researchers from the [...]
New human antibody shows promise for Ebola virus treatment
New research led by scientists at La Jolla Institute for Immunology (LJI) reveals the workings of a human antibody called mAb 3A6, which may prove to be an important component for Ebola virus therapeutics. [...]
Early Alzheimer’s Detection Test – Years Before Symptoms Appear
A new biomarker test can detect early-stage tau protein clumping up to a decade before it appears on brain scans, improving early Alzheimer’s diagnosis. Unlike amyloid-beta, tau neurofibrillary tangles are directly linked to cognitive decline. Years [...]
