Sci, Volume 8, Issue 1 (January 2026) – 24 articles

This study analyzes the observed patterns of Generative Artificial Intelligence (Generative AI) use by students in higher education through the lens of the sociotechnical systems (STS) theory, focusing on the academic subsystem. The empirical basis is a survey of 2083 students (3686 responses) from seven countries in Central and Eastern Europe, Central Asia, and Central Africa. Based on these data, two proxy indicators are proposed: A₁, reflecting the overall academic use of Generative AI and A₂, characterizing the use of Generative AI in a research context. The results show that Generative AI is widely incorporated into students’ academic activities (A₁ = 79.06%), while research-oriented use remains less common (A₂ = 46.66%) and varies significantly across subsamples. A joint analysis of A₁ and A₂, visualized as a zoned space A₁–A₂, reveals different configurations of academic practices: from a predominance of routine educational use to a more pronounced focus on research tasks. Cross-country comparisons show that in certain contexts (e.g., Kazakhstan and one of the Ukrainian subsamples), Generative AI is more often used in a research context, while in other cases, its use remains predominantly educational and routine. In this sense, the results indicate that Generative AI is beginning to fulfill the role of an emerging student research assistant in students’ academic life: technology has already become a familiar tool for completing educational tasks, but its use in supporting research activities remains fragmented. The proposed model and proxy indicators allow us to describe and compare current configurations of Generative AI use in the academic subsystem. The obtained results provide a basis for further research aimed at a deeper understanding of the factors determining the inclusion of Generative AI in student research practice, as well as for the development of management approaches regarding its use in higher education. Full article

This study aimed to examine the association between physical activity and individuals’ health status, healthcare utilization, socio-demographic characteristics, and health behaviors in a large representative sample from Northern Greece. A cross-sectional study was conducted involving 1227 participants (47.4% males, mean age 49.94 ± 14.87 years) from Thrace, Greece, selected through a two-stage stratified sampling method. According to the Greek version of IPAQ, participants were classified as inactive/insufficiently active, sufficiently and highly active. Data on socio-demographic, lifestyle, and health-related variables were collected through structured interviews. Multivariate logistic regression analysis was performed to determine the independent effect of physical activity on subjects’ characteristics using SPSS ver. 19. Half of the participants (49.8%) were inactive/insufficiently active, 418 participants (34.1%) were sufficiently active, and 198 participants (16.1%) were highly active. In univariate analysis, smoking (p < 0.001), higher coffee consumption (p = 0.002), higher adherence to Mediterranean diet (p < 0.001), napping during the day (p = 0.017) and short sleep duration (p < 0.001) were associated with lower prevalence of high activity. In adjusted analyses, sufficiently active participants had a lower risk for bad self-rated health (aOR = 0.63), hypertension (aOR = 0.41), dyslipidemia (aOR = 0.42), diabetes (aOR = 0.53), obesity (aOR = 0.61), cardiovascular diseases (aOR = 0.43), anxiety (aOR = 0.65), depression (aOR = 0.56), daily sleepiness (aOR = 0.62), poor sleep quality (aOR = 0.71), as well as for primary (aOR = 0.54) and secondary (aOR = 0.40) healthcare utilization compared to inactive participants. Higher-intensity physical activity did not enhance these beneficial effects of sufficient activity on subjects’ characteristics. Physical inactivity significantly compromises health across multiple domains. Promoting even moderate-intensity physical activity may reduce chronic disease burden and healthcare utilization. Full article

This study examines the functional degradation of crystalline silicon photovoltaic cells after 17 years of field exposure in the Adrar Desert, Algeria. Harsh thermal, radiative, and mechanical conditions accelerate aging, affecting electrical performance and structural stability. Monocrystalline silicon cells were extracted and analyzed by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Raman spectroscopy, electrical resistivity measurements, and vibrating sample magnetometry (VSM). SEM revealed microcracks, delamination, and corrosion products. EDS showed Ag, Si, O, and C signals, while Raman indicated silicon features and signatures consistent with encapsulant (EVA) degradation. The temperature-dependent resistivity displayed a dual behavior with a minimum near ~72 °C, above which resistivity increased, consistent with a transition in the dominant transport mechanisms. VSM measurements showed an overall diamagnetic response with a weak hysteresis loop suggestive of defect-related contributions. The observed aging is primarily associated with oxidation, metal migration, and encapsulant degradation. These findings motivate more robust materials and interfaces for desert climates, alongside improved thermal management and active monitoring. Full article

