Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

Increasingly available open medical and health datasets encourage data-driven research with a promise of improving patient care through knowledge discovery and algorithm development. Among efficient approaches to such high-dimensional problems are a number of machine learning methods, which are applied in this paper to pressure ulcer prediction in modular critical care data. An inherent property of many health-related datasets is a high number of irregularly sampled time-variant and scarcely populated features, often exceeding the number of observations. Although machine learning methods are known to work well under such circumstances, many choices regarding model and data processing exist. In particular, this paper address both theoretical and practical aspects related to the application of six classification models to pressure ulcers, while utilizing one of the largest available Medical Information Mart for Intensive Care (MIMIC-IV) databases. Random forest, with an accuracy of 96%, is the best-performing approach among the considered machine learning algorithms.

Polyunsaturated fatty acids (PUFA) play an important role in reparative processes. The ratio of PUFAs n-3 to n-6 may affect wound healing. The study aimed to evaluate the effect of dietary supplementation with n-3 and n-6 PUFA in two proportions on skin wounds in laboratory rats. Adult male Wistar rats received 20% fat emulsion with a ratio of 1.4:1 (group A) or 4.3:1 (group B) for n-3:n-6 PUFAs at a daily dose of 1 mL/kg. The control group received water under the same conditions. The animals were supplemented a week before and a week after the skin excision performed on the back. The level of wound closure, various parameters of oxidative stress, and plasma fatty acids composition were evaluated. Wound tissue samples were examined by electron microscopy. The administration of fat emulsions led to significant changes in plasma polyunsaturated fatty acid composition. The increased production of reactive nitrogen species, as well as more numerous newly formed blood vessels and a greater amount of highly organized collagen fibrils in both groups A and B may indicate more intensive healing of the skin wound in rats supplemented with polyunsaturated fatty acids in high n-3:n-6 ratio.

More than four decades passed since sigma receptors were first mentioned. Since then, existence of at least two receptor subtypes and their tissue distributions have been proposed. Nowadays, it is clear, that sigma receptors are unique ubiquitous proteins with pluripotent function, which can interact with so many different classes of proteins. As the endoplasmic resident proteins, they work as molecular chaperones – accompany various proteins during their folding, ensure trafficking of the maturated proteins between cellular organelles and regulate their functions. In the heart, sigma receptor type 1 is more dominant. Cardiac sigma 1 receptors regulate response to endoplasmic reticulum stress, modulates calcium signaling in cardiomyocyte and can affect function of voltage-gated ion channels. They contributed in pathophysiology of cardiac hypertrophy, heart failure and many other cardiovascular disorders. Therefore, sigma receptors are potential novel targets for specific treatment of cardiovascular diseases.

According to the World Health Organization, cardiovascular diseases are the main cause of death worldwide. They may be caused by various factors or combinations of factors. Frequently, endothelial dysfunction is involved in either development of the disorder or results from it. On the other hand, the endothelium may be disordered for other reasons, e.g., due to infection, such as COVID-19. The understanding of the role and significance of the endothelium in the body has changed significantly over time—from a simple physical barrier to a complex system encompassing local and systemic regulation of numerous processes in the body. Endothelium disorders may arise from impairment of one or more signaling pathways affecting dilator or constrictor activity, including nitric oxide–cyclic guanosine monophosphate activation, prostacyclin–cyclic adenosine monophosphate activation, phosphodiesterase inhibition, and potassium channel activation or intracellular calcium level inhibition. In this review, plants are summarized as sources of biologically active substances affecting the endothelium. This paper compares individual substances and mechanisms that are known to affect the endothelium, and which subsequently may cause the development of cardiovascular disorders.