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Immune System Error: How Antibodies in Multiple Sclerosis Mistake Their Targets

Immune System Error: How Antibodies in Multiple Sclerosis Mistake Their Targets
Researchers at HSE University and the Institute of Bioorganic Chemistry of the Russian Academy of Sciences (IBCh RAS) have studied how the immune system functions in multiple sclerosis (MS), a disease in which the body's own antibodies attack its nerve fibres. By comparing blood samples from MS patients and healthy individuals, scientists have discovered that the immune system in MS patients can mistake viral proteins for those of nerve cells. Several key proteins have also been identified that could serve as new biomarkers for the disease and aid in its diagnosis. The study has been published in  Frontiers in Immunology. The research was conducted with support from the Russian Science Foundation.

‘Our Result Was Recognised Not Only Within the Project Defence but Also on International Scale’

‘Our Result Was Recognised Not Only Within the Project Defence but Also on International Scale’
This year, the European AI Conference (ECAI 2025) accepted an article titled ‘Multi-Agent Path Finding for Large Agents is Intractable’  by Artem Agafonov, a second-year student of the Applied Mathematics and Information Science Bachelor’s programme at HSE University’s Faculty of Computer Science. The work was co-authored by Konstantin Yakovlev, Head of the Joint Department with Intelligent Technologies of System Analysis and Management at the Federal Research Centre ‘Informatics and Management’ of the RAS and Associate Professor at the Faculty of Applied Sciences. In the interview, Artem Agafonov explained how he came up with the idea for the article and how he was able to present it at an A-level conference.

HSE Neurolinguists Reveal What Makes Apps Effective for Aphasia Rehabilitation

HSE Neurolinguists Reveal What Makes Apps Effective for Aphasia Rehabilitation
Scientists at the HSE Centre for Language and Brain have identified key factors that increase the effectiveness of mobile and computer-based applications for aphasia rehabilitation. These key factors include automated feedback, a variety of tasks within the application, extended treatment duration, and ongoing interaction between the user and the clinician. The article has been published in NeuroRehabilitation.

Mathematicians from HSE Campus in Nizhny Novgorod Prove Existence of Robust Chaos in Complex Systems

Mathematicians from HSE Campus in Nizhny Novgorod Prove Existence of Robust Chaos in Complex Systems
Researchers from the International Laboratory of Dynamical Systems and Applications at the HSE Campus in Nizhny Novgorod have developed a theory that enables a mathematical proof of robust chaotic dynamics in networks of interacting elements. This research opens up new possibilities for exploring complex dynamical processes in neuroscience, biology, medicine, chemistry, optics, and other fields. The study findings have been accepted for publication in Physical Review Letters, a leading international journal. The findings are available on arXiv.org.

Mathematicians from HSE University–Nizhny Novgorod Solve 57-Year-Old Problem

Mathematicians from HSE University–Nizhny Novgorod Solve 57-Year-Old Problem
In 1968, American mathematician Paul Chernoff proposed a theorem that allows for the approximate calculation of operator semigroups, complex but useful mathematical constructions that describe how the states of multiparticle systems change over time. The method is based on a sequence of approximations—steps which make the result increasingly accurate. But until now it was unclear how quickly these steps lead to the result and what exactly influences this speed. This problem has been fully solved for the first time by mathematicians Oleg Galkin and Ivan Remizov from the Nizhny Novgorod campus of HSE University. Their work paves the way for more reliable calculations in various fields of science. The results were published in the Israel Journal of Mathematics (Q1).

AI to Enable Accurate Modelling of Data Storage System Performance

AI to Enable Accurate Modelling of Data Storage System Performance
Researchers at the HSE Faculty of Computer Science have developed a new approach to modelling data storage systems based on generative machine learning models. This approach makes it possible to accurately predict the key performance characteristics of such systems under various conditions. Results have been published in the IEEE Access journal.

Children with Autism Process Sounds Differently

Children with Autism Process Sounds Differently
For the first time, an international team of researchers—including scientists from the HSE Centre for Language and Brain—combined magnetoencephalography and morphometric analysis in a single experiment to study children with Autism Spectrum Disorder (ASD). The study found that children with autism have more difficulty filtering and processing sounds, particularly in the brain region typically responsible for language comprehension. The study has been published in Cerebral Cortex.

Cerium Glows Yellow: Chemists Discover How to Control Luminescence of Rare Earth Elements

Cerium Glows Yellow: Chemists Discover How to Control Luminescence of Rare Earth Elements
Researchers at HSE University and the Institute of Petrochemical Synthesis of the Russian Academy of Sciences have discovered a way to control both the colour and brightness of the glow emitted by rare earth elements. Their luminescence is generally predictable—for example, cerium typically emits light in the ultraviolet range. However, the scientists have demonstrated that this can be altered. They created a chemical environment in which a cerium ion began to emit a yellow glow. The findings could contribute to the development of new light sources, displays, and lasers. The study has been published in Optical Materials.

Genetic Prediction of Cancer Recurrence: Scientists Verify Reliability of Computer Models

Genetic Prediction of Cancer Recurrence: Scientists Verify Reliability of Computer Models
In biomedical research, machine learning algorithms are often used to analyse data—for instance, to predict cancer recurrence. However, it is not always clear whether these algorithms are detecting meaningful patterns or merely fitting random noise in the data. Scientists from HSE University, IBCh RAS, and Moscow State University have developed a test that makes it possible to determine this distinction. It could become an important tool for verifying the reliability of algorithms in medicine and biology. The study has been published on arXiv.

Russian Scientists Reconstruct Dynamics of Brain Neuron Model Using Neural Network

Russian Scientists Reconstruct Dynamics of Brain Neuron Model Using Neural Network
Researchers from HSE University in Nizhny Novgorod have shown that a neural network can reconstruct the dynamics of a brain neuron model using just a single set of measurements, such as recordings of its electrical activity. The developed neural network was trained to reconstruct the system's full dynamics and predict its behaviour under changing conditions. This method enables the investigation of complex biological processes, even when not all necessary measurements are available. The study has been published in Chaos, Solitons & Fractals.