Within the wider worlds of engineering, manufacturing and construction, there is a growing demand for accurate computer modelling of large-scale projects. Through the use of artificial intelligence, engineers can more accurately predict and improve various aspects of construction work, from calculating the total cost of a project, to optimising the quality of concrete. Dr Ming Lu and his team at the University of Alberta are developing such artificial intelligence systems, towards ultimately overhauling how we plan large-scale engineering work.

Computational Solutions to Engineering Problems

Throughout history, engineers have relied on a combination of skill and experience, passed down from generation to generation, in order to improve upon their designs and methods over time. However, there are drawbacks to this method of gathering information. It can take a long time to train competent engineers, meaning they are often in short supply. Furthermore, some engineering jobs can be time consuming and require a lot of data processing. In essence, many aspects of the profession need to be optimised, so that engineering projects can be completed as efficiently as possible.

Towards this aim, Dr Ming Lu and his colleagues have been researching how we can implement exciting new artificial intelligence (AI) systems into the various fields of engineering. By harnessing the power of AI, he hopes that many large-scale engineering projects can be completed more efficiently and with less subjective input.

Optimising Concrete

For thousands of years, humans have been using lime to create cement, leading to the material that forms a cornerstone of our society: concrete. Increasing in popularity since the Industrial Revolution, this material has been widely used and thus closely studied. This means that there is a huge bank of knowledge on the properties of the material, which modern engineers must appreciate to achieve the best results.

One such property is referred to as the ‘slump’ of concrete, which describes the consistency of fresh concrete before it hardens. This property is essential to understand when working with concrete, as it can affect the way that the material sets, and thus the quality of the final product. As such, it would be useful if engineers could design a system that accurately predicts the outcome when producing concrete using different methods and ingredients.

Previous studies have used artificial neural networks to model the quality of different concrete mixtures and predict the outcome of specific mixing processes. While these neural networks have proven to be effective, many researchers believe that they are too complicated to fully understand and implement effectively. Thus, researchers have been attempting to develop other systems, which are easier to implement while still achieving the same standard of results.

Dr Lu and his team propose a different method of modelling this phenomenon, using what is known as a ‘model tree algorithm’. This method, which works by classifying data and sorting it into categories to better understand it, has been previously shown to be effective for this sort of work. Thus, the team began to compile data and work towards producing a system that would achieve this goal.

Through their work, the team developed a system that can analyse existing data in order to gain better understanding of the factors affecting the slump of the concrete. They then used their system to attain estimates about theoretical mixtures based on a number of possible variables. This is a major breakthrough, as it could allow engineers to better understand how concrete can be produced in an optimal way for a specific purpose, before any resources are consumed. This approach would help to make the construction industry far less wasteful – in terms of raw materials, money and time.

Cost Estimation of Steel Fabrication

Any type of construction project is going to incur large costs due to numerous factors, including the labour required. Often these costs will be calculated by first estimating the number of ‘labour-hours’ that each team will require to complete their tasks, which can be used to give a rough estimate of the total labour costs involved. However, during the planning stages of a construction project, it can be almost impossible to achieve accurate estimates, which can cause cash-flow problems down the line.

Therefore, many engineers turn to computational management systems, which can more precisely predict the labour-hour requirements for construction work, leading to more accurate cost estimations.

In a recent research project, Dr Lu and his colleagues developed a model that can accurately predict the labour costs involved in large-scale construction projects. In this case, the team used a steel fabrication project as a case study. The process of making steel, which itself is widely used in construction, has many crucial steps, each of which can have their own associated costs. Thus, it was a perfect example to test the team’s new model.

Utilising a similar process to that employed to solve the concrete problem, Dr Lu and his team implemented an AI that was able to provide reasonable estimates for the time, and therefore costs, involved in various steelmaking projects. This tool could be invaluable for assisting with the decision-making process when it comes to planning large-scale work in the future.

Calculating the Cost of Prefabricated Components

Dr Lu and his team then decided to extend the limits of their computational work and branch out into other fields of cost prediction. This time, the study wasn’t focused on the fabrication of steel, but the prefabrication of parts for construction, such as precast concrete components or prefabricated piping spools.

With a huge number of different designs, materials and components available, engineers have more choice than ever when it comes to purchasing and using pre-made parts. This can help to significantly reduce the work required, and allows engineers to use a wider array of different components within their projects.