Background: CrossFit^® is a high-intensity functional training modality with increasing popularity, yet limited evidence describes the general profile of its practitioners. Objective: To characterize CrossFit^® athletes based on their training habits, injury prevalence, and nutritional supplement use, with specific consideration given to sex and age. Methods: An online questionnaire was completed by 358 practitioners (182 women; mean age 35.6 ± 9.1 years) from various Spanish regions. Descriptive and comparative analyses (χ² and ANOVA; p < 0.05) were conducted for training patterns, injury history, and supplement consumption. Results: Over half of the sample had practiced CrossFit^® for more than three years, typically training 3–4 days per week in one-hour sessions. Participants primarily reported social and health-related motivations and identified as non-competitive. Overall, 42.2% experienced at least one CrossFit^®-related injury, most frequently affecting the shoulder (15.6%) and lumbar spine (10.1%), largely attributed to repetitive overload. Supplement use was widespread (81.8%), with creatine (60.3%) and protein (49.4%) being the most commonly consumed. Conclusions: CrossFit^® practitioners train consistently, value the social environment, and show an injury pattern similar to that of other strength-based disciplines. Supplement consumption is highly prevalent across groups. Coaches and health professionals should prioritize injury-prevention strategies, promote safe load progression, and guide responsible supplement use. Full article

This study aims to enhance the security of unmanned aerial vehicles (UAVs) within the Internet of Drones (IoD) ecosystem by detecting and preventing Denial-of-Service (DoS) attacks. We introduce DroneDefender, a web-based intrusion detection system (IDS) that employs machine learning (ML) techniques to identify anomalous network traffic patterns associated with DoS attacks. The system is evaluated using the CIC-IDS 2018 dataset and utilizes the Random Forest algorithm, optimized with the SMOTEENN technique to tackle dataset imbalance. Our results demonstrate that DroneDefender significantly outperforms traditional IDS solutions, achieving an impressive detection accuracy of 99.93%. Key improvements include reduced latency, enhanced scalability, and a user-friendly graphical interface for network administrators. The innovative aspect of this research lies in the development of an ML-driven, web-based IDS specifically designed for IoD environments. This system provides a reliable, adaptable, and highly accurate method for safeguarding drone operations against evolving cyber threats, thereby bolstering the security and resilience of UAV applications in critical sectors such as emergency services, delivery, and surveillance. Full article

In recent years, sialidases (neuraminidases) derived from non-clinical sources have attracted considerable interest due to their potential applications in the food and pharmaceutical industries. A deeper understanding of the mechanisms regulating sialidase synthesis could lead to more efficient enzyme production. Induction is considered a key regulatory mechanism. However, there is a lack of data on the regulation of sialidase synthesis in filamentous fungi. This study examines how regulatory mechanisms influence the production of a sialidase enzyme exhibiting high activity at low temperatures in the Antarctic fungal strain Penicillium griseofulvum P29. The inclusion of high- and low-molecular-weight substances possessing terminal non-reducing N-acetylneuramyl groups in the tests led to a marked enhancement of sialidase activity. The strongest induction response was elicited by sialic acid, followed by glycomacropeptide, milk whey, N-acetylglucosamine, N-acetylmannosamine, and colominic acid. RT-qPCR experiments demonstrated that induction occurs at the transcriptional level of the sialidase gene. Biochemical analysis elucidates the function of inducers as triggers in the de novo synthesis of the enzyme protein. To our knowledge, this is the first study to highlight the importance of regulatory mechanism induction in the synthesis of cold-active sialidases. Full article

