Determination of markers of systemic inflammation is one of the important directions in the study of pathogenesis and improvement of diagnosis of chronic obstructive pulmonary disease (COPD), asthma-COPD overlap (ACO), and bronchial asthma (BA). The aim of our work was a comparative study of the features of changes in serum levels of IL-17, IL-18, and TNF-αin patients with COPD, ACO, and BA with various severity of the disease, as well as evaluation of the relationship between the level of these cytokines and lung ventilation function. A total of 147 patients with COPD (n=58), ACO (n=57), and BA (n=32) during a stable period have been examined in this study. The control group included 21 healthy nonsmokers with similar sex-age indicators. Serum levels of IL-17, IL-18, and TNF-αwere determined by ELISA. The concentrations of these cytokines in the circulation in the studied patients with COPD, ACO, and BA were higher than those in healthy nonsmokers (p≤0.001). IL-17 and IL-18 levels in the blood serum were comparable in all examined patients. The mean TNF-αconcentrations in the circulation in COPD and ACO were significantly higher than those in BA (p<0.001). In patients with COPD, the levels of IL-17 and TNF-αincreased progressively against the background of a decrease in numerous spirometric indicators, which allows us to consider these cytokines as systemic biomarkers of disease severity. In BA, the inverse correlations between the level of IL-17 and FEV1/FVC (%) and FEV1 have been found. In patients with ACO, the increase in IL-18 levels was associated with a decrease in FEV1 and TNF-αwith FEV1/FVC (%). These findings indicate that IL-17, IL-18, and TNF-αcan participate in the mechanisms of systemic inflammation and the genesis of disorders of airway obstruction in COPD, AСO, and BA. An increase in the levels of IL-17 and TNF-αmay be associated with impaired bronchial patency in COPD and BA. The established associations of the IL-18 concentration in the blood serum and FEV1 only in patients with ACO allow using the level of IL-18 as a potential marker of the degree of impaired airway obstruction in this disease.

In this paper we measure the relationship between messages in the social media and the stock market prices. First, we measure the correlation and association between the amount of stock related tweets and different financial indicators such as prices, returns and transaction volume. Then, we analyze the content of the messages and test whether the tweets generated during different trends of price change (up, down or steady) can be distinguished by automatic classifiers. Our corpus consist on messages related to nine IT companies and also their daily prices and volume during trading hours for over a period of three months. Two textual representations were used, bag of words and word embeddings. The tweets were automatically tagged using two thresholds to bin the changes in price. We have found a correlation between the amount of daily messages and the volume of financial transactions. We also found negative association (more specifically, what we define as local trend association) between tweet volume and financial indicators that were not found by using only the correlation analysis. Our main contribution is that the messages generated during a positive, negative and neutral trend can be distinguished by state of the art classifiers.

Similarity, correlation and association measures play an important role in statistics, information retrieval, data mining and data science, classification and machine learning, recommender systems and decision-making. They have numerous applications in ecology, social and behavioral sciences, biology and bioinformatics, social network and time series analysis, image and natural language processing. Often the measures with the same name introduced on different domains have different properties, and the measures with the same properties have different names. To unify analysis of measures defined on different domains, this paper considers these measures as functions defined on universal domain and satisfying some sets of properties. The general properties of similarity functions (SF) and dissimilarity functions (DF) under the joint name of resemblance functions (RF) studied on universal domain and illustrated by examples on specific domains. The known and the new methods of construction of similarity measures are considered. This paper discusses the following aspects of RF: relationship with fuzzy (valued) relations, T-transitivity and triangle inequality, Minkowski distance and data transformation, cosine SF, RF on domains with involution (negation), aggregation and transformations of RF, visualization of RF. The paper considers also the lattice of RF, composition and min-transitive transformations of SF (fuzzy proximity relations), applications to hierarchical clustering and non-probabilistic entropy of RF. In addition, the paper proposes the method of construction of correlation functions (association measures) using SF. Pearson correlation and Yule’s Q association coefficients obtained as particular cases of the general method. One can use the paper as a survey of works on similarity and dissimilarity measures on specific domains, as a guide for constructing new similarity and correlation measures, as a base for the study of mathematical properties of resemblance functions on universal and specific domains, and also as a part of the course on Data Science.