However, pre-made parts can sometimes be expensive, so it is important for engineers to understand whether it is more cost-effective to buy such parts or to build them from scratch. In addition, engineers need to predict the time and cost required for making various structural components.

This can once again be achieved through carefully designed models, which can use existing data to establish accurate cost scaling and come up with optimal plans. Dr Lu’s team has been using data from the manufacturing of pipe spools and wall panels, in order to produce a precise and adaptable model.

Once again, the team was successful in delving into established mathematical theories and designing a system that could produce a reasonable prediction of the uncertainties in the costs involved in these processes by analysing existing data on the subject. This tool could provide a suitable way for engineers to plan their work within budget, to the greatest efficiency and cost-effectiveness.

Creating an Explainable AI System

As Dr Lu has demonstrated, well-designed AI systems can allow engineers to greatly cut down on the time they need to spend making calculations. As such, these systems help us to achieve large-scale engineering projects more efficiently.

However, while these AI systems are incredible, their complexity can lead to issues in usability. If an engineer cannot understand how an AI system calculates its results, it is difficult to evaluate how effectively it is working.

The solution is an additional branch of computer management system design, known as ‘explainable AI’. This revolves around the design and implementation of a second model, which can help to articulate the logic used by an AI system, while still allowing it to work effectively. In short, this allows the end user to understand the mechanisms that the system used to reach its results, evaluate its effectiveness and gain additional insight into the problem being solved.

Working alongside a structural steel fabricator, Dr Lu and his colleagues set out to develop a system that would not only predict the labour-hour requirement involved in steel fabrication, but would also do so in a way that can be interpreted by professionals working at the company.

‘Model explainability matters as much as prediction accuracy in engineering applications,’ says Dr Lu. ‘We wished to turn the model tree algorithm into an explainable AI, so as to reveal which inputs play what roles in predicting outputs, along with the uncertainties associated with the predicted outputs.’

When it comes to understanding how to work with certain materials, such as steel, it is hard to do better than real on-the-job experience. The AI system designed by Dr Lu’s team was not supposed to replace this know-how, but rather lend analytical decision support to complement this know-how. To ensure that these predictions were accurate, the team designed their system so that users, both inexperienced and experienced, can understand how the AI system reaches its conclusions.

Transforming the Field of Engineering

Through the inventive use of AI, Dr Lu and his colleagues have demonstrated how we can better cope with the complexities and variations in connection with construction and fabrication processes, providing construction engineers and managers with much needed decision support by making the most of the available data. The team’s systems not only make accurate predictions, but also allow users to understand how they reach their conclusions. Efforts like this may ultimately change the world of engineering, providing future generations of engineers with the tools they need to build as effectively as possible.

People who live close to busy roads and highways are exposed to high levels of traffic-related air pollution. This puts them at risk of significant health difficulties such as high blood pressure, heart attacks and cancer. The Community Assessment of Freeway Exposure and Health Studies led by Dr Doug Brugge from the University of Connecticut represent community-engaged research into the biological impact of high exposure to pollution and importantly, possible solutions to this. This work has shown that high-efficiency particulate arrestance filters are one promising intervention for minimising exposure to pollution and thus improving health.

A Serious Health Issue Worldwide

Traffic-related air pollution is a concern for the health of people living in urban areas all over the world. In the USA, exposure to air pollution is associated with 100,000–200,000 deaths annually This pollution comes in the form of gases and particulate matter of many sizes and compositions suspended in the air.

These pollutants can have serious health implications, and where people live and how long they stay there impacts a person’s exposure and their likelihood of associated illness. The air around high traffic areas can contain two to three times higher concentrations of polluting gases and particulates than other sites, and even inside cars during commuting, concentrations are high.

Ambient airborne particulate matter is one of the top five causes of morbidity and mortality globally. To improve the health of the people living in cities and traffic-heavy areas, it is vital to study local traffic pollution, which has been studied much less than regional pollution in order to understand the impact it has and to reduce exposure and health consequences.

This is the focus of a group of researchers and community partners led by Dr Doug Brugge at the University of Connecticut, USA. Starting in 2008, the original Community Assessment of Freeway Exposure and Health (CAFEH) Study investigated levels of pollution in Boston communities near high traffic volume highways. CAFEH now provides an umbrella for several ongoing research projects that collaborate with affected communities. This work is providing insights into how highway traffic pollution affects nearby communities.