This study evaluated the feasibility of using recycled plastics (PP, HDPE, and PET) for sustainable construction applications. Materials were collected, processed, and extruded following a structured methodology, and their physico-mechanical and environmental properties were assessed through standardized tests, including compression, flexural strength, water absorption, porosity, and apparent density. Compression tests showed that increasing the processing temperature led to a reduction in the compressive strength of polypropylene (PP), while high-density polyethylene (HDPE) achieved its highest strength at the lowest temperature. Polyethylene terephthalate (PET) exhibited a similar decreasing trend with temperature. The processing speed, expressed as revolutions per minute (rpm), had little influence on PP and HDPE performance but positively affected PET, where higher rpm consistently improved compressive strength. Flexural tests revealed that higher rpm values enhanced the mechanical performance of PP and HDPE. However, for PP, an increase in processing temperature resulted in a pronounced decline in flexural strength. Overall, PP and HDPE outperformed PET, reaching compressive strengths near 10 MPa compared to values below 4 MPa for PET. In flexural tests, PP achieved 44 MPa, followed by HDPE with 25 MPa. Water absorption remained below 1% for all materials. The study is limited to physico-mechanical characterization and does not include microstructural or thermal analyses to assess crystallinity, degradation, or molecular orientation. Future research will focus on advanced thermal–chemical characterization and process optimization—particularly for PET—to improve ductility and expand the applicability of recycled plastics in construction. Full article

Ellipsometric mapping measurements and Bayesian evaluation were performed with a non-collimated, imaging ellipsometer using an LCD monitor as a light source. In such a configuration, the polarization state of the illumination and the local angle of incidence vary spatially and spectrally, rendering conventional spectroscopic ellipsometry inversion methods hardly applicable. To address these limitations, a multilayer optical forward model is augmented with instrument-specific correction parameters describing the polarization state of the monitor and the angle-of-incidence map. These parameters are determined through a Bayesian calibration procedure using well-characterized Si-SiO₂ reference wafers. The resulting posterior distribution is explored by global optimization based on simulated annealing, yielding a maximum a posteriori estimate, followed by marginalization to quantify uncertainties and parameter correlations. The calibrated correction parameters are subsequently incorporated as informative priors in the Bayesian analysis of unknown samples, including polycrystalline–silicon layers deposited on Si-SiO₂ substrates and additional Si-SiO₂ wafers outside the calibration set. The approach allows consistent propagation of calibration uncertainties into the inferred layer parameters and provides credible intervals and correlation information that cannot be obtained from conventional least-squares methods. The results demonstrate that, despite the broadband nature of the RGB measurement and the limited number of analyzer orientations, reliable layer thicknesses can be obtained with quantified uncertainties for a wide range of technologically relevant samples. The proposed Bayesian framework enables a transparent interpretation of the measurement accuracy and limitations, providing a robust basis for large-area ellipsometric mapping of multilayer structures. Full article

Introduction: The sustained global epidemic of Human Immunodeficiency Virus (HIV) necessitates comprehensive, region-specific surveillance to inform public health policy. This 30-year retrospective observational cohort study delineated the epidemiological patterns, transmission dynamics, treatment efficacy, and long-term clinical outcomes of HIV infection in Oman to strategically align preventative and therapeutic programs with Oman’s Vision 2040 framework. Methods: We analyzed the clinical and epidemiological data of 429 confirmed HIV-positive patients with a minimum follow-up period of six months, registered at a secondary care facility in North Batinah, Oman, between January 1995 and December 2024. Predictors of mortality were rigorously assessed utilizing Kaplan–Meier survival analysis and Cox proportional hazards regression models. Continuous variables were evaluated using independent sample t-tests or Mann–Whitney U tests, while categorical variables employed chi-square or Fisher’s exact tests. Results: The cohort exhibited a male predominance (70.6%) with a mean age at diagnosis of 32.8 years (SD ± 12.17). Heterosexual contact constituted the predominant mode of acquisition (56%), followed by bisexual (17%) and homosexual (12%) contacts. Although 67.1% of patients presented with early, asymptomatic disease (WHO Stage 1), opportunistic infections were evident in 28.1% of the cohort, with recurrent sepsis (8.4%) and bacterial pneumonia (3.5%) being the most frequent complications. The WHO clinical stage at presentation was confirmed as a highly significant predictor of survival (p < 0.0001). Stage 1 patients achieved excellent long-term prognosis (approximately 75% survival beyond 30 years), markedly contrasting with Stage 4 patients, whose survival declined sharply (median survival of approximately 8 years, and only 10–15% surviving past 20 years). The tenofovir/emtricitabine/efavirenz regimen showed superior efficacy, achieving 75% survival at 30 years, relative to zidovudine-based regimens, which showed significantly poorer performance (15–20% survival at 20 years). Conclusions: This investigation substantiates the shift toward predominant heterosexual transmission and emphasizes the critical prognostic significance of the clinical stage at diagnosis. Optimal long-term survival mandates prompt diagnosis, timely initiation of contemporary antiretroviral therapies, and sustained viral suppression. These findings offer crucial evidence to strengthen HIV prevention and treatment programs within Oman. Full article

