In this literature review, methods for the detection of breast cancer biomarkers and the operation of electrochemical sensors are considered. The work of sensors in the determination of breast cancer biomarkers was systematized, a comparative table with other methods was compiled, as was a classification of sensors depending on their intended use. The various traditional methods for the diagnosis of breast cancer biomarkers are described, including mammography, ultrasound, magnetic resonance imaging, positron emission computed tomography, computed tomography, single-photon emission computed tomography, and biopsy, and their advantages and disadvantages are presented. Key sensor parameters for the detection of breast cancer biomarkers are compared, such as the detection limit, linear detection range, response time, sensitivity, and other characteristics depending on the analyte being analyzed. Based on the reviewed scientific papers, the significance of electrochemical sensors in detecting the biomarkers of breast cancer is demonstrated. The types of tumor biomarkers identified by biosensors were analyzed, with a particular focus on HER2. Studies on HER2 detection using electrochemical methods are compared and systematized, and the features of electrochemical biosensors for determining this biomarker are characterized. Possible interfering agents affecting the accuracy of HER2 determination under experimental conditions are considered, their mechanisms of action are analyzed, and ways to eliminate them are proposed. This report provides a summary of the current aspects of scientific research on electrochemical sensors for the detection of breast cancer biomarkers. The development of electrochemical biosensors opens up new prospects for the early diagnosis and prognosis of breast cancer treatment.

Cardiovascular disease is a major contributor to morbidity, mortality and quality of life in people aged 60 and over. However, this age group of patients was not sufficiently included in large studies, in this regard, the management of elderly patients is an urgent problem in modern medicine. The purpose of this review is to analyze of literature data on the characteristics comorbid pathology and senile asthenia as a predictor of unfavorable course and outcomes of acute myocardial infarction in patients 60 years of age and older. To analyze the literature, we searched for information on this issue in PubMed / MEDLINE, PMC, Web of Science, Scopus, The Сocrane Library. Recent research data show that there are plenty of risk factors for worsening the course of AMI in patients aged 60 and over. The most important of them are considered to be high comorbidity, multivessel lesion of several pools, the presence of senile asthenia. All together lead to difficulties in diagnosing myocardial infarction, to forced polypharmacy and in some cases refraining from early invasive strategy, which worsens the course of the disease, increases the duration of hospital treatment, leads to frequent complications, thereby affects the prognosis of the disease and leads to the development of early disability in patients 60 years of age and older In the vast majority of elderly patients, the development of acute myocardial infarction occurs against the background of a high prevalence of comorbid pathology, which in turn accelerates the development of senile asthenia and its progression.

