Medical Biophysics

Preventing Freezer Burn and Saving Human Tissue

It sounds simple, but Shah Razul’s work involves trying to understand what water molecules do when they get to below zero degrees Celsius because the possibilities that understanding offers could be life-changing.

When water crystallizes, its volume increases and it forms a regular structure that destroys, for example, cells, or, in the case of an organism, its delicate membranes and systems. As most of us will know, when food is frozen and we leave it too long, the ice separates out, leaving the food “freezer burned.” A wasted chicken breast is one thing, but this problem also exists, for example, in cryopreservation of cells, tissues and organs.

“When water’s in a close-to-frozen state, it has some interesting behaviour,” says Razul, an associate professor of chemistry at St. Francis Xavier University. “We’re trying to understand what the water molecules are doing.”

Razul’s overarching goal is to find a way to keep water from forming crystals and then applying that learning to real-world problems in areas such as food and health care.

“If we can, we can solve many problems related to freezing,” he says, including, for example, potentially creating environmentally safer antifreeze products to replace or minimize the use of road salt.

He looks to creatures such as the wood frog, which uses glucose to stop the freezing process from destroying its tissues in winter. Razul, therefore, is running simulations to look at the process of freezing and analyze what water molecules are doing in the fractions of a second just before they form crystals and freeze.

“A lot of my work now is involved with understanding how small biomolecules, such as different kinds of sugars and salts, stop water from freezing or slow down the process,” he says, adding that he wants to figure out a biomolecular combination that would achieve this goal.

Over the past five years, he’s been using computational principles to develop a cryoprotectant to preserve lobster meat.

“We’ve tried it and it works,” he says, adding that some companies from overseas are testing his system currently. “We had a taste test in Atlantic Canada, and we’ve published a couple of studies where the public indicated that it tastes as if it was cooked yesterday, preferring it to frozen lobster meat that is sold currently.”

His next frontier is looking at the ways in which his process can preserve muscle cells and brain cells to see what kind of applications it might have in the healthcare field.

“No one has a definitive answer to how these cryoprotectants work at the molecular level,” he says. “It’s basically all trial and error. We try a little bit of this and a little bit of that and see whether it works.”

To do his work, he’s watching the behaviour of water in minute detail — fractions of seconds — and then he replicates the test multiple times.

“Because of that, I generate a lot of data,” he says, and that’s why he couldn’t do his work without ACENET’s high performance computing.

“It’s almost impossible to do this work without ACENET,” he says. “I would need a year and a half to do it sequentially as opposed to one month with ACENET.”

Machine Learning: Connecting the Brain to the Body

Xianta Jiang uses machine learning and wearable-sensor technology to study how the brain works in telling the body what to do and how to move. Currently, he’s using these techniques to solve a health-care problem for those who have prosthetic hands. But he doesn’t work in a hospital setting; rather, he works in a computer lab.

A professor of computer science at Memorial University, Jiang works with a team of students to study all of the different ways humans use their hands. With that information, they’ll use machine learning to help those who wear prosthetics control them.

“Controlling the prosthetic hand is a super difficult problem to solve, because when a human’s hand is amputated, the brain has a harder time communicating to the arm about how to maneuver the prosthetic limb,” Jiang says. “We are trying to help people control them as naturally as possible, and without surgery. To try to solve the problem, we use muscular sensors attached to a part of the arm to infer movement intentions from the area of the brain used to control the hand.”

However, it’s difficult to make the connections that allows the brain to send the right signals to the hand.

“For that, we attach a camera to the prosthetic and when the camera can identity the target, the hand can configure correspondingly — just like self-driving,” he says.

That’s the hands-on part of the research, but, in the end, much of the work is done by his students in a computer lab using the high-performance computing resources of the Digital Research Alliance of Canada and ACENET. They identify the grasp types the prosthesis user will need and use computer modelling to fine-tune the fit.

“It’s quite a basic question, but we need a lot of computing resources for it,” he says. “We are working to cover 95 per cent of daily life grasps with a total of 16 movements and need a lot of data to train this model, so we use high-performance computing.”

He said he couldn’t do his work without ACENET’s services.

“It’s not affordable to purchase the computing power we’d need,” he says. “We need a lot of memory.” In 2022 alone, Jiang’s group used 128 CPU years and 23 GPU years of compute power.

Another of Jiang’s projects involves monitoring human activity using wearable devices such as smartwatches. The goal is to find a way to allow rehabilitation staff or sports coaches to monitor their patients’ progress digitally. This work is in the early collaboration stage and not yet commercialized. “For this one, we’ll collaborate with industry over the next few years,” he says.