The present paper concerns a new formulation of the optimal design problem of I-shaped multistep steel beam elements, based on the study of the plastic strain spread occurring in the relevant elements, with the aim of determining the length involved by the plastic deformation related to assigned load conditions and different constrained beam schemes. Material behavior is assumed as elastic–perfectly plastic, and the hypothesis of plane cross-sections is accepted. The functions defining the plastic strain spread are analytically obtained in the framework of Euler–Bernoulli beam theory. The proposed optimal design problem is a minimum volume one and the new constraint imposed on the length of the plasticized portion ensures that the minimum volume beam element also represents a maximum plastic dissipation one. Furthermore, the solution to the optimal design problem guarantees that the obtained multistep beam element ensures protection against brittle failure of the beam end sections, provides optimal cross-sections of the different portions belonging to Class 1 and ensures a suitable minimum value of the elastic flexural stiffness to respect the constraint on the deflection. Explicit reference is made to the so-called Reduced Beam Section (RBS), which characterizes the described multistep beam elements. Actually, the proposed formulation represents an innovative approach to obtaining an optimal beam element that really satisfies all the resistance, stiffness and ductility behavioral requirements. Some numerical applications conclude the paper, and their results are confirmed by appropriate FEM analyses in ABAQUS environment. Full article

Assisted Reproductive Technology Units (ARTUs) constitute a rapidly growing sector in healthcare, where service quality and patient safety are closely intertwined with ethical principles, technological precision, and managerial efficiency. This study aims to explore quality management practices and certification standards—such as ISO 9001, ISO 15189, and ISO 13485—within ARTUs, with the goal of developing a model that enhances patient-centered outcomes. The analysis focuses on the roles of leadership, staff training, and internal auditing mechanisms as key factors for the successful implementation of quality management systems (QMSs). Through a structured literature review and thematic synthesis, this study identifies challenges that ARTUs face in aligning with international standards and highlights strategies that strengthen patient trust, transparency, and continuous improvement. The proposed model connects measurable quality indicators with patient perceptions and experiences, providing a comprehensive framework for sustainable quality development. This article contributes to the academic discourse on healthcare quality governance and offers practical insights for policymakers and administrators seeking to improve patient experience and organizational resilience in reproductive medicine. Full article

IoT-based blockchain technology has improved the healthcare system to ensure the privacy and security of healthcare data. A Blockchain Bridge (BB) is a tool that enables multiple blockchain networks to communicate with each other. The existing approach combining the classical and quantum blockchain models failed to secure the data transmission during cross-chain communication. Thus, this study proposes a new BB verification for secure healthcare data transfer. Additionally, a brain tumor analysis framework is developed based on segmentation and neural networks. After the patient’s registration on the blockchain network, Brain Magnetic Resonance Imaging (MRI) data is encrypted using Hash-Keyed Quantum Cryptography and verified using a Peer-to-Peer Exchange model. The Brain MRI is preprocessed for brain tumor detection using the Fuzzy HaMan C-Means (FHMCM) segmentation technique. The features are extracted from the segmented image and classified using the LeCun Kaiming-based Convolutional ModSwish Neural Network (LCK-CMSNN) classifier. Subsequently, the brain tumor diagnosis report is securely transferred to the patient via a smart contract. The proposed model verified BB with a Verification Time (VT) of 12,541 ms, secured the input with a Security level (SL) of 98.23%, and classified the brain tumor with 99.15% accuracy, thus showing better performance than the existing models. Full article

