Tag Archives: #COVID-19

Scent Detection Dogs Can Identify Individuals Infected with COVID-19 (Medicine)

The use of trained scent detection dogs to detect volatile organic compounds associated with the COVID virus shows promise in early studies

In a recent article in the Journal of Osteopathic Medicine, authors gathered previously published research to summarize current thinking on the feasibility and efficacy of using scent detection dogs to screen for the COVID-19 virus. The researchers report that sensitivity, specificity, and overall success rates reported by the canine scent detection studies are comparable or better than the standard RT-PCR and antigen testing procedures.

These findings indicate scent detection dogs can likely be used to effectively screen and identify individuals infected with the COVID-19 virus in hospitals, senior care facilities, schools, universities, airports, and even large public gatherings for sporting events and concerts.

“Accurate and rapid screening of individuals who may be carriers, symptomatic or asymptotic, of the COVID-19 virus will remain important for slowing and limiting the spread of infection,” said Tommy Dickey, PhD, professor, University of California, Santa Barbara. “These preliminary studies suggest the use of medical scent detection dogs offers a promising approach.”

Documented success detecting disease

Dogs can sense a broad range of molecules with extremely small concentrations: 1 part in a quadrillion compared with 1 part in 1 billion for humans. This capability is used for search and for identification of diseases with their individual chemistries and odors.

Using inhaled air molecules and particulates, dogs can detect odorous human molecules (volatile organic compounds, or VOCs) that originate from flaked off skin or hair cells, blood, breath, saliva, sweat, tears, nasal mucous, urine, semen, or feces. Since smells linger, dogs can maintain a historical library of the smells of complex molecules.

“The science behind and efficacy of using dogs in detecting medical conditions and diseases such as cancers, diabetes, malaria, Parkinson’s disease, and more has been documented,” said Heather Junqueira. “These new studies provide support for additional research to determine their ability to detect COVID-19 at scale.”

The qualified studies

For their review, the authors assessed four recent studies analyzing the success of scent detecting dogs at identifying VOCs associated with COVID-19. First, they described the work of a team of collaborating researchers from France and Lebanon, who tested with 8 dogs that had previously been trained to detect both explosives and colon cancer.

These dogs were independently presented with cotton or wool gauze samples that had been soaked with sweat from one of 198 human armpits of patients in different hospitals. While the COVID-19 virus does not itself have a smell, researchers hypothesized that the resulting infection generates metabolic changes, which cause the release of a distinctive type of sweat odor that can be detected by a dog.

The dogs were trained to only sit in front of a COVID-19-positive sample contained in a box with a sample canister. After four days of training using COVID-19 samples, the success rate for the dogs ranged between 83 and 100%.

Saliva or tracheobronchial secretions

In another study described by the authors, a research team in Germany conducted a randomized, double-blinded, controlled pilot study to determine whether previously trained scent dogs could successfully detect the presence of the COVID-19 virus. Dogs were trained over 1 week to detect the COVID-19 virus in samples of saliva or tracheobronchial secretions collected from infected patients.

Each dog, its handler, and the person observing the study were blindfolded. The number and duration of each dogs’ “nose dips” into the scent holes, along with the location of the positive and negative samples, were automatically recorded and verified using time-stamped video analysis, which automated the process and reduced trainer interference.

The results, derived from 1,012 automated sample presentations, showed an overall average detection rate of 94%: 157 correct indications of positive, 792 correct rejections of negative, 33 false positives, and 30 false negative indications. Interestingly, the team reported no notable difference in detection ability between the use of sample saliva and sample tracheal secretion.

While that pilot study had limitations—in particular, the positive samples came only from severely affected, hospitalized COVID-19 patients and the negative samples were from healthy individuals with no indications of respiratory infections—the authors of the present study found those results encouraging.

Support for additional research

A third study done by a team in Colombia tested 6 trained scent dogs of various and mixed breeds to develop a screening method for detecting COVID-19 in individuals who may be asymptomatic, pre-symptomatic, or symptomatic.

The researchers developed a device to safely expose the scent-trained dogs to VOC samples collected from respiratory secretions of COVID-19-positive patients, and their detailed study was conducted in 3 phases, with the third phase ongoing.

