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Showing posts with label Medical Science. Show all posts
Showing posts with label Medical Science. Show all posts

Friday, 3 April 2020

Trial drug can significantly block early stages of COVID-19 in engineered human tissues


An international team led by University of British Columbia researcher Dr. Josef Penninger has found a trial drug that effectively blocks the cellular door SARS-CoV-2 uses to infect its hosts.

The findings, published today in Cell, hold promise as a treatment capable of stopping early infection of the novel coronavirus that, as of April 2, has affected more than 981,000 people and claimed the lives of 50,000 people worldwide.

The study provides new insights into key aspects of SARS-CoV-2, the virus that causes COVID-19, and its interactions on a cellular level, as well as how the virus can infect blood vessels and kidneys.

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"We are hopeful our results have implications for the development of a novel drug for the treatment of this unprecedented pandemic," says Penninger, professor in UBC's faculty of medicine, director of the Life Sciences Institute and the Canada 150 Research Chair in Functional Genetics at UBC.

"This work stems from an amazing collaboration among academic researchers and companies, including Dr. Ryan Conder's gastrointestinal group at STEMCELL Technologies in Vancouver, Nuria Montserrat in Spain, Drs. Haibo Zhang and Art Slutsky from Toronto and especially Ali Mirazimi's infectious biology team in Sweden, who have been working tirelessly day and night for weeks to better understand the pathology of this disease and to provide breakthrough therapeutic options."



ACE2 -- a protein on the surface of the cell membrane -- is now at centre-stage in this outbreak as the key receptor for the spike glycoprotein of SARS-CoV-2. In earlier work, Penninger and colleagues at the University of Toronto and the Institute of Molecular Biology in Vienna first identified ACE2, and found that in living organisms, ACE2 is the key receptor for SARS, the viral respiratory illness recognized as a global threat in 2003. His laboratory also went on to link the protein to both cardiovascular disease and lung failure.

While the COVID-19 outbreak continues to spread around the globe, the absence of a clinically proven antiviral therapy or a treatment specifically targeting the critical SARS-CoV-2 receptor ACE2 on a molecular level has meant an empty arsenal for health care providers struggling to treat severe cases of COVID-19.

"Our new study provides very much needed direct evidence that a drug -- called APN01 (human recombinant soluble angiotensin-converting enzyme 2 -- hrsACE2) -- soon to be tested in clinical trials by the European biotech company Apeiron Biologics, is useful as an antiviral therapy for COVID-19," says Dr. Art Slutsky, a scientist at the Keenan Research Centre for Biomedical Science of St. Michael's Hospital and professor at the University of Toronto who is a collaborator on the study.

In cell cultures analyzed in the current study, hrsACE2 inhibited the coronavirus load by a factor of 1,000-5,000. In engineered replicas of human blood vessel and kidneys -- organoids grown from human stem cells -- the researchers demonstrated that the virus can directly infect and duplicate itself in these tissues. This provides important information on the development of the disease and the fact that severe cases of COVID-19 present with multi-organ failure and evidence of cardiovascular damage. Clinical grade hrsACE2 also reduced the SARS-CoV-2 infection in these engineered human tissues.

"Using organoids allows us to test in a very agile way treatments that are already being used for other diseases, or that are close to being validated. In these moments in which time is short, human organoids save the time that we would spend to test a new drug in the human setting," says Núria Montserrat, ICREA professor at the Institute for Bioengineering of Catalonia in Spain.



"The virus causing COVID-19 is a close sibling to the first SARS virus," adds Penninger. "Our previous work has helped to rapidly identify ACE2 as the entry gate for SARS-CoV-2, which explains a lot about the disease. Now we know that a soluble form of ACE2 that catches the virus away, could be indeed a very rational therapy that specifically targets the gate the virus must take to infect us. There is hope for this horrible pandemic."

This research was supported in part by the Canadian federal government through emergency funding focused on accelerating the development, testing, and implementation of measures to deal with the COVID-19 outbreak.


Bibliography:

Vanessa Monteil, Hyesoo Kwon, Patricia Prado, Astrid Hagelkrüys, Reiner A. Wimmer, Martin Stahl, Alexandra Leopoldi, Elena Garreta, Carmen Hurtado Del Pozo, Felipe Prosper, J.p. Romero, Gerald Wirnsberger, Haibo Zhang, Arthur S. Slutsky, Ryan Conder, Nuria Montserrat, Ali Mirazimi, Josef M. Penninger.

Inhibition of SARS-CoV-2 infections in engineered human tissues using clinical-grade soluble human ACE2.