The winemaking industry represents one of the most important sectors of the Mediterranean agrifood economy, generating large amounts of solid residues, especially grape pomace. The study aimed to evaluate during two consecutive harvest years the influence of the production system (conventional vs. organic) and cultivar on the mineral, chemical, and antioxidant composition, as well as the colorimetric properties, of grape pomaces obtained from four Vitis vinifera L. cultivars in Alentejo-Portugal. The results showed that mineral composition was significantly affected by both production system and cultivar, with organic grape pomace showing higher K and Mn contents, whereas Ca and Cu showed consistently higher content under conventional. Protein content tended to increase under organic production, while fiber and fat were overall higher in conventional, particularly in the first year. Sugars displayed strong cultivar specificity, with Arinto showing the highest concentrations (30 to 40%), and considering all cultivars, total phenolic content ranged between 4000 ando 9000 mg GAE/100 g, while antioxidant capacity varied among cultivars and years. Colorimetric parameters were essentially influenced by cultivar and harvest year rather than production system. The PCA revealed that PC1 (44.06%) represented a gradient associated with mineral and antioxidant composition, while PC2 (21.26%) reflected minor variation in color and sugars, and the hierarchical clustering distinguished Syrah and Alicante Bouschet as the cultivars most responsive to production system, whereas Aragonez and Arinto exhibited greater compositional stability across years. Overall, the findings indicate that both cultivar and management practices (organic and conventional) influence the compositional profile of grape pomace, with organic showing a tendency to enhance K, Mn, protein, and antioxidant parameters, whereas conventional practices favored higher levels of Ca, Cu, and fiber. The results provide valuable insights for the valorization of grape pomace and the development of sustainable viticultural strategies in Mediterranean environments. Full article

Background: Hermetia illucens larvae provide a sustainable bioconversion pathway that transforms agro-industrial residues into protein- and nutrient-dense biomass and frass, suitable for animal feed and soil amendment, respectively. Nevertheless, the potential spread of antibiotic resistance (AR) genes and pathogenic microorganisms poses biosafety concerns. This study examined the impact of four residue-based diet formulations; peas and chickpea (D1), peas and wheat (D2), onion and wheat (D3), and wheat with digestate (D4), on microbial safety during the bioconversion process. Methods: Enterococcus spp. (viable counts), Salmonella spp. (presence/absence), and 13 AR genes associated with resistance to tetracyclines, macrolide-lincosamide-streptogramin B, β-lactams, vancomycin, and aminoglycosides were quantified in single substrates, diets, larvae, and frass using qPCR. Results: Principal component analysis revealed diet-driven AR gene profiles. D1 lowered the levels of the greatest number of tested AR genes, particularly erm(B), tetracycline, and β-lactam genes in frass, as well as tet(O) and vanB in mature larvae. In contrast, D2 increased the AR gene levels in frass. All diets except D4 eliminated Salmonella spp. Enterococcus spp. loads varied by diet and larval stage, with D2 reducing counts in frass. Conclusions: Diet composition directly shapes microbial dynamics and AR gene dissemination, indicating that legume-based substrates may enhance biosafety in bioconversion systems. Full article

Liver fibrosis is a common pathological result of chronic hepatic injury caused by various factors, such as viral hepatitis, alcohol-induced liver disease, and metabolic dysfunction-associated steatohepatitis (MASH). It is characterized by an excessive deposition of extracellular matrix, which disrupts the architecture of the liver and can lead to cirrhosis, liver failure, and hepatocellular carcinoma. Globally, nearly 10% of the population has significant fibrosis, with its prevalence increasing with age, obesity, and metabolic syndrome. Despite its significant clinical impact, early detection of liver fibrosis is still limited due to insufficient diagnostic technologies and low public awareness. The increasing burden of MASH emphasizes the urgent need for scalable therapeutic strategies. Currently, liver transplantation is the only definitive treatment, but it is limited by donor shortages and the need for lifelong immunosuppression. However, fibrosis is now recognized as a dynamic and potentially reversible process if the underlying cause is addressed. This shift in understanding has prompted efforts to develop pharmacological agents that target hepatic stellate cell activation, immune system interactions, and metabolic dysfunction. Advances in organoid platforms, multi-omics, and non-invasive diagnostics are accelerating translational research in this area. This review aims to synthesize current knowledge about the molecular drivers of fibrosis, bottlenecks in the current anti-fibrotic drug discovery process, and emerging therapeutic approaches to inform precision medicine strategies and reduce the global burden of chronic liver disease. Full article

