Multidisciplinary research group has elaborated comprehensive analysis about the Hungarian experiences of the first wave of Coronavirus pandemic

As a part of an interdisciplinary research group, Sándor Zsolt Kovács (junior research fellow, ELKH KRTK RKI) and Annamária Uzzoli (senior research fellow, ELKH KRTK RKI) have been co-authors of a recently published article Translating Scientific Knowledge to Government Decision Makers Has Crucial Importance in the Management of the COVID-19 Pandemicwhich came out in the prestigious journal Population Health Management. The study deals with the course of the first wave of the coronavirus (COVID-19) pandemic in Hungary, in which the authors modelled the effects of the epidemic on the health care system, the economy and regional differences.

The members of the interdisciplinary research group („Translational Action and Research Group against Coronavirus” – acronym: KETLAK) have applied methodologies of different scientific fields (medical science, mathematics, economics, geography and regional science) and included the first results about the Coronavirus pandemics. During March and April 2020, the KETLAK group elaborated two comprehensive research report in which they have identified different problem fields and suggested opportunities for solving them. The introduced models within this article estimated the source and capacities of healthcare, the impacts of “re-opening”. These models have also pointed out on the optimal testing and tracking strategies by different spreading scenarios of the virus. Based on these results, the alternatives of a national testing and screening programme has been determined during the first wave of the pandemics in Hungary.

The research results provided crucial background sources for the Hungarian Government to make strategic decisions about the allocation of healthcare capacities, maintaining the lock-down, as well as the actual testing strategy. These research results supported the decision-makers to take the regional, social, and healthcare differences into consideration (Figure 1).

Figure 1: Regional differences in Hungary. Source: Katalin Gombos, Róbert Herczeg, Bálint Erőss, Sándor Zsolt Kovács, Annamária Uzzoli, Tamás Nagy, Szabolcs Kiss, Zsolt Szakács, Marcell Imrei, Andrea Szentesi, Anikó Nagy, Attila Fábián, Péter Hegyi and Attila Gyenesei (2020).

Reviewing regional differences, map “A” shows the population over 65 years old. Map “B” demonstrates the capacity of intensive care units (ICU) with the number of available ICU beds calculated for 100,000 people. The map “C” illustrates the estimated GDP loss related to the impact of pandemics. The values are normalized and then scaled between 1 and 1.5 to illustrate the differences among the Hungarian regions. The map “D” shows the results of the recently calculated “Complex Health Distance Index” (CHDI). The values of the index have been calculated on a scale from 1 to 5. The higher values of this indicator represent poor health conditions of the local population and barriers in access to health care, while districts with lower values (mostly county seats and Budapest) have the best health conditions and access to health care institutions with outpatient care.

Hungary managed the first part of the COVID-19 pandemic with low infection rate in a European comparison. It also confirms that the formation of multidisciplinary research groups is essential for policy makers in times of epidemics. The establishment, research activity, and participation in decision-making of these groups, such as KETLAK, can serve as a model for other countries, researchers, and policy makers not only in managing the challenges of COVID-19, but in future pandemics as well.

The full text of the article is available here: https://www.liebertpub.com/doi/full/10.1089/pop.2020.0159

The members of the KETLAK research group: Katalin Gombos (University of Pécs), Róbert Herczeg (University of Pécs), Bálint Erőss (University of Pécs), Sándor Zsolt Kovács (ELKH KRTK RKI), Annamária Uzzoli (ELKH KRTK RKI), Tamás Nagy (University of Pécs), Szabolcs Kiss (University of Pécs and University of Szeged), Zsolt Szakács (University of Pécs), Marcell Imrei (University of Pécs), Andrea Szentesi (University of Pécs and University of Szeged), Anikó Nagy (Heim Pál National Pediatric Institute), Attila Fábián (University of Sopron), Péter Hegyi (University of Pécs and University of Szeged) and Attila Gyenesei (University of Pécs and University of Bialystok).