“Of the 6,000 samples, the dogs’ performances [in that study] resulted in a sensitivity of 95.5% and a selectivity of 99.6%,” said Dickey. “The high success rates among different types of dogs suggests a range of breeds or mixed breeds may be trained to effectively screen for COVID-19.”

Challenges remain

“The results of recently reported and ongoing research are encouraging; however, there remain challenges to be considered before broad-scale implementation of scent detecting dogs to identify and screen for COVID-19,” said Junqueira. “Nonetheless, the research supports the use of scent detection dogs for pilot COVID-19 screening studies in venues such as airports and sporting events.”

The authors hope that their research review, which presented recent information and perspectives on the potential for broad application of trained scent dogs for screening of COVID-19-infected individuals, can be used to assist in the development of future studies and implementation of screening programs to benefit preventative medical research.

Featured image: Author Tommy Dickey, PhD, demonstrates how he conducts simple canine scent detection training with his own dogs. These dogs were not part of the reviewed research. © Tommy Dickey, PhD


Reference: Dickey T, Junqueira H. Toward the use of medical scent detection dogs for COVID-19 screening. J Am Osteopath Assoc 2021;121(2):141–148. doi: https://doi.org/10.1515/jom-2020-0222.


Provided by American Osteopathic Association

Protein Sequences Provide Clues to How SARS-CoV-2 Infects Cells (Medicine)

Researchers at EMBL Heidelberg have identified sequences in human proteins that might be used by SARS-CoV-2 to infect cells. They have discovered that the virus might hijack certain cellular processes, and they discuss potentially relevant drugs for treating COVID-19.

SARS-CoV-2 might hijack cellular processes

In the early days of the COVID-19 pandemic, it was established that SARS-CoV-2 infects cells by binding to the human protein ACE2, which plays a role in regulating blood pressure. But ACE2 is almost absent in human lung cells, so how can the lungs be one of the most affected organs in COVID-19? This gave researchers a hint that ACE2 might be more than just a blood pressure regulator, and might not be the only player in the SARS-CoV-2 infection mechanism.

EMBL’s Gibson team, in collaboration with Lucía Chemes at Universidad Nacional de San Martín in Buenos Aires and partners from Merck KGaA Darmstadt and University College Dublin, analysed sequences of ACE2 and other human proteins involved in SARS-CoV-2 infection, such as a class of proteins called integrins. They focused on short strings of amino acids called short linear motifs (SLiMs), which are involved in transmitting information between the inside and outside of cells. Quick identification and comparison of SLiMs was possible thanks to the Eukaryotic Linear Motif (ELM) resource, the largest curated SLiMs database, which the team and collaborators have been developing for 20 years. 

They saw that ACE2 and several integrins contain SLiMs that are probably involved in endocytosis and autophagy – cellular processes of uptake and disposal of substances, respectively. This result suggests previously unknown roles of ACE2 and integrins in cell physiology. “If SARS-CoV-2 targets proteins involved in endocytosis and autophagy, it means these processes might be hijacked by the virus during infection,” says Bálint Mészáros, a postdoc in the Gibson team and the first author of the study.

Several findings were experimentally verified by Ylva Ivarsson and her group at Uppsala University in Sweden. They confirmed the predicted protein interactions, and verified that these interactions are regulated by the naturally occurring addition of ions containing phosphorus. “Ylva Ivarsson was the best person we knew of to test these predictions. We were delighted she agreed to join this project,“ says EMBL team leader Toby Gibson. Ylva Ivarsson is equally enthusiastic. “Switching our work to SARS-CoV-2-related research helped us keep the spirit up in the lab during the pandemic,” she adds.

Potential drugs for COVID-19

The findings might lead to new therapeutic approaches for COVID-19. “SLiMs could ‘switch’ to turn viral entry signals on or off. This means that if we can find a way to reverse these switches using drugs, this might stop coronavirus from entering cells,” says senior author Lucía Chemes. 

Together with a collaborator from Merck KGaA Darmstadt, the team gathered a list of existing drugs that interfere with endocytosis and autophagy. The list includes some surprising candidates, such as the antipsychotic chlorpromazine. “If clinical trials prove some of these drugs to work against COVID-19, this could be a game changer,” says Manjeet Kumar, a bioinformatics scientist in the Gibson team and a senior author in the study.