Submitted to Cell, 2020

DOI: 10.1016/j.cell.2020.04.004

New Nanosensors could offer early detection of lung tumors


People who are at high risk of developing lung cancer, such as heavy smokers, are routinely screened with computed tomography (CT), which can detect tumors in the lungs. However, this test has an extremely high rate of false positives, as it also picks up benign nodules in the lungs.

Researchers at Massachusetts Institute of Technology (MIT) have developed a nanoparticle-based approach that allows the early diagnosis of lung cancer through a simple urine test. The strategy detects biomarkers resulting from the interaction of peptide-coated nanoparticles with disease-associated proteases in the tumor microenvironment.

Experiments in two different mouse models of lung cancer showed that the urine test could detect tumors as small as 2.8 mm3. The researchers hope that this type of noninvasive diagnosis could reduce the number of false positives associated with an existing test method, and help to detect more tumors in the early stages of the disease.

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“If you look at the field of cancer diagnostics and therapeutics, there’s a renewed recognition of the importance of early cancer detection and prevention,” said study lead Sangeeta Bhatia, PhD, who is the John and Dorothy Wilson professor of health sciences and technology and electrical engineering and computer science, and a member of MIT’s Koch Institute for Integrative Cancer Research and the Institute for Medical Engineering and Science. “We really need new technologies that are going to give us the capability to see cancer when we can intercept it and intervene early.” Bhatia and colleagues report on development of the test in Science Translational Medicine Journal.

MIT engineers have developed nanoparticles that can be delivered to the lungs, where tumor-associated proteases cut peptides on the surface of the particles, releasing reporter molecules. Those reporters can be detected by a urine test.

Lung cancer is the most common cause of cancer-related death (25.3%) in the United States the authors wrote, and has a “dismal” five-year survival rate of 18.6%. Early detection is key, as the five-year survival rates are 6- to 13-fold higher in patients whose tumors are detected before they spread to distal sites in the body. People in the United States who are at high risk of developing lung cancer, such as heavy smokers, are routinely screened using low-dose computed tomography (LDCT), which can detect tumors in the lungs.

However, this test has an extremely high rate of false positives, as it also picks up benign nodules in the lungs. There is then a “considerable burden of complications incurred during unnecessary follow-up procedures,” the investigators stated, and the method isn’t routinely used in other countries. “As a result of these complications, screening by LDCT has not been widely adopted outside of the United States, and there is “an urgent need to develop diagnostic tests that increase the effectiveness of lung cancer screening.”

The approach taken by the MIT researchers is based on the use of what they call “activity-based sensors” that monitor for disease and intensify disease-associated signals, which can then be detected in urine. “Activity-based nanosensors leverage dysregulated protease activity to overcome the insensitivity of previous biomarker assays, amplifying disease-associated signals generated in the tumor microenvironment and providing a concentrated urine-based readout,” the team explained.



Bhatia’s lab has for several years been developing such nanoparticles that can detect cancer by interacting with proteases. These enzymes help tumor cells to escape their original locations by cutting through proteins of the extracellular matrix. To find the cancer-associated proteases Bhatia created nanoparticles coated with peptides that are targeted by the cancer-related proteases. The particles accumulate at tumor sites, where the peptides are cleaved, releasing biomarkers that can then be detected in a urine sample.

The Bhatia lab has previously developed sensors for colon and ovarian cancer, and in their new study, the researchers applied the technology to lung cancer, which kills about 150,000 people in the United States every year. They project that the test could be applied to confirm cancer in patients who have had a positive CT scan. These patients would commonly undergo a biopsy or other invasive test to search for lung cancer, but in some cases, this procedure can cause complications, so a noninvasive follow-up test could be useful to determine which patients actually need a biopsy, Bhatia said.

“The CT scan is a good tool that can see a lot of things,” she said. “The problem with it is that 95% of what it finds is not cancer, and right now you have to biopsy too many patients who test positive.”

To customize their sensors for lung cancer, the researchers analyzed data in The Cancer Genome Atlas, and identified proteases that are abundant in lung cancer. They created a panel of 14 peptide-coated nanoparticles that could interact with these enzymes.

The researchers then tested the sensors in two different genetic mouse models, “driven by either Kras/Trp53 (KP) mutations, or Eml4-Alk (EA) fusion,” that spontaneously develop lung cancer. To help prevent background noise that could come from other organs or the bloodstream, the researchers injected the particles directly into the animals’ airways. The researchers carried out their diagnostic test using the sensors at 5 weeks, 7.5 weeks, and 10.5 weeks after tumor growth began. To make the diagnoses more accurate, they used machine learning to train an algorithm to distinguish between data from mice that had tumors and from mice that did not.