The power of teamwork: Hungarian researchers help open up a path for more effective coronavirus drugs

An international collaboration has led to a breakthrough in inhibiting the function of the SARS-CoV-2 main protease, one of the key proteins of Covid-19. Although research began barely half a year ago, by applying the molecular LEGO concept, the consortium was able to identify more than 70 molecules that may serve as a good starting point for the development of drugs.

The Medicinal Chemistry Research Group of the ELKH Research Centre for Natural Sciences (TTK) is also participating in the research program, working in collaboration with research teams from the University of Oxford, the Diamond particle accelerator in England and the Weizmann Institute in Israel with the aim of finding new ways to treat COVID-19 infection by inhibiting the coronavirus proteins of SARS-Cov-2. The findings of the consortium have been published in the journal Nature Communication (https://www.nature.com/articles/s41467-020-18709-w) and the results have also highlighted in an editorial article (https://www.nature.com/articles/s41467-020-18710-3).

The research began with the isolation and purification of viral proteins, which resulted in the identification of a protein that is essential for viral replication, usually referred to as the SARS-Cov-2 main protease. This enzyme is responsible for the production of proteins that are important for the viability of the virus and which are encoded by the virus’s genetic stock. This means that its effective inhibition prevents the virus from multiplying. In terms of treatment options, it is encouraging that the function of the virus’s main protease is fundamentally different from that of human proteases. This means that the inhibitors developed are not expected to have the dangerous side effects of protease inhibition.

The research was based on the structure-based drug design already used successfully in the development of anti-viral drugs for the treatment of AIDS, where the three-dimensional structure of the target protein had to be determined first. Although the first protein structure was unveiled by German researchers in Science magazine on 24 April (DOI: 10.1126/science.abb3405), an international consortium that included Hungarian researchers was already working hard to identify promising new molecules. With this aim in mind, researchers have developed a new, efficient method based on the molecular LEGO concept, which enables efficient recognition of simple molecular building blocks, or fragments, that bind to a protein.

An essential element of the solution is that the fragments designed by the Hungarian research group not only find suitable cavities in the protein, but also react with the protein there, causing them to attach to the pockets of the protein. These molecules form a strong and lasting interaction that prevents the drug molecule from leaving the binding site, ensuring that the inhibition becomes permanent. The molecular sets developed by Hungarian, English and Israeli researchers were studied in the Diamond particle accelerator in England. The crystals of the protein were individually soaked in a solution of 1,250 different fragments, and the location of the protein-bound fragment 74 in the protease binding pocket was determined by X-ray diffraction.

 

A protein-bound fragment in the coronavirus major protease pocket

Thanks to the new procedure and particle accelerator, the measurements were completed in less than a month. Analysis showed that several of the tested Hungarian-developed reactive fragments proved to be effective. This can serve as a promising starting point and contribute to the development of new Covid treatments. One unique feature of the Hungarian building blocks is that the function of additional reference proteins was not inhibited, something which further reduces the risk of side effects. The Hungarian research group is already building on these promising fragments. This is supported not only by the Hungarian National Research, Development and Innovation Office but also by the UK Foreign Office.

The molecular LEGO concept

It has been more than a hundred years since Emil Fischer and Paul Erlich traced the effectiveness of drugs back to molecular interactions between molecules and proteins in the body. With the increase in knowledge concerning the structure of proteins, it was also found that drug molecules exert their effects by binding to the smaller or larger cavities of proteins. Identifying such cavities, as well as molecules capable of forming appropriate interactions within them, is one of the greatest challenges in the early stages of pharmaceutical research. Until the late 20th century, attempts were made to experimentally select molecules that fit into cavities from molecules previously produced for other purposes. Thanks to a new approach, the foundations of a molecular LEGO method based on testing and building molecules (fragments) that are significantly smaller than those of pharmaceuticals has been set out in the last ten years. With this method, the search for ligands begins with an examination of the binding of fragments and is based on the recognition that such molecules are more likely to bind to the cavities of proteins than larger molecules of drug candidate size. As a result, a starting point can be provided by screening a directory that already contains a few hundred or few thousand fragments. These starting points can be used for further development, taking into account the characteristics of the cavity, or in the event of several matches, may also lead to new candidates for medication.