Highlights, challenges, and collaboration during the pandemic

This research was initiated at the beginning of the first lockdown in Germany in spring 2020. The project was an opportunity to strengthen relations between scientists across continents. “Toby and I have had a collaboration since 2012, when Argentina became an associate member of EMBL. Our previous experience enabled us now to work together on SARS-CoV-2,” says Lucía Chemes. 

Working under lockdown conditions was not always easy. For example, one of the co-authors of the study, Elizabeth Martínez Perez from Leloir Institute in Argentina, was unable to return from her secondment in the Gibson team at EMBL Heidelberg. 

At the same time, Manjeet Kumar had to adjust to working from home when his children were around. “I got kind support from our landlady to work in the attic of the building, though the internet signal wasn’t reaching there! Eventually I bought a 35 metre internet cable and connected it to the attic. Once this was set, I got momentum in the project,” he recalls.

For many, working on SARS-CoV-2 research was an inspiring experience. “We wanted to contribute to combating COVID-19. This gave us a common aim,” says Toby Gibson. Bálint Mészáros agrees. “It’s strange, thrilling, and a bit unsettling breaking new ground in the COVID-19 field,” he says. “As researchers we’re enthusiastic about figuring out bits of the biology, but at the same time we’re thoroughly excited to work on such an important topic.”

Featured image: The analysis of protein sequences allows scientists to predict protein interactions. They can then identify potential new players in the coronavirus infection process. Rayne Zaayman-Gallant/EMBL


Source articles

(1) Bálint Mészáros et al. Short linear motif candidates in the cell entry system used by SARS-CoV-2 and their potential therapeutic implications. Science Signalling, published on 12 January 2021 DOI: 10.1126/scisignal.abd0334 (2) Johanna Kliche et al. Cytoplasmic short linear motifs in ACE2 and integrin beta3 link SARS-CoV-2 host cell receptors to endocytosis and autophagy. Science Signalling, published on 12 January 2021 DOI: 10.1126/scisignal.abf1117


Provided by EMBL

Advanced Simulations Reveal How Air Conditioning Spreads COVID-19 Aerosols (Medicine)

The detailed physical processes and pathways involved in the transmission of COVID-19 are still not well understood. Researchers decided to use advanced computational fluid dynamics tools on supercomputers to deepen understanding of transmission and provide a quantitative assessment of how different environmental factors influence transmission pathways and airborne infection risk.

A restaurant outbreak in China was widely reported as strong evidence of airflow-induced transmission of COVID-19. But it lacked a detailed investigation about exactly how transmission occurred.

Why did some people get infected while others within the same area did not? What specific role did ventilation and air conditioning play in disease transmission? Exploring these questions can help develop more pinpointed preventative measures to improve our safety.

In Physics of Fluids, from AIP Publishing, Jiarong Hong and colleagues at the University of Minnesota report using advanced simulation methods to capture the complex flows that occur when the cold airflow from air conditioners interacts with the hot plume from a dining table and the transport of virus-loading particles within such flows.

“Our simulation captures various physical factors, including turbulent air flow, thermal effect, aerosol transport in turbulence, limited filtration efficiency of air conditioners, as well as the complex geometry of the space, all of which play a role in airborne transmission,” said Hong.

Although many computer simulation studies of airborne transmission of COVID-19 have been conducted recently, few directly link the prediction of high-fidelity computational fluid dynamics simulation with the actual infection outbreaks reported through contact tracing.

This work is the first realistic case simulated and linked directly with the prediction of simulation.

“It was enabled by advanced computational tools used in our simulation, which can capture the complex flows and aerosol transport and other multiphysics factors involved in a realistic setting,” Hong said.

The results show a remarkable direct linkage between regions of high aerosol exposure index and the reported infection patterns within the restaurant, which provides strong support to airborne transmission in this widely reported outbreak.

By using flow structure analysis and reverse-time tracing of aerosol trajectories, the researchers further pinpointed two potential transmission pathways that are currently being overlooked: the transmission caused by aerosols rising from beneath a table and transmission due to reentry aerosols associated with limited filtration efficiency of air conditioners.

“Our work highlights the need for more preventive measures, such as shielding more properly underneath the table and improving the filtration efficiency of air conditioners,” Hong said. “More importantly, our research demonstrates the capability and value of high-fidelity computer simulation tools for airborne infection risk assessment and the development of effective preventive measures.”

