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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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In vitro toxicity of carbon nanotubes: a systematic review

RCS Advances. 2022. Vol. 12. No. 25. P. 16235–16256 .
Margarita R. Chetyrkina, Fedorov F., Nasibulin A.

Carbon nanotube (CNT) toxicity-related issues provoke many debates in the scientific community. The controversial and disputable data about toxicity doses, proposed hazard effects, and human health concerns significantly restrict CNT applications in biomedical studies, laboratory practices, and industry, creating a barrier for mankind in the way of understanding how exactly the material behaves in contact with living systems. Raising the toxicity question again, many research groups conclude low toxicity of the material and its potential safeness at some doses for contact with biological systems. To get new momentum for researchers working on the intersection of the biological field and nanomaterials, i.e., CNT materials, we systematically reviewed existing studies with in vitro toxicological data to propose exact doses that yield toxic effects, summarize studied cell types for a more thorough comparison, the impact of incubation time, and applied toxicity tests. Using several criteria and different scientific databases, we identified and analyzed nearly 200 original publications forming a “golden core” of the field to propose safe doses of the material based on a statistical analysis of retrieved data. We also differentiated the impact of various forms of CNTs: on a substrate and in the form of dispersion because in both cases, some studies demonstrated good biocompatibility of CNTs. We revealed that CNTs located on a substrate had negligible impact, i.e., 90% of studies report good viability and cell behavior similar to control, therefore CNTs could be considered as a prospective conductive substrate for cell cultivation. In the case of dispersions, our analysis revealed mean values of dose/incubation time to be 4–5 μg mL−1 h−1, which suggested the material to be a suitable candidate for further studies to get a more in-depth understanding of its properties in biointerfaces and offer CNTs as a promising platform for fundamental studies in targeted drug delivery, chemotherapy, tissue engineering, biosensing fields, etc. We hope that the present systematic review will shed light on the current knowledge about CNT toxicity, indicate “dark” spots and offer possible directions for the subsequent studies based on the demonstrated here tabulated and statistical data of doses, cell models, toxicity tests, viability, etc.

Research target: Biology Nanotechnologies
Language: English
DOI
Keywords: carbon nanotubes
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Background:  Delay discounting refers to the tendency to choose sooner, smaller rewards over larger, later rewards. Many previous studies link this tendency positively to reward sensitivity, yet the specific mechanisms behind this association remain poorly understood. Reward sensitivity may relate to delay discounting through at least three possible pathways: increased sensitivity to reward size, increased sensitivity ...
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Added: February 10, 2026
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