Respiratory diseases are accompanied by intensification of free radical processes at different levels of the biological body organization. Simultaneous stress and suppression of various parts of antioxidant protection lead to the development of oxidative stress (OS) and nitrosative stress (NS). The basic mechanisms of initiation and development of the OS and NS in pulmonary pathology are considered. The antioxidant defense system of the respiratory tract is characterized. The results of the NS and OS marker study in various respiratory diseases are presented. It is shown that NS and OS are multilevel complex-regulated processes, existing and developing in inseparable connection with a number of physiological and pathophysiological processes. The study of NS and OS mechanisms contributes to the improvement of the quality of diagnosis and the development of therapeutic agents that act on different pathogenetic stages of the disease.

Background: The opioid crisis poses a significant health challenge in the United States, with increasing overdoses and death rates due to opioids mixed with other illicit substances. Various strategies have been developed by federal and local governments and health organizations to address this crisis. One of the most significant objectives is to understand the epidemic through better health surveillance, and machine learning techniques can support this by identifying opioid users at risk of overdose through the analysis of social media data, as many individuals may avoid direct testing but still share their experiences online. Objective: In this study, we take advantage of recent developments in machine learning that allow for insights into patterns of opioid use and potential risk factors in a less invasive manner using self-reported information available on social platforms. Methods: This study used YouTube comments posted between December 2020 and March 2024, in which individuals shared their self-reported experiences of opioid drugs mixed with other substances. We manually annotated our dataset into multiclass categories, capturing both the positive effects of opioid use, such as pain relief, euphoria, and relaxation, and negative experiences, including nausea, sadness, and respiratory depression, to provide a comprehensive understanding of the multifaceted impact of opioids. By analyzing this sentiment, we used 4 state-of-the-art machine learning models, 2 deep learning models, 3 transformer models, and 1 large language model (GPT-3.5 Turbo) to predict overdose risks to improve health care response and intervention strategies. Results: Our proposed methodology (GPT-3.5 Turbo) was highly precise and accurate, helping to automatically identify sentiment based on the adverse effects of opioid drug combinations and high-risk drug use in YouTube comments. Our proposed methodology demonstrated the highest achievable F ₁ -score of 0.95 and a 3.26% performance improvement over traditional machine learning models such as extreme gradient boosting, which demonstrated an F ₁ -score of 0.92. Conclusions: This study demonstrates the potential of leveraging machine learning and large language models, such as GPT-3.5 Turbo, to analyze public sentiment surrounding opioid use and its associated risks. By using YouTube comments as a rich source of self-reported data, the study provides valuable insights into both the positive and negative effects of opioids, particularly when mixed with other substances. The proposed methodology significantly outperformed traditional models, contributing to more accurate predictions of overdose risks and enhancing health care responses to the opioid crisis.

This review describes the unique links of the functioning of the nitric oxide cycle in the respiratory tract in normal and pathological conditions. The concept of a nitric oxide cycle has been expanded to include the NO-synthase and NO-synthase-independent component of its synthesis and the accompanying redox cascades in varying degrees of reversible reactions. The role of non-NO-synthase cycle components has been shown. Detailed characteristics of substrates for the synthesis of nitric oxide (NO) in the human body, which can be nitrogen oxides, nitrite and nitrate anions, and organic nitrates, as well as nitrates and nitrites of food products, are given. The importance of the human microbiota in the nitric oxide cycle has been shown. The role of significant components of nitrite and nitrate reductase systems in the nitric oxide cycle and the mechanisms of their activation and deactivation (participation of enzymes, cofactors, homeostatic indicators, etc.) under various conditions have been determined. Consideration of these factors allows for a detailed understanding of the mechanisms underlying pathological conditions of the respiratory system and the targeting of therapeutic agents. The complexity of the NO cycle with multidirectional cascades could be best understood using dynamic modeling.