This paper presents an intelligent operational strategy that performs the coordinated dispatch of active and reactive power from PV distributed generators (PV DGs) and Distributed Static Compensators (D-STATCOMs) to support secure and economical operation of active distribution networks. The problem is formulated as a nonlinear optimization problem that explicitly represents the P and Q control capabilities of Distributed Energy Resources (DER), encompassing small-scale generation and compensation units connected at the distribution level, such as PV generators and D-STATCOM devices, adjusting their reference power setpoints to minimize daily operating costs, including energy purchasing and DER maintenance, while satisfying device power limits and the voltage and current constraints of the grid. To solve this problem efficiently, a parallel version of the Population Continuous Genetic Algorithm (CGA) is implemented, enabling simultaneous evaluation of candidate solutions and significantly reducing computational time. The strategy is assessed on the 33- and 69-node benchmark systems under deterministic and uncertainty scenarios derived from real demand and solar-generation profiles from a Colombian region. In all cases, the proposed approach achieved the lowest operating cost, outperforming state-of-the-art metaheuristics such as Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA), while maintaining power limits, voltages and line currents within secure ranges, exhibiting excellent repeatability with standard deviations close to 0.0090%, and reducing execution time by more than 68% compared with its sequential counterpart. The main contributions of this work are: a unified optimization model for joint P–Q control in PV and D–STATCOM units, a robust codification mechanism that ensures stable convergence under variability, and a parallel evolutionary framework that delivers optimal, repeatable, and computationally efficient energy management in distribution networks subject to realistic operating uncertainty. Full article

The integration of artificial intelligence into the Industrial Internet of Things (IIoT), supported by edge computing architectures, marks a new paradigm of intelligent automation. Tiny Machine Learning (TinyML) is emerging as a key technology that enables the deployment of machine learning models on ultra-low-power devices. This study presents a systematic review of 110 peer-reviewed publications (2020–2025) identified from Scopus, Web of Science, and IEEE Xplore following the PRISMA protocol. Bibliometric and thematic analyses were conducted using Biblioshiny and VOSviewer to identify major trends, architectural approaches, and industrial applications of TinyML. The results reveal four principal research clusters: edge intelligence and energy efficiency, federated and explainable learning, human-centric systems, and sustainable resource management. Importantly, the surveyed industrial implementations report measurable gains—typically reducing inference latency to the millisecond range, lowering on-device energy cost to the sub-milliwatt regime, and sustaining high task accuracy, thereby substantiating the practical feasibility of TinyML in real IIoT settings. The analysis indicates a conceptual shift from engineering- and energy-focused studies toward cognitive, ethical, and security-oriented perspectives aligned with the principles of Industry 5.0. TinyML is positioned as a catalyst for the transition from automation to cognitive autonomy and as a technological foundation for building energy-efficient, ethical, and sustainable industrial ecosystems. Full article

Research suggests that overweight/obese adults (BMI ≥ 25 kg/m²) have an elevated risk of cognitive decline. Although exercise is recommended to improve both physical and cognitive health, adherence is often low in this population. Virtual reality (VR) is an emerging strategy that may enhance exercise engagement. This pilot study compared the effects of traditional (TRAD) cycling and VR-based exercise on cognitive performance and prefrontal cortex (PFC) oxygenation (O₂Hb). Eleven adults (M = 6, F = 5; BMI: 31.1 ± 2.8 kg/m²; VO_2max: 30.4 ± 5.7 mL/kg/min) completed a VO_2max test and two 16 min moderate-intensity cycling sessions (TRAD, VR) on separate days, each followed by a Stroop task (four rounds of 30 trials). Exercise intensity did not differ between conditions (TRAD: %HRmax 73.9 ± 4.2, RPE 12.9 ± 1.5, BLa⁻ 2.5 ± 1.3; VR: %HRmax 74.0 ± 5.6, RPE 12.7 ± 1.4, BLa⁻ 2.7 ± 1.7). Stroop accuracy was similar between conditions; however, response time was faster post-TRAD in round two (p = 0.005) and round three (p = 0.004). No significant differences in PFC O₂Hb were observed. These preliminary results suggest that both TRAD cycling and VR-based exercise are feasible modes of moderate-intensity exercise in overweight/obese adults, with largely comparable post-exercise cognitive outcomes. Larger, counterbalanced studies are warranted. Full article