Featured image: Schematic of the flow and particle transport, highlighting the two transport pathways, within the entire restaurant. Mannequins show where customers were seated. CREDIT: Han Liu


Reference: Han Liu, Sida He, Lian Shen and Jiarong Hong, “Simulation-based study of COVID-19 outbreaks associated with air-conditioning in a restaurant“, Physics of Fluids 33, 023301 (2021); https://doi.org/10.1063/5.0040188


Provided by AIP Publishing

Porous Materials Unfavorable for Coronavirus Survival (Material Science)

As COVID-19 spreads via respiratory droplets, researchers have become increasingly interested in the drying of droplets on impermeable and porous surfaces. Surfaces that accelerate evaporation can decelerate the spread of the COVID-19 virus.

In Physics of Fluids, by AIP Publishing, researchers from IIT Bombay show a droplet remains liquid for a much shorter time on a porous surface, making it less favorable to survival of the virus.

The researchers found the coronavirus can survive for four days on glass, seven days on plastic, and seven days on stainless steel. But on paper and cloth, the virus survived for only three hours and two days, respectively.

“Based on our study, we recommend that furniture in hospitals and offices, made of impermeable material, such as glass, stainless steel, or laminated wood, be covered with porous material, such as cloth, to reduce the risk of infection upon touch,” said author Sanghamitro Chatterjee.

Similarly, the researchers suggest seats in public places, such as parks, shopping malls, restaurants, and railway or airport waiting halls, could be covered with cloth to alleviate the risk of disease spread.

For both impermeable and porous surfaces, 99.9% of the droplet’s liquid content is evaporated within the first few minutes. After this initial state, a microscopic thin residual liquid film remains on the exposed solid parts, where the virus can still survive.

The researchers discovered the evaporation of this remnant thin film is much faster in the case of porous surfaces as compared to impermeable surfaces. The d­­­­roplets spread due to capillary action between the liquid near the contact line and the horizontally oriented fibers on the porous surface and the void spaces in porous materials, which accelerates evaporation.

“The fact that just the geometric features rather than the chemical details of the porous material make the thin-film lifetime significantly less was surprising,” said Rajneesh Bhardwaj.

Specific findings, such as the droplet’s liquid phase lifetime of approximately six hours on paper, will be particularly relevant in certain contexts, like schools. While this timescale is shorter than that of any permeable material (e.g., glass with a liquid phase lifetime of approximately four days), it would impact the exchange of notebooks, for example, as policymakers evaluate safe measures for reopening schools or the exchange of currency note transactions in retail banks.

Similarly, cardboard boxes, used commonly by e-commerce companies around the world, could be deemed relatively safe, since they would inhibit the virus survival.

Featured image: Left: Schematic of capillary imbibition and thin-film formation on porous surfaces; right: a comparative picture depicting the time varying film thickness on glass (an impermeable material) and paper (a porous material) in the context of coronavirus survival. CREDIT: S. Chatterjee, J. S. Murallidharan, A. Agrawal, and R. Bhardwaj


Reference: Sanghamitro Chatterjee, Janani Srree Murallidharan, Amit Agrawal and Rajneesh Bhardwaj, “Why coronavirus survives longer on impermeable than porous surfaces“, Physics of Fluids 33, 021701 (2021); https://doi.org/10.1063/5.0037924


Provided by AIP Publishing

Patients With Lymphoma Treated With B-cell-depleting Therapies May Have Worse Outcomes From COVID-19 Infection (Medicine)

Among patients with lymphoma admitted to the hospital for severe COVID-19, those treated with B-cell-depleting therapies within the previous 12 months had an increased risk of prolonged hospital stay and death, according to results presented at the AACR Virtual Meeting: COVID-19 and Cancer, held Feb. 3-5.

“Patients with lymphoma may develop immune deficiency due to particular features of their disease or due to their treatment regimen, which can lead to increased incidence and increased severity of infections,” said study senior author Caroline Besson, MD, PhD, a hematologist at Centre Hospitalier de Versailles and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ) in France. “In the context of the COVID-19 pandemic, it appeared necessary to analyze the clinical course of this infection in our patients and to characterize the determinants of worse outcomes.”