Air Filtration to Lower Pollution Particle Concentration

‘I have played a leading role in a series of community-based participatory research studies and implementation projects in the Boston area for about 15 years’ explains CAFEH Principal Investigator and Steering Committee Co-chair, Dr Brugge.

Particles emitted from vehicle exhaust pipes generally make up around 30% of fine particulate matter and are regulated in the USA. Concentrations are consistent over tens to hundreds of kilometres making them a regional pollutant. But a particular concern is the high concentration of the smaller ultrafine particles commonly found in traffic-heavy areas of cities. These concentrations vary a great deal over tens to hundreds of metres and remain unregulated.

Ultrafine particles represent an emerging health burden, considering there is evidence to suggest poor health indicators and cardiovascular disease risks are elevated by their presence. ‘We started out studying the effects of traffic-related ultrafine particles on health, focusing on biomarkers of cardiovascular risk… (and) we were among the first to report associations of ultrafine particles with biological health measures and continue to study these associations. However, we rapidly shifted from studying the problem to also exploring options to reduce exposure’, explains Dr Brugge.

These explorations lead them to air purifiers, which have become increasingly popular due to the COVID-19 pandemic. These machines have the ability to reduce the concentrations of particulate matter indoors, including traffic-related particles, allergens and even viruses. They could be a promising tool although their efficacy is not well established for residential exposure to traffic particles. Therefore, the CAFEH project team conducted a pilot study that recruited low-income Puerto Ricans across 23 households in Boston and Chelsea in Massachusetts. Reducing ultrafine particles in low-income households can be especially difficult due to a lack of mechanical ventilation in their homes.

Participants were given high-efficiency particulate arrestance (HEPA) filtration devices for their homes for three weeks and a sham device that had no filtration capabilities for a separate three weeks (with the order of the intervention/sham intervention randomised and participants blinded as to which they had). Particle number concentrations were measured continuously over the six weeks. The results revealed that when HEPA filtration was in effect, the participants’ homes had 50–85% lower concentrations of particles than with sham filtration. However, the researchers did not find a statistically significant reduction in blood inflammatory biomarkers in these participants.

A number of explanations could account for this finding, including the amount of time that participants spent in rooms with the filters and serious underlying health conditions that might mask the benefits of reduced exposure to particulate matter. Therefore, the team concluded that whilst HEPA filtration does lower ultrafine particle concentration, there was a need for an improved study design to better understand the impact of filtration on health indicators.

Controlled Air Filtration Study

Dr Brugge and the CAFEH project team next developed a methodologically more rigorous study, which they conducted under highly controlled conditions. This research focused on the effect that traffic-related air pollution has on short-term changes in blood pressure, the elevation of which is a risk factor for cardiovascular disease. A total of 77 participants each spent two hours in a room near an interstate highway on three separate occasions. A HEPA filter and the opening/closing of windows and doors were used to control the concentration of particles and therefore, the exposure the participants experienced. In a randomised order, participants were exposed to low, medium and high levels of traffic pollution, with a one-week ‘washout’ period in between each test to allow valid comparisons between the different study conditions.

Systolic blood pressure (the larger blood pressure number, which represents the pressure in the heart during contraction) and diastolic blood pressure (the smaller number, which is the pressure in the arteries between beats/at rest) were measured every 10 minutes. These readings showed that systolic blood pressure increase over the two hours but that the amount of increase was lower with lower exposures. According to Dr Brugge, this study from his team provides ‘strong causal evidence that air purifiers could both reduce ultrafine concentrations indoors near a highway and positively affect blood pressure.’

Investigating Air Filtration in the Real World

Although this controlled exposure study was incredibly useful in demonstrating that near highway pollution affects health and that air purifiers could be a useful tool to reduce risk, it did not reflect real-world conditions. To understand the effectiveness of air filtration in the real world, Dr Brugge and the CAFEH project team have set out to study 200 adults who live near a highway in Somerville, Massachusetts. This study is still ongoing and expected to continue for 2–3 years.

Households in this study have been given two HEPA filters so that they could have them running in both the living and bedrooms. Either real or sham filtration is used for one month, followed by a month-long washout period and then the alternate configuration for another month. Half of the participants begin with sham filtration followed by true filtration to ensure participants were blinded as to which version they had. Unlike the previous study, this one allows participants to open windows, cook food, spend time in rooms without the filter and generally go about their lives.