Improving the Flow of COVID-19 Screening with X-rays and AI

Professor Moulay Akhloufi didn’t expect to join the effort against COVID-19. And while it may be difficult to imagine X-rays and artificial intelligence helping in the face of a world-paralyzing virus, that’s exactly what Akhloufi and his computer science lab—PRIME—at l’Université de Moncton are hoping to do. Akhloufi and two of his graduate students, Mohamed Chetoui and Andy Couturier, are teaching AI to recognize the signs of COVID-19 on X-ray images of patients’ lungs. So far, the results look promising. PRIME stands for Perception, Robotics and Intelligent Machines. The group uses deep learning, the science of developing machines that can learn by example. “It consists of various types of algorithms that form artificial neural networks,” says Akhloufi. In simpler terms, they use math to mimic the human brain. The applications are as varied as modelling forest fires and autonomous navigation of drones. “We’ll take data representing this or that category and train the network to differentiate them,” explains Akhloufi. By showing the machine images of lungs that are healthy, or have pneumonia or COVID-19, it gets progressively better at telling them apart. When the pandemic hit Canada, Akhloufi’s team wasn’t overly affected. “We’re in computer science,” says Akhloufi. “We adapted very quickly to working from home.” With remote access to ACENET and Compute Canada’s computing power to pummel through their complex calculations, it was business as usual. Chetoui, who was finishing his master’s, then had the idea to adapt his project to this new coronavirus disease. He had developed algorithms to recognize and give severity scores for diabetic retinopathy, a complication from diabetes causing damage to the blood vessels of the eye’s retina. A crossover to COVID-19 seemed feasible. Around mid-February, Spain and Italy’s health systems were overloading, creating a shortage of materials necessary for the tests that diagnose the disease. Radiologists began using X-rays, CT scans and ultrasounds of patients’ lungs to make quicker diagnoses and made the images public. With this first batch of images and others coming from Chinese studies, the team developed two algorithms. One gives the probability of infection and the other, a heat map of the affected areas. Near the end of March, they put up a website that allows medical professionals to upload and obtain readouts for their X-ray images. “When the Institut du Savoir Montfort joined us, the project took a whole other dimension,” adds Akhloufi. The Hôpital d’Ottawa’s research institute supplied them with more images to improve the tool.Akhloufi says that predictions on a set of more than 5,000 images are now close to 98 per cent accurate and will only get better with more data. Because most images come from patients presenting symptoms, it is too early to tell if AI will be as effective with asymptomatic people. Although a team at the University of California San Diego detected one case through a similar technique, they’ll need more targeted data to be sure it wasn’t just a lucky guess. Akhloufi hopes AI will help improve the flow of COVID-19 screening by offering radiologists and other medical professionals a fast and reliable tool to inform their diagnoses. “The aim isn’t to replace radiologists,” he specifies. “When it comes to my health, I’d rather speak to another human.”

Decoding the Human Brain

Jacob Levman is trying to figure out what parts of the brain develop abnormally in children with autism, so it’s surprising to learn he’s a professor in the department of mathematics, statistics and computer science at St. Francis Xavier University in Antigonish, Nova Scotia. But it turns out it makes sense. His bachelor’s degree was in computer engineering and his master’s in electrical and computer engineering. “My research has involved various contributions in the statistics domain, so I guess I was a good fit when they sought a Canada Research Chair in bioinformatics, which is a combination of medical stuff and computer technology.” Indeed it was. Levman’s PhD from the University of Toronto was in medical biophysics. “My background is really interdisciplinary,” he says. “Medical biophysicists are the people who build better MRI machines and come up with therapies such as radiation treatments. My research combined my background in computers with MRI data and MRI technology.” Levman did post-doctoral studies at Oxford’s department of biomedical engineering, looking at how to apply advanced computer technologies to help predict or identify the extent of tissue damage due to stroke from MRI examinations. His post-doctoral studies at Harvard looked at both healthy and autistic patients in pediatrics at Boston Children’s Hospital Today, doctors diagnose autism by symptoms and behaviours, but Levman thinks there are probably underlying anatomical or physical issues associated with autism.” “We look at the differences in the way the brains of children with autism present,” he says. “Potentially, our work could help inspire new therapies by helping us better understand which regions of the brain are developing abnormally and in what way.” “We’re also looking at the genetic profiles of children with autism and comparing those with the presentation of the brain from an MRI examination,” he says. “If we can say an abnormal mutation in autism seems to be highly related to this area of the brain forming abnormally, that might help decode the mysteries behind autism.” To that end, he’s completed the world’s largest autism-MRI study, reviewing the volumes of substructures in the brain. But his work also has a technical side, through which he develops general-purpose machine-learning algorithms or pattern-recognition technologies that help extract measurements from examinations using existing formal computer science methods and new ones he’s trying to create. To do his work, he uses ACENET and Compute Canada computer clusters. Just to do a basic analysis of an MRI from one child with autism, extracting thousands of measurements from across the brain, would take nine hours on one computer. A large study involves about 2000 examinations, which would take about 18,000 hours, or more than two years. “If you want to get the job done in a reasonable amount of time, you need to do parallel processing — breaking apart a huge job into a bunch of smaller ones that run simultaneously on supercomputers ,” Levman says. “ACENET and Compute Canada’s resources are very helpful for this. Without them, it would be impossible to get these things done in a reasonable time. In addition to using their computer resources, Levman has used ACENET’s training programs for every one of his lab students. “It’s been extremely useful — essential, actually,” he says.