Introduction: The presence of high comorbidity, risk factors lead to a worsening of the course and an unfavorable outcome of acute myocardial infarction (AMI). Taking into account gender and age characteristics, the study of the most important risk factors will help in the future create activities focused on combating comorbidity. The main benefit of this work is the identification and management of high-risk patients. The aim of the study: study of risk factors, structure and severity of comorbid pathology and its influence on the clinical course of AMI in different gender-age groups of patients. Materials and methods: The retrospective study included case histories of 634 patients with a favorable outcome of AMI. The study group is represented by 63.9% of men and 36.1% of women. The comorbidity of AMI patients was assessed using the Charlson comorbidity index (IC). The patients were divided into 3 groups: Group I (low comorbidity, ICR≤3 points); Group II (average comorbidity, CI 3-5 points); Group III (high comorbidity, CI ≥5 points). Results and discussion: Compared to women, men were more likely to have AH 89.7%, hypercholesterolemia 75%, overweight 40%, smoking 31.5%, obesity 31%, diabetes mellitus 24.4% and impaired glucose tolerance in 20% of patients. The average number of nosologies was 3.7 ± 1.04. The Charlson comorbidity index is 5.5 ± 1.9 points. There was a high 47.3% and moderate comorbidity of 48.3%. A high comorbidity index was found in men (49.7%), in women (50.3%). Patients with high comorbidity often had recurrent AMI (59.1%, p = 0.001). A direct correlation was found between the severity of coronary artery disease, age, and the severity index of comorbidity (r = 0.32, p = 0.001). Compared to the three women, the vascular lesion of the coronary bed was more detected in men 64.1%, p = 0.001. Considering the structure and severity of comorbid pathology in patients with AMI, there was a low 10-year survival rate according to the comorbidity index. Conclusion: Most of the patients with risk factors and comorbid pathology were men. The patients with AMI studied by us had high and moderate comorbidity; more often these patients had repeated AMI, multivessel coronary artery disease and a decrease in the frequency of coronary angiography, then PCI. When calculating the comorbidity index, a low 10-year survival rate was revealed. This work proves the need for timely correction of risk factors, improvement of comorbidity management tactics, which helps to reduce the unfavorable course of AMI. Введение: Наличие высокой коморбидности факторов риска ведут к ухудшению течения и неблагоприятному исходу острого инфаркта миокарда (ОИМ). Учитывая гендерные и возрастные особенности, изучение наиболее важных факторов риска помогут в дальнейшем создавать мероприятия, ориентированные на борьбу с коморбидностью. Основным благоприятным результатом в этой работе является выявления пациентов повышенного риска и работа с ними. Цели исследования: изучение факторов риска, структуры и степени тяжести коморбидной патологии и ее влияние на клиническое течение ОИМ у различных гендерно-возрастных групп пациентов. Материалы и методы: В ретроспективное исследование были включены истории болезни 634 пациентов с благоприятным исходом ОИМ. Исследуемая группа представлена: 63,9% мужчин и 36,1% женщин. Коморбидность больных ОИМ оценивали с помощью индекса коморбидности (ИК) Чарлсона. Больные были разделены на 3 группы: I группа (низкая коморбидность, ИКЧ≤3 балла); II группа (средняя коморбидность, ИКЧ 3-5 баллов); III группа (высокая коморбидность, ИКЧ ≥5 балла). Результаты: По сравнению с женщинами у мужчин чаще встречались АГ - 89,7%, гиперхолестеринемия - 75%, избыточная масса тела - 40%, курение - 31,5%, ожирение - 31%, СД - 24,4% и нарушение толерантности к глюкозе у 20% пациентов. Среднее количество нозологий составило 3,7±1,04. Индекс коморбидности Чарлсона 5,5±1,9 балла. Отмечалась высокая у 47,3% и средней степени тяжести коморбидность у 48,3%. Высокий индекс коморбидности встречался у мужчин (49,7%), у женщин (50,3%). У больных с высокой коморбидностью часто встречались повторный ОИМ (59,1%, р=0,001). Выявлена прямая корреляционная связь между степенью тяжести поражения коронарных артерий, возрастом и индексом тяжести коморбидности (r=0,32, p=0,001). По сравнению с женщинами трех сосудистое поражение коронарного русла больше выявлялось у мужчин 64,1%, р=0,001. Учитывая структуру и степень тяжести коморбидной...

In recent years, there has been increasing interest in the frailty as a prognostic factor of acute myocardial infarction in elderly patients. Frailty is an important prognostic marker of frequent complications, readmission to hospital, high hospital mortality and major cardiovascular events in elderly patients with acute myocardial infarction. This category of persons is often not allowed to undergo invasive interventions and are often excluded from the recommended treatment, and they tolerate cardiac surgery worse, recovery from illness is slower, functionality decreases, then disability and death develop. The present review aims to investigate the impact of frailty on management of elderly patients with acute myocardial infarction (AMI). To analyze the literature, we searched for information on this issue in PubMed / MEDLINE, PMC, Web of Science, Scopus, The Сocrane Library. The search depth was 15 years: from 2006 to 2021. One of the important factors in improving clinical outcomes, improving the quality of life in elderly patients with acute myocardial infarction is the early detection of frailty. Frailty assessment is a valuable tool for risk stratification that can be helpful to clinicians in deciding the optimal pathway for management and treatment strategies. Risk prediction is also important for deciding secondary prevention and cardiac rehabilitation measures in the elderly with acute myocardial infarction.

This paper examines various machine learning methods for assessing risk factors for cardiovascular diseases. To build predictive models, two approaches were used: the extreme gradient boosting (XGBoost) algorithm and a convolutional neural network (CNN). The focus is on analyzing the performance of each model in classification and regression tasks, as well as their ability to identify key biomarkers and risk factors such as cholesterol, ferritin, homocysteine and aspartate aminotransferase (AST) levels. XGBoost parameters have been optimized for working with tabular data, demonstrating high accuracy in risk prediction. The CNN model, despite the initial reduction in error on the training set, showed signs of overfitting when analyzing validation data. Performance evaluation using the metrics of mean squared error (MSE), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC) revealed significant differences between the models. The study results confirm the effectiveness of XGBoost in analyzing tabular data and summarizing risk factor knowledge, while the CNN model needs further optimization to handle sparse data. The work demonstrates the importance of choosing the right model architecture and training parameters to ensure reliable diagnosis of cardiovascular diseases.