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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Effects of Angiotensin-I-Converting Enzyme (ACE) Mutations Associated with Alzheimer’s Disease on Blood ACE Phenotype

Biomedicines. 2024. Vol. 12. No. 10. Article 2410.
Kryukova O., Islanov I., Zaklyazminskaya E., Korostin D., Belova V., Cheranev V., Repinskaia Z., Tonevitskaya S., Petukhov P., Dudek S., Kost O., Rebrikov D., Danilov S.

Backgrounds. Our recent analysis of 1200+ existing missense ACE mutations revealed that 400+ mutations are damaging and led us to hypothesize that carriers of heterozygous loss-of-function (LoF) ACE mutations (which result in low ACE levels) could be at risk for the development of late- onset Alzheimer’s disease (AD). Methods. Here, we quantified blood ACE levels in EDTA plasma from 41 subjects with 10 different heterozygous ACE mutations, as well as 33 controls, and estimated the effect of these mutations on ACE phenotype using a set of mAbs to ACE and two ACE substrates.
Results. We found that relatively frequent (~1%) AD-associated ACE mutations in the N domain of ACE, Y215C, and G325R are truly damaging and likely transport-deficient, with the ACE levels in plasma at only ~50% of controls. Another AD-associated ACE mutation, R1250Q, in the cytoplasmic tail, did not cause a decrease in ACE and likely did not affect surface ACE expression. We have also developed a method to identify patients with anti-catalytic mutations in the N domain. These mutations may result in reduced degradation of amyloid beta peptide Aβ42, an important component for amyloid deposition. Consequently, these could pose a risk factor for the development of AD. Conclusions. Therefore, a systematic analysis of blood ACE levels in patients with all ACE mutations has the potential to identify individuals at an increased risk of late-onset AD. These individuals may benefit from future preventive or therapeutic interventions involving a combination of chemical and pharmacological chaperones, as well as proteasome inhibitors, aiming to enhance ACE protein traffic. This approach has been previously demonstrated in our cell model of the transport-deficient ACE mutation Q1069R.

Research target: Biology Medical Technologies Medical and Health Sciences
Language: English
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Keywords: mutationsAlzheimer’s Diseasescreeningangiotensin-I-converting enzymeconformational changesblood ACE
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