This article describes the manufacturing technology of biocompatible flexible strain-sensitive sensor based on Ecoflex silicone and multi-walled carbon nanotubes (MWCNT). The sensor demonstrates resistive behavior. Structural, electrical, and mechanical characteristics are compared. It is shown that laser radiation significantly reduces the resistance of the material. Through laser radiation, electrically conductive networks of MWCNT are formed in a silicone matrix. The developed sensor demonstrates highly sensitive characteristics: gauge factor at 100% elongation −4.9, gauge factor at 90° bending −0.9%/deg, stretchability up to 725%, tensile strength 0.7 MPa, modulus of elasticity at 100% 46 kPa, and the temperature coefficient of resistance in the range of 30–40 °C is −2 × 10⁻³. There is a linear sensor response (with 1 ms response time) with a low hysteresis of ≤3%. An electronic unit for reading and processing sensor signals based on the ATXMEGA8E5-AU microcontroller has been developed. The unit was set to operate the sensor in the range of electrical resistance 5–150 kOhm. The Bluetooth module made it possible to transfer the received data to a personal computer. Currently, in the field of wearable technologies and health monitoring, a vital need is the development of flexible sensors attached to the human body to track various indicators. By integrating the sensor with the joints of the human hand, effective movement sensing has been demonstrated.

Numerous physiological processes in the human skin are mediated by nitric oxide, a gaseous signalling molecule. Almost every type of skin cell may produce nitric oxide, it is possible to generate nitric oxide without the need of enzymes. Nitric oxide plays a crucial role in regulating apoptosis, keratinocyte differentiation and proliferation, the protective properties of the epidermal barrier, and the structure and functions of the microcirculatory bed. Nitric oxide is involved in immunological and inflammatory responses, hair growth regulation, and wound healing processes. It mediates ultraviolet-induced processes such as erythema and edema development and participates in melanogenesis. Furthermore, the ability of nitric oxide to bind reactive oxygen species and prevent lipid peroxidation gives it antioxidant qualities. This coordinated action of nitric oxide on gene expression and membrane integrity effectively protects cells from ultraviolet A-induced apoptosis and necrosis. Furthermore, nitric oxide can be considered as a molecule that inhibits the development of cancer and photoaging. It directly harms microorganisms and indirectly activates the immune system, exhibiting antibacterial, antiviral, and antifungal qualities. Notably, nitric oxide is effective against antibiotics-resistant bacteria. All of the aforementioned findings suggest that nitric oxide is a gaseous mediator that can protect skin function.

The development of many human disorders, including celiac disease (CD), is thought to be influenced by the microbiota of the gastrointestinal tract and its metabolites, according to current research. This study’s goal was to provide a concise summary of the information on the contribution of the intestinal microbiota to the CD pathogenesis, which was actively addressed while examining the reported pathogenesis of celiac disease (CD). We assumed that a change in gluten tolerance is formed under the influence of a number of different factors, including genetic predisposition and environmental factors. In related investigations, researchers have paid increasing attention to the study of disturbances in the composition of the intestinal microbiota and its functional activity in CD. A key finding of our review is that the intestinal microbiota has gluten-degrading properties, which, in turn, may have a protective effect on the development of CD. The intestinal microbiota contributes to maintaining the integrity of the intestinal barrier, preventing the formation of a “leaky” intestine. On the contrary, a change in the composition of the microbiota can act as a significant link in the pathogenesis of gluten intolerance and exacerbate the course of the disease. The possibility of modulating the composition of the microbiota by prescribing probiotic preparations is being considered. The effectiveness of the use of probiotics containing Lactobacillus and Bifidobacterium bacteria in experimental and clinical studies as a preventive and therapeutic agent has been documented.

Nitric oxide (NO) is a gaseous molecule which plays a key role in wound healing. Previously, we identified the optimal conditions for wound healing strategies using NO donors and an air plasma generator. The aim of this study was to compare the wound healing effects of binuclear dinitrosyl iron complexes with glutathione (B-DNIC-GSH) and NO-containing gas flow (NO-CGF) at their optimal NO doses (0.04 mmol for B-DNIC-GSH and 1.0 mmol for NO-CGF per 1 cm²) in a rat full-thickness wound model over a 3-week period. Excised wound tissues were studied by light and transmission electron microscopy and immunohistochemical, morphometrical and statistical methods. Both treatments had an identical stimulating impact on wound healing, which indicated a higher dosage effectiveness of B-DNIC-GSH compared to the NO-CGF. B-DNIC-GSH spray application reduced inflammation and promoted fibroblast proliferation, angiogenesis and the growth of granulation tissue during the first 4 days after injury. However, prolonged NO spray effects were mild compared to NO-CGF. Future studies should determine the optimal B-DNIC-GSH solution course for a more effective wound healing stimulation.

Nitric oxide can activate neutrophils and macrophages, facilitate the synthesis of collagen, which allows significantly accelerating the regeneration of traumatized tissues. We studied the effects of nitric oxide-containing gas flow generated by plasma-chemical device “Plason” in a rat model of full-thickness wounds. Histological and morphometric analyses revealed that Plason treated wounds expressed significantly fewer signs of inflammation and contained a more mature granulation tissue on day 4 after the operation. Considering the results of the experimental study, we applied the Plason device in sports medicine for the treatment of lower limb bruises of 34 professional soccer players. Athletes were asked to assess the intensity of pain with the Visual Analogue Scale. Girths of their lower limbs were measured over the course of rehabilitation. Nitric oxide therapy of full-thickness wounds inhibited inflammation and accelerated the regeneration of skin and muscle tissues. Compared with the control, we observed a significant reduction in pain syndrome on days 2–7 after injuries, edema, and hematoma, and shortened treatment duration. This pilot study indicates that the use of nitric oxide is a promising treatment method for sports injuries.

Despite the growing interest in physiological stress monitoring, an objective measure of stress is currently lacking, especially in clinical and rehabilitation contexts. With the emerging integration of artificial intelligence (AI) in data analytics, heart rate variability (HRV) has gained attention as an effective biomarker; however, the literature remains fragmented across disciplines, stress types, and methodological approaches. This scoping review aims to investigate how AI techniques are applied to HRV analysis for stress detection and prediction in adult populations. Although this review does not focus on a specific subtype of stress, its primary objective is to explore the current methodological state of the art as reported in the literature, without restrictions on stress typology. Following PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Scopus, and Google Scholar for studies published between 2005 and 2025, using MeSH terms including “HRV”, “Rehabilitation”, “SCI” (for Spinal Cord Injury), “Stress”, “Sympathetic”, “Parasympathetic”, “Non-linear”, “Gamification”, “AI” and “Machine Learning”. Inclusion criteria targeted adult human populations and studies employing HRV features as input for AI and machine learning techniques for psychophysical stress assessment. Of the 566 records identified, 15 studies met the eligibility criteria. The reviewed studies exhibit substantial heterogeneity in terms of settings, populations, sensors, and algorithms with most employing supervised methods (e.g., random forest, support vector machine), alongside several applications of deep learning and explainable AI. Only one study focused specifically on physiological stress, none focused on SCI populations, and rehabilitation-related research was scarce, thereby underscoring important gaps in the current literature. Overall, HR variability analysis, especially when combined with artificial intelligence techniques, represents a promising approach for stress assessment; however, the field is methodologically fragmented and clinically underdeveloped in critical areas, underscoring the need for a multidisciplinary methodological framework.