Patients with lymphoma are often treated with B-cell-depleting antibodies, such as rituximab (Rituxan) or obinutuzumab (Gazyva). These drugs target CD20, a protein found on the surface of B cells. “More than 20 years ago, anti-CD20 monoclonal antibodies were shown to improve survival among patients with B-cell non-Hodgkin lymphoma, the most frequent subtype of the disease,” said presenting author Sylvain Lamure, MD, a hematologist at Centre Hospitalier Universitaire (CHU) Montpellier in France. “However, these treatments induce rapid B-cell depletion, which alters the generation of antibody responses to new pathogens, which may impact the clinical course of COVID-19,” he added.

To better elucidate factors associated with worse outcomes from COVID-19 in this patient population, the researchers evaluated data from 111 patients with lymphoma who were admitted to one of 16 French hospitals for severe COVID-19 during March and April of 2020. The researchers specifically focused on identifying factors associated with prolonged hospital stay (longer than 30 days) and death from any cause.

Of the 111 patients, 63 (57 percent) had previously received B-cell-depleting therapy; 29 percent of all patients required a prolonged hospital stay due to severe COVID-19 symptoms. After a median follow-up of 191 days, the six-month overall survival in this patient population was 69 percent.

After adjusting for age, comorbidities, and the presence of relapsed/refractory disease, the authors found that receipt of B-cell-depleting treatment within the previous 12 months nearly doubled the likelihood of a prolonged hospital stay and more than doubled the risk of death. Other factors that were significantly associated with decreased overall survival and prolonged hospital stay were having relapsed/refractory lymphoma or being at least 70 years of age.

“Our findings regarding the impact of anti-CD20 therapy on the course of COVID-19 can contribute to the guidelines for managing patients with lymphoma during the pandemic,” said Besson. “Our results also

highlight the need for specific therapies for patients with COVID-19 who are B-cell depleted, and for the evaluation of the efficacy and timing of vaccination in this particular population,” she said.

“Patients who recently received B-cell-depleting therapies and have COVID-19 should refer to their physician and should be closely monitored,” Lamure added.  

Limitations of this study include its retrospective nature.

Besson receives funding from Roche to study the immune response against SARS-CoV-2, the virus that causes COVID-19, among patients with lymphoma. Lamure declares no conflict of interest.


Provided by American Association for Cancer Research

Patients Receiving Chemotherapy May Not be at Increased Risk for COVID-19 (Medicine)

Study shows disruptions to cancer treatment should be minimized during the pandemic

Compared with patients with cancer who were not on active treatment, those receiving chemotherapy did not have an increased risk for developing COVID-19, according to data presented at AACR Virtual Meeting: COVID-19 and Cancer, held Feb. 3-5.

“Given the concern that patients with cancer are at increased risk for COVID-19, there have been widespread changes to the practice of clinical oncology since the start of the pandemic last year,” began Monica F. Chen, MD, a third-year resident in the Department of Medicine at Columbia University Vagelos College of Physicians and Surgeons and NewYork-Presbyterian Hospital, New York.

Because of the pandemic, surgeries have been delayed, treatment regimens have been modified to minimize the number of visits, and clinical trial enrollment has gone down. Chen and colleagues sought to understand what demographic, clinical, tumor- and treatment-related factors are associated with developing COVID-19 among patients with cancer.

“We found that patients on active treatment, including chemotherapy, were not at increased risk for COVID-19, and surprisingly, they were less likely to test positive for COVID-19 than those not on treatment,” Chen said.

“Our study shows that with proper precautions in the clinical setting, disruptions in lifesaving cancer treatment should be minimized during the COVID-19 pandemic,” Chen added.

Chen, her mentor Katherine Crew, MD, and colleagues conducted a retrospective study of cancer patients tested for COVID-19 between March 1, 2020, and June 6, 2020, at NewYork-Presbyterian Hospital/Columbia University Irving Medical Center in New York City. Of the 1,174 patients tested for COVID-19, 317 (27 percent) were positive. About 27 percent had a recent cancer diagnosis, 56.7 percent had active disease, and 56.7 percent had been on active cancer treatment within the past year.

