Publications by authors named "A V Galatenko"

Within-host infection dynamics of Omicron dramatically differs from previous variants of SARS-CoV-2. However, little is still known about which parameters of virus-cell interplay contribute to the observed attenuated replication and pathogenicity of Omicron. Mathematical models, often expressed as systems of differential equations, are frequently employed to study the infection dynamics of various viruses.

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Mathematical modeling is widely used to study within-host viral dynamics. However, to the best of our knowledge, for the case of SARS-CoV-2 such analyses were mainly conducted with the use of viral load data and for the wild type (WT) variant of the virus. In addition, only few studies analyzed models for data, which are less noisy and more reproducible.

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The T-cell immune response is a major determinant of effective SARS-CoV-2 clearance. Here, using the recently developed T-CoV bioinformatics pipeline (https://t-cov.hse.

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Feature selection is one of the main techniques used to prevent overfitting in machine learning applications. The most straightforward approach for feature selection is an exhaustive search: one can go over all possible feature combinations and pick up the model with the highest accuracy. This method together with its optimizations were actively used in biomedical research, however, publicly available implementation is missing.

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In this study we analyzed expression of CD24 in a cohort of colorectal cancer patients using immunohistochemistry staining of CD24. We found a significant association between absence or low expression of CD24 (10% of membranous and 55% of cytoplasmic staining) and shortened patient survival. Protein localization played a crucial role in the prognosis: membranous form was the major and prognostic one in primary tumors, while cytoplasmic expression was elevated in liver metastases compared to the primary tumors and contained prognostic information.

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