Using this approach, the researchers found that they could accurately detect tumors in one of the mouse models as early as 7.5 weeks, when the tumors were only 2.8 mm3, on average. In the other strain of mice, tumors could be detected at 5 weeks. The sensors’ success rate was also comparable to or better than the success rate of CT scans performed at the same time points.

“Intrapulmonary administration of the nanosensors to a Kras- and Trp53-mutant lung adenocarcinoma mouse model confirmed the role of metalloproteases in lung cancer and enabled accurate detection of localized disease, with 100% specificity and 81% sensitivity,” they reported. “Furthermore, this approach generalized to an alternative autochthonous model of lung adenocarcinoma, where it detected cancer with 100% specificity and 95% sensitivity and was not confounded by lipopolysaccharide-driven lung inflammation.”

Importantly, the sensors could distinguish between early-stage cancer and noncancerous inflammation of the lungs, a common condition in smokers, and one of the reasons that CT scans produce so many false positives. “Activity-based nanosensors may have clinical utility as a rapid, safe, and cost-effective follow-up to LDCT, reducing the number of patients referred for invasive testing,” the authors concluded. “With further optimization and validation studies, activity-based nanosensors may one day provide an accurate, noninvasive, and radiation-free strategy for lung cancer testing.”

The authors acknowledged that their study was carried out in mouse models, which do not fully recapitulate human disease, and there were other study limitations that will need to be addressed. Clinical trials will be needed to fully validate the use of activity-based nanosensors for detecting lung cancer and distinguishing malignant from benign and extrapulmonary disease, they pointed out.



Bhatia envisions that the nanoparticle sensors could be used as a noninvasive diagnostic for people who get a positive result on a screening test, potentially eliminating the need for a biopsy. For use in humans, her team is working on a form of the particles that could be inhaled as a dry powder or through a nebulizer. Another possible application is using the sensors to monitor how well lung tumors respond to treatment, such as drugs or immunotherapies. “A great next step would be to take this into patients who have known cancer, and are being treated, to see if they’re on the right medicine,” Bhatia said.


Bibliography:

Urinary detection of lung cancer in mice via noninvasive pulmonary protease profiling

Jesse D. Kirkpatrick, Andrew D. Warren, Ava P. Soleimany, Peter M. K. Westcott1, Justin C. Voog, Carmen Martin-Alonso, Heather E. Fleming, Tuomas Tammela, Tyler Jacks and Sangeeta N. Bhatia.

Science Translational Medicine  01 Apr 2020:
Vol. 12, Issue 537, eaaw0262

DOI: 10.1126/scitranslmed.aaw0262

Friday, 27 March 2020

New mathematical model can more effectively track epidemics



As COVID-19 spreads worldwide, leaders are relying on mathematical models to make public health and economic decisions.

A new model developed by Princeton and Carnegie Mellon researchers improves tracking of epidemics by accounting for mutations in diseases. Now, the researchers are working to apply their model to allow leaders to evaluate the effects of countermeasures to epidemics before they deploy them.

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“We want to be able to consider interventions like quarantines, isolating people, etc., and then see how they affect an epidemic’s spread when the pathogen is mutating as it spreads,” said H. Vincent Poor, one of the researchers on this study and Princeton’s interim dean of engineering.

The models currently used to track epidemics use data from doctors and health workers to make predictions about a disease’s progression. Poor, the Michael Henry Strater University Professor of Electrical Engineering, said the model most widely used today is not designed to account for changes in the disease being tracked. This inability to account for changes in the disease can make it more difficult for leaders to counter a disease’s spread. Knowing how a mutation could affect transmission or virulence could help leaders decide when to institute isolation orders or dispatch additional resources to an area.



“In reality, these are physical things, but in this model, they are abstracted into parameters that can help us more easily understand the effects of policies and of mutations,” Poor said.

If the researchers can correctly account for measures to counter the spread of disease, they could give leaders critical insights into the best steps they could take in the face of pandemics. The researchers are building on work published March 17 in the Proceedings of the National Academy of Sciences. In that article, they describe how their model is able to track changes in epidemic spread caused by mutation of a disease organism. The researchers are now working to adapt the model to account for public health measures taken to stem an epidemic as well.