Chen noted that consistent with the general population, older age, minority race/ethnicity, and obesity were associated with COVID-19 among patients with cancer. Compared with non-Hispanic white patients, black patients and Hispanic patients were 2.2 times and 2.7 times more likely to test positive for COVD-19, respectively.

Compared with cancer patients not receiving any treatment at the time of the study, those receiving chemotherapy were 35 percent less likely to develop COVID-19. Chen speculates that patients undergoing chemotherapy are likely more vigilant about social distancing, wearing face masks, and hand hygiene than those in remission, potentially resulting in fewer infections. “Regardless, it is reassuring to see that cancer patients receiving chemotherapy were not at increased risk of testing positive for COVID-19,” Chen added.

Consistent with prior studies, cancer patients who tested positive for COVID-19 had higher death rates than those who tested negative for the infection.

Limitations of the study include the retrospective study design. Results from a single academic urban medical center may not be generalizable to other study populations. While universal COVID-19 testing was implemented for all hospitalized patients, only symptomatic patients were tested in the outpatient setting, which may have introduced selection bias. The study was not adjusted for comorbid conditions. While patients were followed for up to six months since COVID-19 diagnosis, long-term effects are still uncertain, Chen noted.

This study was sponsored by the National Cancer Institute. Chen declares no conflicts of interest.


Provided by American Association for Cancer Research

Artificial Intelligence Yields New Ways to Combat the Coronavirus (Medicine)

Countering COVID-19 mutations and designing updated vaccines could occur at lightning speeds thanks to a new, USC-developed AI framework.

USC researchers have developed a new method to counter emergent mutations of the coronavirus and hasten vaccine development to stop the pathogen responsible for ruining the economy and killing thousands of people.

Using artificial intelligence (AI), the research team at the USC Viterbi School of Engineering developed a method to speed the analysis of vaccines and zero in on the best potential preventive medical therapy.

The method is easily adaptable to analyze potential mutations of the virus, ensuring the best possible vaccines are quickly identified — solutions that give humans a big advantage over the evolving contagion. Their machine-learning model can accomplish vaccine design cycles that once took months or years in a matter of seconds and minutes, the study says.

“This AI framework, applied to the specifics of this virus, can provide vaccine candidates within seconds and move them to clinical trials quickly to achieve preventive medical therapies without compromising safety,” said Paul Bogdan, associate professor of electrical and computer engineering at USC Viterbi and corresponding author of the study. “Moreover, this can be adapted to help us stay ahead of the coronavirus as it mutates around the world.”

The findings appear today in Nature Research’s Scientific Reports.

AI-assisted computer model predicts potential coronavirus vaccines

When applied to SARS-CoV-2 — the virus that causes COVID-19 — the computer model quickly eliminated 95% of the compounds that could’ve possibly treated the pathogen and pinpointed the best options, the study says.

The AI-assisted method predicted 26 potential vaccines that would work against the coronavirus. From those, the scientists identified the best 11 from which to construct a multi-epitope vaccine, which can attack the spike proteins that the coronavirus uses to bind and penetrate a host cell. Vaccines target the region — or epitope — of the contagion to disrupt the spike protein, neutralizing the ability of the virus to replicate.

Moreover, the engineers can construct a new multi-epitope vaccine for a new virus in less than a minute and validate its quality within an hour. By contrast, current processes to control the virus require growing the pathogen in the lab, deactivating it and injecting the virus that caused a disease. The process is time-consuming and takes more than one year; meanwhile, the disease spreads.

USC-developed method could help counter COVID-19 mutations

USC’s AI-assisted method will be especially useful during this stage of the pandemic as the coronavirus begins to mutate in populations around the world. Some scientists are concerned that the mutations may minimize the effectiveness of vaccines by Pfizer and Moderna, which are now being distributed. Recent variants of the virus that have emerged in the United Kingdom, South Africa and Brazil seem to spread more easily, which scientists say will rapidly lead to many more cases, deaths and hospitalizations.

But Bogdan said that if SARS-CoV-2 becomes uncontrollable by current vaccines, or if new vaccines are needed to deal with other emerging viruses, then the method can be used to design other preventive mechanisms quickly.

For example, the study explains that the USC scientists used only one B-cell epitope and one T-cell epitope, whereas applying a bigger dataset and more possible combinations can develop a more comprehensive and quicker vaccine design tool. The study estimates the method can perform accurate predictions with over 700,000 different proteins in the dataset.

