The male factor is responsible for infertility in about 35–40% of all cases. Idiopathic oligo- and/or astheno- and/or therato-zoospermia is one of the most common male fertility disorders and remains a significant therapeutic challenge. The primary cause of idiopathic male infertility remains unknown but seems to be associated with oxidative stress. Objective: The use of antioxidative formulation to improve qualitative and quantitative deficiencies in the male gametes. In total, 78 subjects were treated with a combination of 1,725 mg L-carnitine fumarate, 500 mg acetyl-L-carnitine, 90 mg vitamin C, 20 mg coenzyme Q₁₀, 10 mg zinc, 200 µg folic acid, 50 µg selenium, and 1.5 µg vitamin B₁₂ (Proxeed^® Plus, Sigma-Tau, Italy) for 6 months; the preparation was taken twice daily from the time idiopathic infertility was diagnosed. Basic seminal parameters were evaluated by a European Society of Human Reproduction and Embryology (ESHRE) -certified embryologist following the fifth edition of the World Health Organisation (2010) guidelines at three time points: at baseline and 3 and 6 months of treatment. Improvements in semen parameters (differing in terms of dynamics) were evident at 3 months and gradually improved over the 6 months of treatment. Each parameter: sperm concentration, total sperm count, sperm total and progressive motility improved significantly after treatment except for the percentage of sperm of abnormal morphology and ejaculate volume. Proxeed Plus was effective for patients with idiopathic infertility; however, a long treatment period is needed to achieve optimal results.

The aim of this study was to find out if selected lactic acid bacteria (LAB) strains (antagonistic or nonantagonistic againstH. pylori in vitro) would differ in their abilities to modulate the DCs maturation profiles reflected by their phenotype and cytokine expression patterns.Methods. Monocyte-derived DCs maturation was elicited by their direct exposure to the LAB strains ofL. rhamnosus900 orL. paracasei915 (antagonistic and nonantagonistic toH. pylori, resp.), in the presence or absence ofH. pyloristraincagA+. The DCs maturation profile was assessed on the basis of surface markers expression and cytokines production.Results. We observed that the LAB strains and the mixtures of LAB withH. pyloriare able to induce mature DCs. At the same time, theL. paracasei915 leads to high IL-10/IL-12p70 cytokine ratio, in contrast toL. rhamnosus900.Conclusions. This study showed that the analyzed lactobacilli strains are more potent stimulators of DC maturation thanH. pylori. Interestingly from the two chosen LAB strains the antagonistic toH. pylori-L. rhamnosusstrain 900 has more proinflammatory and probably antibactericidal properties.

This article examines the growing role of machine learning (ML) in promoting next-generation climate change adaptation through the improved integration and performance of renewable energy systems. As climate change accelerates, innovative solutions are urgently needed to enhance the resilience and sustainability of energy infrastructure.ML offers powerful capabilities to handle complex data sets, forecast energy supply and demand, and optimize grid operations. This review highlights key applications of ML, such as predictive maintenance, intelligent grid management, and the real-time optimization of renewable energy resources. It also examines current challenges, including data availability, model transparency, and the need for interdisciplinary collaboration, both in technology development and policy and regulation. By synthesizing recent research and case studies, thisarticle shows how ML can significantly improve the performance, reliability, and scalability of renewable energy systems. This review emphasizes the importance of aligning technological advances with policy and infrastructure development. Successful implementation requires not only ensuring technological capabilities (robust infrastructure, structured data sets, and interdisciplinary collaboration) but also the careful consideration and alignment of ethical and regulatory factors from strategic to regional and local levels. Machine learning is becoming a key enabler for the transition to more adaptive, efficient, and low-carbon energy systems in response to climate change.

Introduction: optic flow is studied in many scientific areas such as automation, robotics, biomechanics and neuropsychology. People who are not visually impaired use this mechanism to move around, usually without realising its complexity. It consists of information provided by the sense of sight and analysed in the central nervous system about the speed and direction of a moving object and the direction and amplitude of the angular and linear components of one's body. However, little is known about how this mechanism develops in the young child. Objective: Observation of upper and lower limb movement in infants between 4 and 12 weeks. of age under the influence of optic flow stimulation as a stimulus. Development of an optimal test protocol and adaptation of the author's research station. Material and method: 16 infants aged from 4 to 12 weeks were initially invited to the study, of which 14 infants were finally included in the study group. The motor response test consisted of two stages. Stage I (static trial): the stimulus was a stationary black and white chessboard pattern; stage II (dynamic trial): the chessboard pattern moved toward the child at a speed of 0.17m/s. Cameras recorded the test in both stages. Results: the children showed increased limb movement under the influence of optical flow (test stage II: dynamic test) compared to stimulation with a fixed pattern (test stage I: static test). Conclusions: a response to dynamic versus static stimuli was observed in 11 of 14 children, but testing in one-month-olds is unreliable due to poor head control.

Introduction Eyesight is crucial for each child to achieve the stages of gross and fine motor development. One of the essential processes during movement is the effect of optical flow on the organ of vision and its influence on the performance of the movement. The phenomenon of optical flow is ubiquitous in everyday activities, and it is still not fully understood how it triggers a reaction. Aim The article is a presentation and summary of the research methodology of various research teams studying the influence of the optical flow on motor reactions in infants. Material and methods Review of publications searched using keywords (optical flow, motor reaction, reflexes, newborn, infant) in databases such as PubMed and ScienceDirect. The methods and conditions presented by various authors are summarized in one table. Results and conclusions Optical flow studies and the influence on motor reactions differ significantly in methodology and study groups, making it impossible to compare the results between all found publications. Most cited studies have shown that optical flow can induce or intensify a motor response in infants. Keywords: newborn, optical flow

Collagen implants in neurosurgery are widely used due to their biocompatibility, biodegradability, and ability to support tissue regeneration, but their mechanical properties, such as low tensile strength and susceptibility to enzymatic degradation, remain challenging. Current technologies are improving these implants through cross-linking, synthetic reinforcements, and advanced manufacturing techniques such as 3D bioprinting to improve durability and predictability. Industry 4.0 is contributing to this by automating production, using data analytics and machine learning to optimize implant properties and ensure quality control. In Industry 5.0, the focus is shifting to personalization, enabling the creation of patient-specific implants through human–machine collaboration and advanced biofabrication. eHealth integrates digital monitoring systems, enabling real-time tracking of implant healing and performance to inform personalized care. Despite progress, challenges such as cost, material property variability, and scalability for mass production remain. The future lies in smart biomaterials, AI-driven design, and precision biofabrication, which could mean the possibility of creating more effective, accessible, and patient-specific collagen implants. The aim of this article is to examine the current state and determine the prospects for the development of mechanical properties of collagen implant used in neurosurgery towards Industry 4.0/5.0, including ML model.