The researchers’ work stems from their examination of the movement of information through social networks, which has remarkable similarities to the spread of biological infections. Notably, the spread of information is affected by slight changes in the information itself. If something becomes slightly more exciting to recipients, for example, they might be more likely to pass it along or to pass it along to a wider group of people. By modeling such variations, one can see how changes in the message change its target audience.

“The spread of a rumor or of information through a network is very similar to the spread of a virus through a population,” Poor said. “Different pieces of information have different transmission rates. Our model allows us to consider changes to information as it spreads through the network and how those changes affect the spread.”

“Our model is agnostic with regard to the physical network of connectivity among individuals,” said Poor, an expert in the field of information theory whose work has helped establish modern cellphone networks. “The information is being abstracted into graphs of connected nodes; the nodes might be information sources or they might be potential sources of infection.”



Obtaining accurate information is extremely difficult during an ongoing pandemic when circumstances shift daily, as we have seen with the COVID-19 virus. “It’s like a wildfire. You can’t always wait until you collect data to make decisions — having a model can help fill this void,” Poor said.

 “Hopefully, this model could give leaders another tool to better understand the reasons why, for example, the COVID-19 virus is spreading so much more rapidly than predicted, and thereby help them deploy more effective and timely countermeasures,” Poor said.


Bibliography:

Rashad Eletreby, Yong Zhuang, Kathleen M. Carley, Osman Yağan, H. Vincent Poor.

The effects of evolutionary adaptations on spreading processes in complex networks. 

Proceedings of the National Academy of Sciences, 2020; 117 (11): 5664

DOI: 10.1073/pnas.1918529117

Saturday, 21 March 2020

Study reveals how long COVID-19 remains infectious on cardboard, metal and plastic


The virus that causes COVID-19 remains for several hours to days on surfaces and in aerosols, a new study published in the New England Journal of Medicine found.

The study suggests that people may acquire the coronavirus through the air and after touching contaminated objects. Scientists discovered the virus is detectable for up to three hours in aerosols, up to four hours on copper, up to 24 hours on cardboard and up to two to three days on plastic and stainless steel.

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"This virus is quite transmissible through relatively casual contact, making this pathogen very hard to contain," said James Lloyd-Smith, a co-author of the study and a UCLA professor of ecology and evolutionary biology. "If you're touching items that someone else has recently handled, be aware they could be contaminated and wash your hands."



The study attempted to mimic the virus being deposited onto everyday surfaces in a household or hospital setting by an infected person through coughing or touching objects, for example. The scientists then investigated how long the virus remained infectious on these surfaces.

The study's authors are from UCLA, the National Institutes of Health's National Institute of Allergy and Infectious Diseases, the Centers for Disease Control and Prevention, and Princeton University. They include Amandine Gamble, a UCLA postdoctoral researcher in Lloyd-Smith's laboratory.

As shown in Panel A, the titer of aerosolized viable virus is expressed in 50% tissue-culture infectious dose (TCID50) per liter of air. Viruses were applied to copper, cardboard, stainless steel, and plastic maintained at 21 to 23°C and 40% relative humidity over 7 days. The titer of viable virus is expressed as TCID50 per milliliter of collection medium. All samples were quantified by end-point titration on Vero E6 cells. Plots show the means and standard errors ( bars) across three replicates. As shown in Panel B, regression plots indicate the predicted decay of virus titer over time; the titer is plotted on a logarithmic scale. Points show measured titers and are slightly jittered (i.e., they show small rapid variations in the amplitude or timing of a waveform arising from fluctuations) along the time axis to avoid overplotting. Lines are random draws from the joint posterior distribution of the exponential decay rate (negative of the slope) and intercept (initial virus titer) to show the range of possible decay patterns for each experimental condition. There were 150 lines per panel, including 50 lines from each plotted replicate. As shown in Panel C, violin plots indicate posterior distribution for the half-life of viable virus based on the estimated exponential decay rates of the virus titer. The dots indicate the posterior median estimates, and the black lines indicate a 95% credible interval. Experimental conditions are ordered according to the posterior median half-life of SARS-CoV-2. The dashed lines indicate the limit of detection, which was 3.33×100.5 TCID50 per liter of air for aerosols, 100.5 TCID50 per milliliter of medium for plastic, steel, and cardboard, and 101.5 TCID50 per milliliter of medium for copper.


In February, Lloyd-Smith and colleagues reported in the journal eLife that screening travelers for COVID-19 is not very effective. People infected with the virus -- officially named SARS-CoV-2 -- may be spreading the virus without knowing they have it or before symptoms appear. Lloyd-Smith said the biology and epidemiology of the virus make infection extremely difficult to detect in its early stages because the majority of cases show no symptoms for five days or longer after exposure.