“The proposed vaccine design framework can tackle the three most frequently observed mutations and be extended to deal with other potentially unknown mutations,” Bogdan said.

The raw data for the research comes from a giant bioinformatics database called the Immune Epitope Database (IEDB) in which scientists around the world have been compiling data about the coronavirus, among other diseases. IEDB contains over 600,000 known epitopes from some 3,600 different species, along with the Virus Pathogen Resource, a complementary repository of information about pathogenic viruses. The genome and spike protein sequence of SARS-CoV-2 comes from the National Center for Biotechnology Information.

COVID-19 has led to 87 million cases and more than 1.88 million deaths worldwide, including more than 400,000 fatalities in the United States. It has devastated the social, financial and political fabric of many countries.

The study authors are Bogdan, Zikun Yang and Shahin Nazarian of the Ming Hsieh Department of Electrical and Computer Engineering at USC Viterbi.

Support for the study comes from the National Science Foundation (NSF) under the Career Award (CPS/CNS-1453860) and NSF grants (CCF-1837131, MCB-1936775 and CNS-1932620); a U.S. Army Research Office grant (W911NF-17-1-0076); a Defense Advanced Research Projects Agency (DARPA) Young Faculty Award and Director Award grant (N66001-17-1-4044), and a Northrop Grumman grant.

Featured image: The USC Viterbi machine-learning model can accomplish vaccine design cycles that once took months in a matter of minutes. (Illustration/iStock)


Reference: Yang, Z., Bogdan, P. & Nazarian, S. An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study. Sci Rep 11, 3238 (2021). https://www.nature.com/articles/s41598-021-81749-9 https://doi.org/10.1038/s41598-021-81749-9


Provided by USC

Computer Can Determine Whether You’ll Die From COVID (Medicine)

Using patient data, artificial intelligence can make a 90 percent accurate assessment of whether a person will die from COVID-19 or not, according to new research at the University of Copenhagen. Body mass index (BMI), gender and high blood pressure are among the most heavily weighted factors. The research can be used to predict the number of patients in hospitals, who will need a respirator and determine who ought to be first in line for a vaccination.

Artificial intelligence is able to predict who is most likely to die from the coronavirus. In doing so, it can also help decide who should be at the front of the line for the precious vaccines now being administered across Denmark. The result is from a newly published study by researchers at the University of Copenhagen’s Department of Computer Science. Since the COVID pandemic’s first wave, researchers have been working to develop computer models that can predict, based on disease history and health data, how badly people will be affected by COVID-19.  

Based on patient data from the Capital Region of Denmark and Region Zealand, the results of the study demonstrate that artificial intelligence can, with up to 90 percent certainty, determine whether an uninfected person who is not yet infected will die of COVID-19 or not if they are unfortunate enough to become infected. Once admitted to the hospital with COVID-19, the computer can predict with 80 percent accuracy whether the person will need a respirator.

“We began working on the models to assist hospitals, as during the first wave, they feared that they did not have enough respirators for intensive care patients. Our new findings could also be used to carefully identify who needs a vaccine,” explains Professor Mads Nielsen of the University of Copenhagen’s Department of Computer Science.

Older men with high blood pressure are highest at risk

The researchers fed a computer program with health data from 3,944 Danish COVID-19 patients. This trained the computer to recognize patterns and correlations in both patients’ prior illnesses and in their bouts against COVID-19.

“Our results demonstrate, unsurprisingly, that age and BMI are the most decisive parameters for how severely a person will be affected by COVID-19. But the likelihood of dying or ending up on a respirator is also heightened if you are male, have high blood pressure or a neurological disease,” explains Mads Nielsen.

The diseases and health factors that, according to the study, have the most influence on whether a patient ends up on a respirator after being infected with COVID-19 are in order of priority: BMI, age, high blood pressure, being male, neurological diseases, COPD, asthma, diabetes and heart disease.

“For those affected by one or more of these parameters, we have found that it may make sense to move them up in the vaccine queue, to avoid any risk of them becoming inflected and eventually ending up on a respirator,” says Nielsen. 

Predicting respiratory needs is a must

Researchers are currently working with the Capital Region of Denmark to take advantage of this fresh batch of results in practice. They hope that artificial intelligence will soon be able to help the country’s hospitals by continuously predicting the need for respirators.