A virus bubble instead of loneliness? According to the KRTK Game Theory Research Group, this may slow down the spread of the coronavirus epidemic

A recent study at the Institute of Economics of the Centre for Economic & Regional Studies (KRTK) at ELKH examines cooperative games where interactions between coalitions take place through a network that players can otherwise shape themselves. If the members of society differ only in their social needs, then society is naturally divided into roughly homogeneous groups according to this characteristic, where the night watchmen form smaller groups and the prima donnas create larger groups. Thus, in the time of an epidemic, all members of society receive the optimal combination of social life and the associated risk of infection.

The global coronavirus epidemic has changed our daily lives in several respects. We wash our hands regularly and wear masks in public transport, shops and offices to prevent infection during such necessary, accidental encounters. However, at the time of the lockdown, we were allowed to stay in one place only with members of our household.

The latter caused innumerable difficulties. Think of the elderly in need of care – obviously, we should not stop caring for them – or young families where grandparents help them out regularly and do not want to give up on their grandchildren because of the coronavirus, or think of single people, for whom this restriction is much like solitary confinement. Regardless of the size of the household, we may rightly feel that such a restriction on our social lives no longer meets our needs, and after a full day of video conferencing with colleagues, we no longer want to meet our friends in the same way.

Disciplined people adapt to the circumstances and tend to become depressed, while others, taking the risk of infection, breach the restrictions. It’s hard to condemn them for this: over the millennia, man has become so accustomed to living in a large crowd with his peers that he can’t stand being alone today. Of course, a night watchman and a prima donna require company to a different extent, but this need rarely matches the size of their household. As a result of such offenses, the epidemic also spreads among households and, through some offenders, it can reach a large part of society (see figure belowon the left).

The so-called ‘virus bubble’ can provide a solution to this problem. The essence of this system, which has been formally introduced in several countries (New Zealand, Canada), is to create a circle of companies (a ‘bubble’) whose members are in contact with each other, but not with others, or only to the extent necessary. A virus bubble, by definition, consists of one or more households and provides an opportunity to care for an elderly grandmother (she should be included in the bubble) or joint programmes with a group of friends (if they are all members of the bubble). In general, we can say that this method satisfies social needs to a much greater extent, and thus, without external contact, the infection does not spread from bubble to bubble.

 

 

The virus bubble is based on mutual agreement and is thus an exciting field of application for cooperative game theory dealing with exactly such groups, so-called coalitions. At the same time, it is important to recognize that the risk of infection depends not only on how many and who are members of the virus bubble or what virus bubbles are created around us, but also on the network of relationships that are actually live. Relationships are shaped by individuals, taking into account the pros and cons of face-to-face contact. If the company offered by the bubble is not enough, and someone would take the risk of infection for another external contact, it would be more appropriate to form a larger bubble.

A recent study by the KRTK Game Theory Research Group examines cooperative games where interactions between coalitions take place through a network that players can otherwise shape themselves. If the members of the society differ only in their social needs, the society is naturally divided according to this property into roughly homogeneous groups, where the night watchmen form smaller groups and the prima donna form larger groups. Thus, every member of society receives the optimal combination of social life and the associated risk of infection.

Real life is much more exciting and complex than this, but the results provide many lessons in practice. The virus bubble is an effective and proven antidote to the epidemic, in case members only have personal contact with the other members of the bubble and if they try to keep this circle of friends as close as possible. So, the motto of an effective virus bubble is: small and exclusive.

 

Author: László Á. Kóczy (Game Thory Research Group, KRTK)

 

EU provides extraordinary funds for the international COVID project led by SZTAKI

The 21-member EU project led by SZTAKI has the objective of making European production systems and supply chains more flexible, as well as facilitating the transfer of medical equipment if required due to another global pandemic or further waves of COVID-19.

SZTAKI, an ELKH member institution, won the CO-VERSATILE tender for the European Union Horizon 2020 framework program for the extraordinary challenge of combating and eradicating COVID. In May, the EU advertised the opportunities to submit tenders in five sub-categories, with a total of 23 projects receiving support from 454 bids submitted. Because of the urgency of the matter, everything was completed in a single month, from the initial instructions to the preparation and putting together of a consortium, while the results were also not announced according to the original schedule.