"Many people won't have developed symptoms yet," Lloyd-Smith said. "Based on our earlier analysis of flu pandemic data, many people may not choose to disclose if they do know."

The new study supports guidance from public health professionals to slow the spread of COVID-19:



  • Avoid close contact with people who are sick.

  • Avoid touching your eyes, nose and mouth.

  • Stay home when you are sick.

  • Cover coughs or sneezes with a tissue, and dispose of the tissue in the trash.

  • Clean and disinfect frequently touched objects and surfaces using a household cleaning spray or wipe.


Bibliography:

Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1.

Neeltje van Doremalen, Trenton Bushmaker, Dylan H. Morris, Myndi G. Holbrook, Amandine Gamble, Brandi N. Williamson, Azaibi Tamin, Jennifer L. Harcourt, Natalie J. Thornburg, Susan I. Gerber, James O. Lloyd-Smith, Emmie de Wit, Vincent J. Munster.

New England Journal of Medicine, 2020;

DOI: 10.1056/NEJMc2004973

Friday, 20 March 2020

Coronavirus spreads quickly and sometimes before people have symptoms, study finds


Infectious disease researchers at The University of Texas at Austin studying the novel coronavirus were able to identify how quickly the virus can spread, a factor that may help public health officials in their efforts at containment. They found that time between cases in a chain of transmission is less than a week and that more than 10% of patients are infected by somebody who has the virus but does not yet have symptoms.

In the paper in press with the journal Emerging Infectious Diseases, a team of scientists from the United States, France, China and Hong Kong were able to calculate what's called the serial interval of the virus. To measure serial interval, scientists look at the time it takes for symptoms to appear in two people with the virus: the person who infects another, and the infected second person.

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Researchers found that the average serial interval for the novel coronavirus in China was approximately four days. This also is among the first studies to estimate the rate of asymptomatic transmission.

The speed of an epidemic depends on two things -- how many people each case infects and how long it takes for infection between people to spread. The first quantity is called the reproduction number; the second is the serial interval. The short serial interval of COVID-19 means emerging outbreaks will grow quickly and could be difficult to stop, the researchers said.

"Ebola, with a serial interval of several weeks, is much easier to contain than influenza, with a serial interval of only a few days. Public health responders to Ebola outbreaks have much more time to identify and isolate cases before they infect others," said Lauren Ancel Meyers, a professor of integrative biology at UT Austin. "The data suggest that this coronavirus may spread like the flu. That means we need to move quickly and aggressively to curb the emerging threat."



Meyers and her team examined more than 450 infection case reports from 93 cities in China and found the strongest evidence yet that people without symptoms must be transmitting the virus, known as pre-symptomatic transmission. According to the paper, more than 1 in 10 infections were from people who had the virus but did not yet feel sick.

Previously, researchers had some uncertainty about asymptomatic transmission with the coronavirus. This new evidence could provide guidance to public health officials on how to contain the spread of the disease.

"This provides evidence that extensive control measures including isolation, quarantine, school closures, travel restrictions and cancellation of mass gatherings may be warranted," Meyers said. "Asymptomatic transmission definitely makes containment more difficult."

Meyers pointed out that with hundreds of new cases emerging around the world every day, the data may offer a different picture over time. Infection case reports are based on people's memories of where they went and whom they had contact with. If health officials move quickly to isolate patients, that may also skew the data.

"Our findings are corroborated by instances of silent transmission and rising case counts in hundreds of cities worldwide," Meyers said. "This tells us that COVID-19 outbreaks can be elusive and require extreme measures."

Zhanwei Du of The University of Texas at Austin, Lin Wang of the Institut Pasteur in Paris, Xiaoke Xu of Dalian Minzu University, Ye Wu of Beijing Normal University and Benjamin J. Cowling of Hong Kong University also contributed to the research. Lauren Ancel Meyers holds the Denton A. Cooley Centennial Professorship in Zoology at The University of Texas at Austin.



The research was funded by the U.S. National Institutes of Health and the National Natural Science Foundation of China.


Bibliography:

Serial Interval of COVID-19 from Publicly Reported Confirmed Cases.

Zhanwei Du, Xiaoke Xu, Ye Wu, Lin Wang, Benjamin J. Cowling, Lauren Ancel Meyers.

Emerging Infectious Diseases, April 2020;

DOI: 10.3201/eid2606.200357

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