Ravaged Landscape of COVID-19 Lungs

A revolutionary tool designed to broaden our understanding of human anatomy has for the first time provided scientists with a cellular-level look at lungs damaged by COVID-19. In healthy lungs, the blood vessel system that oxygenates the blood is separate from the system that feeds the lung tissue itself. But in some severe respiratory illnesses, such as pneumonia, pressures caused by the infection can lead blood vessels in the heart and lungs to expand and grow, sometimes cutting through the body and forming channels between parts of the pulmonary system that shouldn’t be connected. Similarly, COVID-19 infections can create the same types of abnormal channels. The channels give unoxygenated blood coming into the lungs an alternate exit ramp, allowing it to essentially skip the line and shoot back into the body without picking up any oxygen molecules first. Scientists believed that this could be a cause of the low blood oxygen levels sometimes experienced by COVID-19 patients, a condition known as hypoxemia.

Blood vessel growth is a very controlled process,” said Claire Walsh, a medical engineer at University College London and the first author of the imaging study, published in the journal Nature Methods. “It should be in this lovely tree-like branching structure. And you look at the COVID lungs, and you can just see it’s in these big clumps of really dense vessels all over the place, so that it just looks … wrong.

Walsh’s team, which included clinicians from Germany and France, has procured sharper-than-ever images of these warped structures, thanks to an imaging technique known as HiP-CT, or Hierarchical Phase-Contrast Tomography, which allows them to zoom in on any body part with 100 times the resolution of a traditional CT scan. Although the technique can only be used to capture images of samples removed from a body and preserved in a way that minimizes interference (rather than of organs that are still part of a living person), in pairing it with the world’s brightest X-rays at the European Synchrotron particle accelerator, the researchers hope to build a visual database of not only lungs infected with COVID-19, but other, healthy organs throughout the body.

Source: https://www.insidescience.org/

How The Coronavirus Infects Human Cells

Tiny artificial lungs grown in a lab from adult stem cells have allowed scientists to watch how coronavirus infects the lungs in a new ‘major breakthrough‘. Researchers from Duke University and Cambridge University produced artificial lungs in two independent and separate studies to examine the spread of Covid-19. The ‘living lung‘ models minimic the tiny air sacs that take up the oxygen we breathe, known to be where most serious lung damage from the deadly virus takes place.   Having access to the models to test the spread of SAS-CoV-2, the virus responsible for Covid-19, will allow researchers to test potential drugs and gain a better understanding of why some people suffer from the disease worse than others.

In both studies the 3D min-lung models were grown from stem cells that repair the deepest portions of the lungs when SARS-CoV-2 attacks – known as alveolar cells. To date, there have been more than 40 million cases of COVID-19 and almost 1.13 million deaths worldwide. The main target tissues of SARS-CoV-2, especially in patients that develop pneumonia, appear to be alveoli, according to the Cambridge team. They extracted the alveoli cells from donated tissue and reprogrammed them back to their earlierstem cell‘ stage and forced them to grow into self-organising alveolar-like 3D structures that mimic the behaviour of key lung tissue. Dr Joo-Hyeon Lee, co-senior author of the Cambridge paper, said we still know surprisingly little about how SARS-CoV-2 infects the lungs and causes disease.

Representative image of three – dimensional human lung alveolar organoid produced by the Cambridge and Korean researchers to better understand SARS-CoV-2

Our approach has allowed us to grow 3D models of key lung tissue – in a sense, “mini-lungs” – in the lab and study what happens when they become infected.’

Duke researchers took a similar approach. The team, led by Duke cell biologist Purushothama Rao Tata, say their model will allow for hundreds of experiments to be run simultaneously to screen for new drug candidates. ‘This is a versatile model system that allows us to study not only SARS-CoV-2, but any respiratory virus that targets these cells, including influenza,‘ Tata said.

Both teams infected models with a strain of SARS-CoV-2 to better understand who the virus spreads and what happens in the lung cells in response to the disease. The Cambridge team worked with researchers from South Korea to take a sample of the virus from a patient who was infected in January after travelling to Wuhan. Using a combination of fluorescence imaging and single cell genetic analysis, they were able to study how the cells responded to the virus.