The CO-VERSATILE project aims to increase the adaptability and flexibility of the European manufacturing industry, with a particular focus on vital medical devices and protective equipment. The results have the potential to help Europe prepare for a global pandemic and respond to unexpected requirements.

The director of the project, Dr. Róbert Lovas, is SZTAKI’s deputy director. Under the coordination of SZTAKI, as part of the CO-VERSATILE research and development project and with the help of their partners, they have created a digital environment and processes that, in as little as 48 hours, can flexibly adjust and reallocate production capacities – which otherwise manufacture other products – if there is a need for an extremely large quantity of medical equipment or personal protective equipment. Another goal is to create a ‘Digital Technopole’ that not only serves urgent demands but where the developments and solutions applied there can also be transferred to other manufacturers all over Europe.

The EU has awarded EUR 5.4 million to the 24-month international project. The project is coordinated by SZTAKI – of the member states that joined the European Union after 2004 (EU13), Hungary was the only country to be awarded such a role in the fight against COVID-19. The 21-member consortium includes the German Fraunhofer IGD (Institute for Computer Graphics Research) and IML (Institute for Material Flow and Logistics) research centers, EIT Manufacturing, Leibniz Universität and the University of Westminster, as well as several European manufacturers and centers for digital innovation.

In addition to coordination, SZTAKI has also contributed to the development of the Digital Technopole with cloud-based solutions and the elaboration and adaptation of simulation models. This means that in the fight against COVID, the institute not only provides R&D background, IT expertise and resources for the Hungarian scientific community and virus research teams, but also for the European industrial sector.

Innomine is an important domestic member of the consortium. As a digital innovation hub, Innomine brings together various actors in the field, including suppliers, universities and research institutes, small and medium-sized enterprises and investors, to promote the further development and strengthening of the digital ecosystem. One of Innomine’s main tasks will be to promote and disseminate the resulting solutions on the market, including through the organisation of a Europe-wide networking event (matchaton).

Further information:
Media Contact:

Bálint Laza, laza.balint[at]sztaki.hu

Researchers from Szeged develop most sensitive method for detecting coronavirus to date

The RT-qPCR testing procedure developed at the Szeged Biological Research Center is extremely sensitive, cost-effective and suitable for mass testing.

The researchers at the Institute of Genetics of the ELKH Szeged Biological Research Center (SZBK) led by Péter Vilmos, have developed the most sensitive laboratory test to date for the detection of the SARS-CoV-2 virus from nasal secretions. The cooperating partners of the research and development project are the Institute of Medical Microbiology and Immunobiology of the University of Szeged, the No.1 Department of Infectious Diseases of the Internal Medicine Clinic, headed by Katalin Burián and Edit Hajdú, and the ELKH Alfréd Rényi Institute of Mathematics. The new method uses a known procedure. However, due to their innovative enhancements the method is 50 times faster than the RT-qPCR diagnostic procedure used today, enabling reliable mass screening, potentially covering the entire population. This could open up a whole new perspective in prevention, something that is particularly timely given the expected new wave of infections from Covid-19.

The most significant feature of this method is that it can test a pooled sample of 50 people at a time in a single reaction without compromising reliability. Another important feature of the method is that it can identify all infectious individuals, including asymptomatic patients, thus allowing effective control of the spread of the pandemic. In addition, the test can be performed with less expensive, domestically available compounds, which is a major advantage due to the worldwide shortage of more expensive reagents as a result of the pandemic. In addition, the new testing protocol could be used not only to detect the SARS-CoV-2 virus, but may also be easily adapted to new pandemics in the future and to monitoring the spread of any pathogen, which will make it easier to control any future pandemic.