This investigation is ongoing and Dr Brugge is keen to see the outcomes in due course. The primary health outcomes include systolic blood pressure and the secondary outcomes include diastolic and central blood pressure. Blood levels of C-reactive protein, which is an indicator of inflammation, are also being measured, as well as D-dimer, a blood clotting factor. Throughout the study, other factors will be monitored and used to evaluate the success or failure of the air filtration intervention. These include indoor and outdoor monitoring of particulate pollution levels, size and composition of the particulates in the air, tracking the amount of time spent in the room with the filter and in addition, interviews to gain qualitative feedback from participants.

Once all these elements and the health outcomes have been assessed and analysed, the CAFEH project team will be able to determine whether the daily habits of people impact the potential benefits of filtration shown in the previous study. If successful, this promising research could provide evidence for an effective intervention to reduce the exposure to traffic-related air pollution in people who live near busy roads. In turn, this could improve the cardiovascular and overall health of these residents.

Utilising the knowledge built up through ongoing research, Dr Brugge and colleague Sharon Ron (Metropolitan Area Planning Council) wrote a white paper to inform policy for near roadway exposure to ultrafine particles. Their paper outlines what is known about the health risks of exposure to traffic-related particulate matter and why it should be addressed. Their recommendations cover high-efficiency air filtration, noise barriers, building away from highways and many others.

With considerable work already completed and even more underway, Dr Brugge and the CAFEH project team remain committed to influencing health outcomes related to local traffic pollution at a policy level, which will, ultimately, be critical for the large-scale protection of health. By expanding our understanding of this underappreciated type of pollution, the CAFEH project team is also raising awareness about this invisible, odourless toxic pollution that comes from motor vehicles.

Although human immunodeficiency virus (HIV) is still prevalent worldwide, life-saving antiretroviral drugs can now prevent an infection from progressing into acquired immunodeficiency syndrome (AIDS). Nevertheless, people who are HIV-positive are still at increased risk of developing neurological disorders and cardiovascular diseases, known as co-morbidities. Professor Michael Bukrinsky from the George Washington University in Washington DC works to understand the underlying biological mechanisms that lead to these disorders. His research has produced interesting results that demonstrate the role of altered lipid (cholesterol) homeostasis in HIV-infected cells and how this comes to pass.

Uncovering Causes and Proposing Solutions

Over 37 million people worldwide are infected with human immunodeficiency virus (HIV), and an estimated 1.7 million of these are children. The virus replicates by entering immune cells, particularly CD4+ T cells and macrophages, and multiplying inside them. Once new HIV virions have been produced, they burst out of the host cell to repeat replication, which kills the cell in the process. Due to viral reproduction in the immune cells, the immune systems of people infected with HIV are greatly impacted and they become susceptible to additional diseases.

HIV is spread via certain bodily fluids and can, therefore, be transmitted during unprotected sex, sharing needles and during pregnancy or breastfeeding. Although initial infection may result in a few weeks of flu-like symptoms, many people usually will not present any symptoms for a number of years. However, if it is left untreated, HIV leads to acquired immunodeficiency syndrome (AIDS), whereby the immune system is so damaged and unable to repair itself, that previously minor infections and illnesses become life-threatening.

Thankfully, treatments have been found that allow those living with HIV infection to live long, healthy lives. These treatments come in the form of antiretroviral drugs, which prevent the virus from infecting new T cells and macrophages and in turn, allow the immune system to repair itself. Eventually, taking these tablets every day results in an undetectable viral load, meaning that the amount of HIV in the body is so low that it cannot be detected by a test and the risk of transmission is minimal.

Although impressive progress has been made to improve the lives of those living with HIV/AIDS, research has shown that they still have a high risk of co-morbidities. These are ailments that are not caused by a damaged immune system, but by other, HIV-associated, yet less clear mechanisms. For example, people who are HIV-positive are more likely to have high blood pressure and high cholesterol, develop heart disease, kidney disease, neurological issues and cancer.

Uncovering the reasons behind and solutions to these troubling HIV co-morbidities is Professor Michael Bukrinsky from the Department of Microbiology, Immunology and Tropical Medicine at the George Washington University in Washington DC. In collaboration with scientists across the globe, Professor Bukrinsky is working to elucidate the molecular mechanisms behind these comorbidities so that they can be targeted with therapies.

