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News
September 15, 2026
Immunity to Chaos: How Personal Resources Help Us Cope with the Challenges of a Turbulent World
International conflicts, crises and digital overload—the modern world puts our minds to the test every day. Traditional psychology often focuses on the consequences: anxiety, depression, and psychosomatic disorders. But what if we looked at the problem differently—through the lens of the resources that prevent us from breaking down? Psychological immunity is precisely this set of resources. Alena Zolotareva and her group, Psychological Immunity as a Resource for Positive Functioning, are developing an integrative model of this phenomenon, adapting diagnostic tools and preparing for large-scale empirical research. Why do psychologists need to collaborate with medical professionals, and how could their research transform preventive care in clinics and corporations?
September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.

 

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External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction

International Journal of Cardiology. 2021. No. 329. P. 130–135.
Attia, I. Z., Tseng, A. S., Benavente, E. D., Medina-Inojosa, J. R., Clark, T. G., Malyutina, S.., Leon D. A., Lopez-Jimenez, F.

Objective: To validate a novel artificial-intelligence electrocardiogram algorithm(AI-ECG) to detect left ventricular
systolic dysfunction (LVSD) in an external population.
Background: LVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived
AI-ECG to detect LVSD using ECGs based on a large sample of patients treated at the Mayo Clinic.
Methods: We performed an external validation study with subjects from the Know Your Heart Study, a crosssectional
study of adults aged 35–69 years residing in two cities in Russia, who had undergone both ECG and
transthoracic echocardiography. LVSD was defined as left ventricular ejection fraction ≤ 35%. We assessed the
performance of the AI-ECG to identify LVSD in this distinct patient population.
Results: Among 4277 subjects in this external population-based validation study, 0.6% had LVSD (compared to
7.8% of the original clinical derivation study). The overall performance of the AI-ECG to detect LVSD was robust
with an area under the receiver operating curve of 0.82.When using the LVSD probability cut-off of 0.256 from
the original derivation study, the sensitivity, specificity, and accuracy in this population were 26.9%, 97.4%,
97.0%, respectively. Other probability cut-offs were analysed for different sensitivity values.
Conclusions: The AI-ECG detected LVSDwith robust test performance in a population thatwas very different from
that used to develop the algorithm. Population-specific cut-offs may be necessary for clinical implementation.
Differences in population characteristics, ECG and echocardiographic data quality may affect test performance.

Research target: Health Studies Sociology (including Demography and Anthropology
Priority areas: sociology
Language: English
DOI
Text on another site
Keywords: искусственный интеллектмашинное обучениеэлектрокардиограмма (ЭКГ)Artificial intelligence Machine learning Electrocardiogram Left ventricular systolic dysfunctionСистолическая дисфункция левого желудочка
Publication based on the results of:
Demographic consequences of COVID-19 epidemic in Russia (2021)
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