Using machine learning to study the population life quality: methodological aspects

Assessment of the population life quality is an important and relevant sociological task. Machine learning as a classification tool of social network users’ digital traces makes it possible to create a base to calculate subjective life quality index. The article consistently reviews all stages of the machine learning algorithms application to assess the life quality of the population of the regions of the Russian Federation and the issues of improving neural network accuracy. To train the neural network the authors formed a set of marked-up data extracted from regional communities of the social network “VKontakte”. Various approaches to text vectorisation, publicly available neural network models pre-trained on large Russian-language text corpora, as well as metrics for evaluating the algorithms results were analysed. Computational experiments with different algorithms were carried out, according to the results of which the Rubert-tiny algorithm was selected due to its high learning and classification rate. During the model parameters adjustment, the accuracy of f1-macro 0.545 was achieved. Computational experiments were carried out using Python scripts.Typical errors that a neural network makes in the process of automatic content classification were considered. The results of the study can be used to calculate the online activity index in the VKontakte social network of users from various Russian regions, on the basis of which the subjective life quality index will be calculated in the future. Improving the neural network accuracy will make it possible to obtain more reliable data for assessing the life quality in Russian regions based on users’ digital traces

Особенности клинического течения COVID-19 у людей, живущих с ВИЧ

Purpose. To study the features of the clinical course of coronavirus infection (COVID-19) in people living with HIV and risk factors for adverse outcomes.Materials and methods. The study included 523 patients with a confirmed diagnosis of COVID-19 occurring against the background of HIV infection and hospitalized from March 2020 to September 2021 on the basis of the GBUZ “S.P. Botkin KIB” in St. Petersburg. Two groups were formed: 1 – receiving antiretroviral therapy (n=204), 2 – not receiving ART (n=319). A comparative analysis of the results obtained during the examination was carried out using statistical methods: Mann-Whitney (p≤0.05) and the calculation of the relative risk (RR) when comparing the probability of the outcome of the disease depending on the presence of risk factors: respiratory rate ( NPV),% lung damage, levels of CD4 and C-reactive protein (CRP) with a significance level of p≤0.05.Results. Among the patients, persons aged 30 to 49 years predominated. In 50.5% of cases, coronavirus infection proceeded in the form of acute respiratory viral infections, pneumonia was diagnosed in 49.5%, which was subsequently complicated in 22.9% by the development of acute respiratory distress syndrome or sepsis in 2.1%. Severe course of COVID-19 was observed in non-adherent to ART, with CD4 lymphocyte count (≤50 cells/µl), multimorbidity and amounted to 45%.Conclusion. A feature of the course of COVID-19 in patients with HIV/SARS-COV-2 coinfection was a high number of deaths – 21.6%. In the overall structure of causes of death, the maximum share fell on HIV infection – 58.4%, COVID-19 – 24.8%, HIV/ COVID-19 –9.7% coinfection and other causes – 7.1%. Factors associated with the development of severe forms of coronavirus infection caused by SARS-COV-2 in HIV-infected patients who were hospitalized, the combination of which can be used as a predictor of death, have been identified: respiratory rate (RR) > 20 per minute, percentage of involvement lungs> 50%, CD4 lymphocyte level <40 cells/µl, CRP>50 mg/l, presence of three or more concomitant diseases.Цель: изучить особенности клинического течения коронавирусной инфекции (COVID-19) у людей, живущих с ВИЧ, и факторы риска неблагоприятных исходов.Материалы и методы. В исследование включены 523 пациента с подтвержденным диагнозом COVID-19, протекающим на фоне ВИЧ-инфекции и госпитализированных в период с марта 2020 г. по сентябрь 2021 г. в Клиническую инфекционную больницу им. С.П. Боткина в Санкт-Петербурге. Сформировано 2 группы: 1 – получавшие антиретровирусную терапию (n=204), 2 – не получавшие ее (n=319). Сравнительный анализ результатов, полученных в ходе обследования, проведен с использованием статистических методов: Манна – Уитни (р≤0,05) и расчета относительного риска при сравнении вероятности исхода заболевания в зависимости от наличия факторов риска: частота дыхательных движений, процент поражения легких, уровни CD4 и С-реактивного белка с уровнем значимости р≤0,05.Результаты. Среди пациентов преобладали лица в возрасте от 30 до 49 лет. В 50,5% случаев коронавирусная инфекция протекала в форме острой респираторной вирусной инфекции, пневмония диагностирована в 49,5%, которая в последующем в 22,9% осложнялась развитием острого респираторного дистресс-синдрома или сепсисом в 2,1%. Тяжелое течение COVID-19 наблюдалось у не приверженных к антиретровирусной терапии, с уровнем CD4 лимфоцитов (≤50 кл/мкл), мультиморбидностью и составило 45%.Заключение. Особенностью течения COVID-19 у пациентов с коинфекцией ВИЧ/SARS-COV-2 явилось высокое число летальных исходов – 21,6%. В общей структуре причин смерти максимальная доля пришлась на ВИЧ-инфекцию – 58,4%, COVID-19–24,8%, ко-инфекцию ВИЧ/COVID-19 – 9,7% и другие причины – 7,1%. Выявлены факторы, ассоциированные с развитием тяжелых форм коронавирусной инфекции, вызванной SARS-COV-2, у ВИЧ-инфицированных, находившихся на стационарном лечении, сочетание которых может быть использовано в качестве предиктора летального исхода: частота дыхательных движений >20 в минуту, процент поражения легких> 50%, уровень CD4 лимфоцитов <40 кл/мкл, С-реактивный белок >50 мг/л, наличие 3 и более сопутствующих заболеваний

Different modulated structures of topological defects stabilized by adaptive targeting nanoparticles

It is demonstrated that interactions between nanoparticles and topological defects induce a twist-grain boundary phase in a chiral liquid crystal. The occurrence of this phase, the analogue of the Shubnikov phase in type-II superconductors, is driven by direct interactions between surface-functionalized CdSe quantum dots and screw dislocations. It is shown that, within an adaptive-defect-core-targeting mechanism, nanoparticles of appropriate size and functionalization adapt to qualitatively different cores of topological defects such as disclination lines and screw dislocations. This mechanism enables the effective reduction of the energetically costly, singular defect core volume, while the surrounding phase ordering remains relatively weakly affected. The findings suggest new pathways towards the controlled assembly of superstructures in diverse, symmetry-broken, condensed-matter systems, ranging from nanoparticle-decorated liquid crystals to superconductors. © The Royal Society of Chemistry 2013