“We are working towards a goal that we should be able to predict the need for respirators five days ahead by giving the computer access to health data on all COVID positives in the region,” says Mads Nielsen, adding:

“The computer will never be able to replace a doctor’s assessment, but it can help doctors and hospitals see many COVID-19 infected patients at once and set ongoing priorities.”

However, technical work is still pending to make health data from the region available for the computer and thereafter to calculate the risk to the infected patients. The research was carried out in collaboration with Rigshospitalet and Bispebjerg and Frederiksberg Hospital.

Facts:

  • Data is processed on Computerome, a secure supercomputer for personal data, and under the permission of the Danish Patient Safety Authority, data owners and other relevant authorities.
  • Artificial intelligence predicts with 90 percent accuracy whether an infected patient will die of COVID-19.
  • Once a person is hospitalized with COVID-19, artificial intelligence can predict whether the person should be on a respirator with 80 percent accuracy.
  • BMI, age, high blood pressure, being male, neurological diseases, COPD, asthma, diabetes and heart disease are factors that artificial intelligence weigh`s most to with the risk of getting into the respirator.
  • The computer models are based on health data from 3,944 COVID-19 patients from the Capital Region and Region Zealand.
  • The article is published in the scientific journal Scientific Reports.
  • The study is supported by the Novo Nordisk Foundation and the Innovation Fund.

Featured image: The researchers fed a computer program with health data from 3,944 Danish COVID-19 patients. This trained the computer to recognize patterns and correlations in both patients’ prior illnesses and in their bouts against COVID-19. Photo: Getty


Reference: Jimenez-Solem, E., Petersen, T.S., Hansen, C. et al. Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients. Sci Rep 11, 3246 (2021). https://doi.org/10.1038/s41598-021-81844-x


Provided by University of Copenhagen

Dialysis Patients Have 4-fold Greater Risk of Dying From COVID-19 (Medicine)

People undergoing long-term dialysis are almost 4 times more likely to die from COVID-19 and should be prioritized for vaccination, found a new Ontario study published in CMAJ (Canadian Medical Association Journal).

“As the COVID-19 pandemic proceeds, focused efforts should be made to protect this population from infection including prioritizing patients on long-term dialysis and the staff treating them for SARS-CoV-2 vaccination,” writes Dr. Peter Blake, provincial director, Ontario Renal Network, Ontario Health, and professor, Schulich School of Medicine and Dentistry, Western University, London, Ontario, with coauthors.

The study looked at data on 12 501 patients undergoing long-term dialysis in Ontario between March 12 and August 20, 2020, of whom 187 (1.5%) were diagnosed with SARS-CoV-2 infection. Of these, 53 people (28.3%) died and 117 (62.6%) were admitted to hospital. By contrast, uninfected people who were receiving dialysis during that period had a death rate of 5.8% and a hospitalization rate of 27%. Since this analysis and particularly in the last two months, the number of people on dialysis infected with the virus has risen to over 570 and the number of deaths has increased to 120.

Risk factors for SARS-CoV-2 infection in people on dialysis include hemodialysis at a hospital facility as compared to home dialysis; living in long-term care; living in the Greater Toronto Area; Black, Indian subcontinent and other non-White ethnicity; and lower income.

In addition to vaccination and infection precautions, the authors recommend educating patients about their increased risk of infection and higher mortality, including risks associated with social activities. Paid sick leave should be available for those in high-risk occupations. Other strategies should include a low symptom threshold for testing, more space between treatment stations in dialysis units, and regular testing of high-risk groups, such as those living in long-term care.

Listen to a podcast with authors Dr. Peter Blake and Rebecca Cooper.

“COVID-19 in patients undergoing long-term dialysis in Ontario” is published February 4, 2021.


Reference: Leena Taji, Doneal Thomas, Matthew J. Oliver, Jane Ip, Yiwen Tang, Angie Yeung, Rebecca Cooper, Andrew A. House, Phil McFarlane and Peter G. Blake, “COVID-19 in patients undergoing long-term dialysis in Ontario”, CMAJ February 04, 2021 cmaj.202601; DOI: https://doi.org/10.1503/cmaj.202601


Provided by CMAJ