Lipid Homeostasis in HIV-infected Macrophages

The role of cholesterol in cardiovascular diseases has been well established. However, prior to Dr Bukrinsky’s studies, there has been little investigation into the impact HIV has on cholesterol homeostasis. Cholesterol is an essential lipid (or fat) in the body that aids in the synthesis of cell membranes, hormones and vitamins. But when there is an imbalance between the high-density lipoprotein (often called ‘good’ HDL) and low-density lipoprotein (‘bad’ LDL) forms, the risk of cardiovascular diseases increases. An excess of LDL in the bloodstream also frequently leads to atherosclerosis, which is a thick deposit of cholesterol, fat and calcium within an artery. As this deposit (known as a plaque) grows, the artery narrows and a blood clot trapped in the small space can result in a heart attack or stroke.

Professor Bukrinsky and his colleagues carried out research to discover why this is so common in HIV patients. His team included Professor Dmitri Sviridov at the Baker Heart and Diabetes Institute, Dr Zahedi Mujawar (Astra Zeneca, a former graduate student), and Professor Michael Fitzgerald at Harvard Medical School. They discovered that the virus inhibits a cholesterol transporter protein in macrophages – a type of white blood cell in the immune system. This transporter is called ATP-binding cassette transporter A1 (ABCA1) and when it is prevented from functioning normally, cholesterol cannot leave the macrophage cell. The inhibition of cholesterol efflux is a condition previously documented as causing high atherosclerosis risk.

The team ascertained that in HIV-infected cells, this inhibition event was regulated by an HIV protein called Nef, and further investigation found that two biological mechanisms were responsible. First, Nef binds to and down-regulates ABCA1, meaning that it reduces its abundance – and thus, its ability to facilitate cholesterol efflux. Second, Nef interacts with another cellular protein, calnexin, which regulates glycosylation and maturation of ABCA1. As a result, ABCA1 cannot mature and is sent to the degradation pathway as a cellular defence reaction to the accumulation of immature proteins. Together, these mechanisms cause the reduction of ABCA1 and prevent the transfer of cholesterol to apolipoprotein A-I, a major component of HDL cholesterol. These processes result in an imbalance of blood lipoprotein cholesterol.

Studies by Dr Brichacek in Professor Bukrinsky’s group further demonstrated that the described above effects of Nef are also reproduced by treating cells with Nef-containing extracellular vesicles (EVs). These vesicles are produced by HIV-infected cells and persist in infected individuals even when the virus is undetectable. The reason for this is that the antiretroviral drugs used to treat infected patients prevent new infections, but do not block low-level production of Nef in infected cells. Circulation of these Nef-containing EVs may be the reason for the persistence of co-morbidities in successfully treated patients with undetectable HIV load.

Professor Bukrinsky’s studies also showed that HIV-infected and Nef-EV-treated macrophages accumulated a significant amount of lipids within them. This gave them a resemblance to foam cells, a type of macrophage that sticks to blood vessel walls, internalising LDL and facilitating atherosclerosis formation. In addition, cholesterol enrichment leads to changes in the cellular membrane, increasing the size and number of cholesterol-rich islets called ‘lipid rafts’. These lipid rafts are places where cellular receptors sensing and responding to inflammation accumulate, making cholesterol-rich macrophages overresponsive to inflammatory stimuli. This mechanism may underlie another important cause of atherosclerosis in HIV-infected individuals, persistent inflammation. When Professor Bukrinsky stimulated cholesterol efflux from HIV-infected macrophages, the HIV viruses that were produced inside were much less infectious. This shows that the virus requires a certain, high cholesterol level within its host cell to maximise its replication ability, and HIV achieves this by preventing cholesterol efflux.

These exciting results revealed brand-new mechanisms by which HIV Nef regulates intracellular lipid homeostasis within HIV-infected cells and cells encountering Nef-containing EVs. In this way, Professor Bukrinsky’s work showed how HIV infection of macrophages contributes to the co-morbidity of atherosclerosis and its associated risks. A subsequent study with Dr Ruth Hunegnaw (National Institutes of Health, a former graduate student) and Professor Alexei Adzhubei (Engelhardt Institute of Molecular Biology) and Amol Kulkarni (Howard University) revealed that specific interactions with Nef could be blocked and therefore potentially targeted with drugs to prevent HIV from impacting cholesterol homeostasis and thus, reduce the risk of atherosclerosis.