The new method is based on the extremely high-sensitivity detection of the SARS-CoV-2 virus inheritance, which enables the detection of even as low number as five pathogens, even when a large number of nasal samples are mixed. This result was made possible by several modifications to the testing procedure currently in use worldwide. The new testing procedure combines the 50 nasal samples in two steps, which ultimately leads to only a ten-fold dilution for viruses, even from a single positive nasal sample. Simultaneous detection of multiple SARS-CoV-2 genes using the simplest method available and repetition of the reaction underlying the detection significantly increases the efficiency and specificity of the new method and greatly offsets the loss of sensitivity resulting from sample pooling.

The laboratory phase of development is complete, and the method is ready to be tried in mass testing. The procedure could certainly be of considerable international interest given that the US Food and Drug Administration (FDA) said in a statement issued on June 16 that it promised to accelerate the development and recognition of new sample pooling procedures to be developed for mass testing.

Researchers in Szeged have developed a completely new method for diagnosing Covid-19 infection

Based on artificial intelligence and automatic microscopy, the serological testing model developed at the Szeged Biological Research Centre is highly accurate, fast and cost-effective.

The Biomag Research Group of the Szeged Biological Research Centre (SZBK) at the Eötvös Loránd Research Network, led by Péter Horváth and working in collaboration with the Institute of Microbiology and Immunology of the University of Szeged, two research groups of the University of Helsinki and a spinoff company, Single-Cell Technologies, has developed an entirely new serological test for SARS-CoV2 – which has never been used anywhere, including Hungary.

The method, based on artificial intelligence and automated microscopy, identifies those who have already recovered with high accuracy and provides reliable feedback on the level of protection. It may also be suitable for the identification of newly infected people. The method has been validated for over 1,000 cases and accuracy has been measured at close to 100%. The method has very high throughput and is already suitable for 5-10,000 tests per day, so in the case of a second or further wave of infection, it could be successfully used for mass testing, i.e. to identify those who have already contracted the disease as well as newly infected individuals.

In addition to a turnaround time of 6 to 8 hours, repeatability and cost-effectiveness, another important feature of the method is high sensitivity, which allows the detection of infection even with mild immunity. The method does not show false positive results in healthy samples, and another great advantage is that it can be easily adapted to the proteins of any virus, so it can be quickly applied to epidemics caused by other viruses.

The theoretical basis of the model is the detection of antibodies – immunoglobulins – produced by the human body as these can already be detected in the blood a few days after infection and then for months afterwards. During the test, the blood sample is added to specially modified cells and then the cells are studied with a high-sensitivity, high-throughput automated microscope. Finally, the presence or absence of the antibody in each cell is determined by the artificial intelligence-based method.

The analysis of images captured with an automated microscope using artificial intelligence is one of the main profiles of Péter Horváth’s research group. For this, so-called “deep learning” algorithms are used, which belong to a new branch of artificial intelligence. A similar algorithm was also used in their new testing model developed for SARS-CoV2 to automatically and reliably evaluate images. Interestingly, these deep learning algorithms are also used for many functions of self-driving cars – e.g. zebra crossing pedestrian detection and overtaking – and for facial recognition in social media.

Researchers at ELKH Research Centre for Natural Sciences examine infection mechanism and spread of coronavirus to support the development of efficient pharmaceuticals and treatments

The Research Centre for Natural Sciences (TTK) is one of the leading multidisciplinary institutions in Hungary, where outstanding scientific expertise in chemistry, biology, medical and psychology sciences is complemented by state-of-the-art research infrastructure. Leveraging the combination of these four disciplines and in cooperation with their existing university and industry partners they have launched several coronavirus related projects in the field of prevention, treatment and diagnostics. In addition, TTK has been invited by the Operational Group and the Government of Hungary to lead one of the drug development consortium projects targeted at overcoming the COVID-19 virus infection.

The Medicinal Chemistry Research Group of TTK and dr. Krisztián Bányai virologist, senior researcher at the Institute of Veterinary Medical Research of the Centre for Agricultural Research also participate in the work of the Coronavirus Research Action Group established by the Prime Ministry. The Research Group was the first to identify the full genetic code of the coronavirus and it is currently examining its infection mechanism and spread. Their objective is to define potential prevention steps and to develop efficient pharmaceuticals and treatments in the long run.