Outsmarting Superbugs

The evolution of antibiotic-resistant superbugs is one of the biggest threats facing medicine today. Modern antibiotics – long the key to fighting deadly infections – have little effect on the new resistant strains of bacteria that commonly infect hospital patients who are in a weakened state. Now a Cape Breton University scientist is working with a Halifax-based drug company to develop one possible solution to this looming problem. Matthias Bierenstiel is an associate professor of inorganic chemistry and chair of the chemistry department at Cape Breton University. Much of his research involves studying and synthesizing new transition metal complexes – complex molecules that contain two metal centres. Some of the complexes he studies have been found to possess antimicrobial properties that are capable of switching off a bacterium’s resistance to antibiotics. Bierenstiel is helping to develop a way of synthesizing polymer molecules that can be used to fight drug resistant bacteria. “My area of expertise is in the binding of the iron to the polymer,” he says. There are two fundamental questions he’s trying to answer. The first is how these polymers can be manufactured to use as pharmaceuticals. The other is how exactly does the process work. “One of the questions we’re trying to answer is how the polymer wraps around iron molecules. The ACENET network gives us the computer power to model it to examine ways that it binds together. We can also use ACENET to make the next generation of compounds better.” Bierenstiel is working with Chelation Partners, a Halifax-based development stage company. that is using his research in the quest to develop a new platform of chemically synthesized chelating compounds that withhold iron from pathogens. “ACENET allows us to model the reactions without the need to go into the lab. Some runs take several weeks. Even with a supercomputer it takes a considerable amount of time.” Bierenstiel first used the ACENET system six years ago. He turned to it again last year when he began working with Chelation Partners. “My interest is in making a compound and putting it in someone else’s hands to develop and distribute,” says Bierenstiel. Bierenstiel’s research has attracted more than $2 million in equipment and operating funds over the past six years, including from the Canada Foundation for Innovation (CFI), Natural Sciences & Engineering Research Council of Canada (NSERC), Springboard Atlantic, Mitacs, Innovacorp and the National Research Council Canada (NRC).

Developing Artificial Lung Surfactant

Lung surfactant is a material that lines the air-sacs in the lungs and is essential for breathing. Premature babies are often born before they have a chance to produce sufficient lung surfactant and they need to be given surfactant in order to help them breathe. This surfactant is generally sourced from animals. People of any age who are seriously ill or injured also frequently have damage to their lung surfactant, which impairs their ability to breathe. One such condition, called Acute Respiratory Distress Syndrome (ARDS), affects 150,000 people per year in the US, and has a fatality rate of about 40%. Unlike with premature babies, treating adults with surfactant derived from animals has been very challenging for two reasons. First, adult lungs are larger than a baby’s and it’s therefore difficult to get enough animal surfactant together to cover it. Second, there are hostile conditions present in the lungs of ARDS patients that deactivated their own lung surfactant, and that rapidly inhibits surfactant. Dr. Valerie Booth is working with a multi-disciplinary team at Memorial University and a research team in Spain to understand the essential features of the proteins in natural lung surfactant and to use this knowledge to develop artificial lung surfactant treatments that are more resistant to deactivation. Specifically, Booth’s research focuses on Surfactant Protein B (SP-B). SP-B is of particular importance because unlike the other lung surfactant proteins, you absolutely can’t live without it in your lungs. Furthermore, unlike most essential proteins, we strangely do not know the three-dimensional structure of SP-B, or what it looks like. Consequently, we don’t understand how it works. This is because SP-B is exceptionally hard to work with experimentally. SP-B is very hydrophobic, a fancy word for sticky. It likes to stick to greasy, lipid molecules instead of to water. Despite the challenges, it is possible to use experimental techniques to find out small pieces of information about its structure. However, the key to unlocking SP-B’s molecular structure is through running simulations on Compute Canada infrastructure. Booth’s group uses these simulations to make predictions about what SP-B looks like, and then compares those to the experimental data in order to provide the best possible picture of SP-B. Proteins are like tiny molecular machines and we can’t figure out how they work until we know what they look like. Determining what SP-B looks like tells researchers how it works. Understanding how we breathe will help Booth develop a treatment for ARDS. In fact, she, two of her PhD students and a research assistant are currently working towards a provisional patent for a formulation for artificial surfactant, with the hope it will result in an industrial partnership. _Illustration courtesy of Mohammad Hassan Khatami_