Neurodegeneration in HIV

Professor Bukrinsky’s group has further studied lipid rafts, the solid structures in the plasma membrane and the barrier between a cell and its surroundings. These rafts are full of cholesterol molecules and help to mediate interactions with the cell’s environment. Whilst these interactions are usually beneficial, pathogens can take advantage of lipid rafts and use and even modify them to facilitate their own replication. Lipid raft therapy can be implemented as a treatment for viral infections by reducing unwanted modifications of lipid rafts or preventing viral interactions with these regions, and Professor Bukrinsky suggests this may even be an effective treatment for COVID-19.

Similar to atherosclerosis, lipid raft structures are key players in HIV-associated neurocognitive disorders (HAND), which are very frequent even in HIV patients receiving treatment. HAND is often seen as behavioural changes, declining cognitive function and motor impairment, and Professor Bukrinsky studied the role of Nef in HAND.

His studies showed that the Nef protein secreted from HIV-infected cells in EVs was rapidly internalised by neural and glial cells, and as seen in previous experiments, reduced ABCA1 abundance and cholesterol efflux, and also increased the number of modified lipid rafts in the plasma membrane.

Nef EVs also caused the increase and redistribution of amyloid precursor protein (APP) and Tau to lipid rafts, both of which are associated with brain disorders. With the addition of the activation of inflammatory pathways, Professor Bukrinsky observed consequent neuronal functional impairment which was reversed when the lipid rafts were disrupted. These findings were supported by evidence from brain tissue of deceased HIV patients with HAND, which showed lower levels of ABCA1 and more lipid rafts compared to HIV-negative brain tissue. Additionally, an abundance of Nef in the HIV-positive tissue correlated with APP and Tau levels. Finally, Jessica Schenck, a graduate student in Bukrinsky’s laboratory, demonstrated that Nef-carrying EVs cause demyelination of the brain. Myelin forms cholesterol-rich sheaths of neurons required for neuronal functions, and cholesterol transport is essential for their maintenance.

As a result, Professor Bukrinsky concluded that the cholesterol homeostasis alterations arising from the presence of Nef in the brains of HIV-infected people may contribute to neurodegeneration and HAND. Understanding the underlying mechanisms of disease facilitates the development of therapies to target them, and Professor Bukrinsky’s research suggests that treatments preventing Nef-induced alterations of cholesterol homeostasis and/or normalising lipid raft structure may reverse HAND.

Discovering HIV-Specific Memory

The most recent discovery by Larisa Dubrovsky, a scientist in Professor Bukrinsky’s group demonstrated that cells treated with Nef EVs acquire a memory of this encounter. This memory is maintained by epigenetic (not involving genetic material) changes that regulate the expression of pro-inflammatory and lipid metabolism genes. As a result, cells, and in particular macrophages, become overresponsive to inflammatory stimuli.

This mechanism supplements and potentiates the mechanisms described above. The length of this memory is extended by the possibility that Nef EVs affect progenitor cells in the bone marrow, thus potentially impacting cells produced from these progenitors for a very long time. This finding adds another complication to be considered in the efforts to cure HIV infection. Indeed, besides silencing HIV, either by eliminating it from the body or by permanently blocking the virus from producing proteins and nucleic acids, care should be taken to clear the memory formed during the encounter with the virus or Nef EVs. Professor Bukrinsky’s studies support the development of approaches aimed at reversing or blocking the establishment of this memory.

Discovering and Blocking a Pro-inflammatory Protein

While Professor Bukrinsky has devoted much of his research to HIV and its co-morbidities, his laboratory explores other avenues of research as well. These include obtaining a better understanding of inflammatory diseases, the biological interactions that cause them and how they can also be targeted by drugs. Studies with Professors Yurchenko (University of Ostrava) and Sherry (Feinstein Institutes) identified that extracellular proteins called cyclophilins and their receptor on immune cells called CD147 regulate various inflammatory disorders such as acute lung inflammation, cardiovascular disease and rheumatoid arthritis. CD147- mediated signalling by cyclophilins guide immune cells to the sites of inflammation.

Therefore, Professor Bukrinsky and his colleagues believed that targeting these interactions would be a promising possibility for anti-inflammatory therapeutics. Together with Professors Stephanie Constant (National Institutes of Health) and Gunter Fischer (Max Planck Research Unit) he developed and patented an exciting new drug that blocks the action of extracellular cyclophilin A. They hope that this could be used to alleviate the symptoms of both acute and chronic inflammation and mitigate the subsequent risks associated with it.