Receptor discovered using artificial intelligence model developed by SZBK opens up new ways to protect against coronavirus

Péter Horváth, director of the Biomag Research Group of the Szeged Biological Research Centre (SZBK) at the Eötvös Loránd Research Network and Head of the Biomag Research Group, and his partners, Peter Cullen and Yohei Yamauchi, research professors at the University of Bristol, have shown that the SARS-CoV-2 coronavirus can enter the host cell through a hitherto unknown actor, the neuropilin-1 (NRP1) receptor on the host cell surface, which they discovered through their research on influenza.

Many research laboratories around the world are working to help develop effective treatment by understanding the process of coronavirus (COVID-19) infection. Researchers have so far been able to identify the angiotensin-converting enzyme 2 (ACE2), through which the virus is able to enter cells. Research based on SZBK’s artificial intelligence model suggests that NRP1, in addition to the already well-known ACE2, may be a new, second focal point for the treatment of COVID-19.

Neuropilin-1 (NRP1) is a receptor found on the surface of a host cell to which the SARS-CoV-2 virus is able to bind through a protein called S (Spike). From this S protein, the S1 protein is formed by enzymatic cleavage, which has a special pattern, the ‘C-end rule’ (CendR), at one end, the so-called C-terminal end. Through this region of the protein, the virus is able to bind to NRP1 and enter the cell. Infected cells, unlike healthy cells, have several nuclei.

To detect and quantify this difference, Péter Horváth and his team have developed a method that is unique in the world and based on a new trend in artificial intelligence, deep learning that enables researchers to perform very accurate microscopic analysis.

Previously, the Szeged research group used a similar methodology to screen for the NRP1 gene in influenza research. They gave this algorithm the name nucleAIzer (www.nucleaizer.org). Intelligent algorithms, such as those used to control self-driving cars or for intelligent analysis of images on social media, need huge training databases that the research team did not previously have access to. They therefore developed a hybrid method employing a deep learning method to generate artificial examples and train another intelligent method based on them. The method has just been published in the most prestigeous journal of systems biology, Cell Systems (https://www.sciencedirect.com/science/article/pii/S2405471220301174).

The accuracy of the algorithm is shown by the fact that it allowed the Biomag Research Group in Szeged to achieve the highest score in one of the largest global competitions.

SZTAKI and Wigner FK have offered their cloud computing capacity to support the fight against the pandemics

The Institute for Computer Sciences and Control (SZTAKI) and the Wigner Research Centre for Physics (Wigner FK) have offered thousands of processors and terabytes of their cloud computing capacity integrated with the MTA Cloud. In the first step SZTAKI has connected its machines to the Folding@Home international research project and community computing platform, where research with significant potential to contribute to the containment of the coronavirus pandemic is ongoing. The Wigner Data Centre has offered to provide reliable information technology support for virus research and genome sequencing efforts, based on their vast experience. A Call for research teams who need cloud capacity for their virus-related projects is open at both institutes.

Prevention, cure, diagnosis – The role of TTK in combating the virus

The Research Centre for Natural Sciences (TTK) is one of the leading multidisciplinary institutions in Hungary, where outstanding scientific expertise in chemistry, biology, medical and psychology sciences is complemented by state-of-the-art research infrastructure. Leveraging the combination of these four disciplines and in cooperation with their existing university and industry partners they have launched several coronavirus-related projects in the field of prevention, treatment and diagnostics. In addition, TTK has been invited by the Operational Group and the Government of Hungary to lead one of the drug development consortium projects targeted at overcoming the COVID-19 virus infection.

The Medicinal Chemistry Research Group of TTK and virologist Dr. Krisztián Bányai, senior researcher at the Institute of Veterinary Medical Research of the Centre for Agricultural Research also participate in the work of the Coronavirus Research Action Group established by the Prime Minister. The Research Group was the first to identify the complete genetic code of the coronavirus and it is currently examining its infection mechanism and spread. Their objective is to define potential prevention steps and to develop efficient pharmaceuticals and treatments in the long run.

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