When the 3D models were exposed to SARS-CoV-2, the virus began to replicate rapidly, reaching full cellular infection just six hours after infectionReplication enables the virus to spread throughout the body, infecting other cells and tissue, explained the Cambridge research team. Around the same time, the cells began to produce interferonsproteins that act as warning signals to neighbouring cells, telling them to activate their defences. After 48 hours, the interferons triggered the innate immune response – its first line of defence – and the cells started fighting back against infectionSixty hours after infection, a subset of alveolar cells began to disintegrate, leading to cell death and damage to the lung tissue.

Source: https://today.duke.edu/

Machine Learning Predicts Heart Failure

Every year, roughly one out of eight U.S. deaths is caused at least in part by heart failure. One of acute heart failure’s most common warning signs is excess fluid in the lungs, a condition known as “pulmonary edema.” A patient’s exact level of excess fluid often dictates the doctor’s course of action, but making such determinations is difficult and requires clinicians to rely on subtle features in X-rays that sometimes lead to inconsistent diagnoses and treatment plans.

To better handle that kind of nuance, a group led by researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) has developed a machine learning model that can look at an X-ray to quantify how severe the edema is, on a four-level scale ranging from 0 (healthy) to 3 (very, very bad). The system determined the right level more than half of the time, and correctly diagnosed level 3 cases 90 percent of the time.

Working with Beth Israel Deaconess Medical Center (BIDMC) and Philips, the team plans to integrate the model into BIDMC’s emergency-room workflow this fall.

This project is meant to augment doctors workflow by providing additional information that can be used to inform their diagnoses as well as enable retrospective analyses,” says PhD student Ruizhi Liao, who was the co-lead author of a related paper with fellow PhD student Geeticka Chauhan and MIT professors Polina Golland and Peter Szolovits.

The team says that better edema diagnosis would help doctors manage not only acute heart issues, but other conditions like sepsis and kidney failure that are strongly associated with edema.

As part of a separate journal article, Liao and colleagues also took an existing public dataset of X-ray images and developed new annotations of severity labels that were agreed upon by a team of four radiologists. Liao’s hope is that these consensus labels can serve as a universal standard to benchmark future machine learning development.

An important aspect of the system is that it was trained not just on more than 300,000 X-ray images, but also on the corresponding text of reports about the X-rays that were written by radiologists. “By learning the association between images and their corresponding reports, the method has the potential for a new way of automatic report generation from the detection of image-driven findings,says Tanveer Syeda-Mahmood, a researcher not involved in the project who serves as chief scientist for IBM’s Medical Sieve Radiology Grand Challenge. “Of course, further experiments would have to be done for this to be broadly applicable to other findings and their fine-grained descriptors.”

Chauhan, Golland, Liao and Szolovits co-wrote the paper with MIT Assistant Professor Jacob Andreas, Professor William Wells of Brigham and Women’s Hospital, Xin Wang of Philips, and Seth Berkowitz and Steven Horng of BIDMC.

Source: https://news.mit.edu/

Non Invasive Breathing Aid From Mercedes Formula One

A new version of a breathing aid that can help coronavirus patients has been developed in less a week by a team involving Mercedes Formula One, and is being trialed at London hospitals.

Continuous Positive Airway Pressure (CPAP) devices have been used in China and Italy to deliver air and oxygen under pressure to patients’ lungs to help them breathe without the need for them to go on a ventilator, a more invasive process.

The new CPAP has already been approved by the relevant regulator and now 100 of the machines will be delivered to University College London Hospital (UCLH) for trials, before being rolled out to other hospitals.

Reports from Italy indicate that approximately 50% of patients given CPAP have avoided the need for invasive mechanical ventilation, which involves patients being sedated, freeing up ventilators for those more in need.

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These devices will help to save lives by ensuring that ventilators, a limited resource, are used only for the most severely ill,” UCLH critical care consultant Professor Mervyn Singer said in a statement.

We hope they will make a real difference to hospitals across the UK by reducing demand on intensive care staff and beds, as well as helping patients recover without the need for more invasive ventilation.”

Source: www.reuters.com