This study aims to report the first Latvian version of the RESTQ-76, focusing on its internal validity, reliability, structural validity, and the relationships between the factors of the questionnaire. A total of 394 athletes (225 men and 169 women), aged 18–30 years (average age = 21, SD = 1.65), participated in the study. They were recruited from various sports clubs and universities. The RESTQ-76 was translated into Latvian through a forward-backward translation process to ensure accuracy of cultural relevance. To examine the questionnaire’s structure, principal component analysis (PCA) was performed. This analysis revealed a stable four-factor model comprising 18 scales, with acceptable fit indices (RMSEA = 0.089, CFI = 0.914, TLI = 0.896, NFI = 0.889), indicating good model fit. The psychometric validity of the Latvian version is supported by the original, indicating it is appropriate for use in sports settings throughout Latvia. As research on training load and recovery continues to expand, tools like the RESTQ-76 are becoming increasingly important. They help athletes and coaches monitor recovery and stress levels, which is essential for optimizing performance and preventing overtraining in competitive sports. Full article

Cervical cancer remains the fourth most common malignancy among women worldwide. Despite well-developed prevention measures, incidence and mortality continue to rise, especially in low- and middle-income countries due to low screening coverage and unavailability of human papillomavirus (HPV) vaccination. The cervical cancer screening coverage could be improved by the implementation of a self-sampling modality for HPV testing. Multiple research pieces support the validity and reliability of a self-sampling modality as an alternative approach to clinician-collected samples for primary cervical cancer screening via HPV genotyping. Moreover, growing research evidence on the self-sampling modality reception shows high acceptance of the method among screened populations. Studies on the self-sampling approach economic efficiency also revealed a high cost-effectiveness of HPV testing through a self-sampling modality compared to other screening modalities for cervical cancer. It is specifically important for low-resource settings, which should use the self-sampling cost advantages to improve cervical cancer screening coverage by attracting underscreened populations. Overall, self-sampling modality has a higher participation rate and better patient satisfaction reported; thus, the method is highly recommended by the World Health Organization for cervical cancer screening. Full article

The Mediterranean Sea is recognized for its high biodiversity but is also a hotspot for pollution. In this study, fish samples of four native marine species were collected from wild catches to determine contaminants such as Anisakis parasites and heavy metals, including nickel, lead, copper, zinc, and chromium, within local marine fish species. The detection of Anisakis parasites was performed by a visual inspection and a digestion method. Metal analysis was carried out on skin, muscle, viscera, and bones of fish, using Microwave Plasma-Atomic Emission Spectroscopy. This study demonstrated that Boops boops was the least infested species by Anisakis parasite, while Scomber colias was the most infested, with Sardinella aurita and Trachurus trachurus showing a lower infestation rate. Pearson correlation statistics revealed that infestation correlated with fish size but not with maturity or sex. Principal component analysis (PCA) demonstrated that the carnivorous species were more prone to Anisakis infestation than the omnivorous species. The maximum levels of copper, nickel, chromium, lead, and zinc content in fish tissues were 13.2 ± 0.11, 19.5 ± 0.02, 19.9 ± 0.01, 28.8 ± 0.09, and 184.87 ± 0.63 µg/g, respectively. PCA revealed that heavy metal contamination does not discriminate between fish species and sex, as opposed to tissue type and location of catch. Some metals, such as zinc and lead, seem to accumulate more in muscle rather than the other tissues. These findings indicate that Anisakis infestation and heavy metal analysis should be monitored and extended beyond the current EU requirements. Full article

This study examines the impact of sociodemographic factors on the attitudes of EU citizens towards animal welfare and their implications for trade policy. Variations in animal welfare legislation across major commercial blocks create ethical and economic challenges, with the EU implementing stringent standards compared with other countries. Data were drawn from the Special Eurobarometer 533 survey, conducted between the 3rd and 26th March 2023, which employed a multistage, clustered sampling method across all 27 EU Member States, yielding a representative sample of 26,368 respondents. The survey collected detailed information on attitudes toward animal welfare alongside sociodemographic characteristics. The results revealed substantial public support for stricter regulations and informative labelling, with attitudes differing with age (p < 0.001), education (p < 0.001), income (p < 0.001), and political orientation (p < 0.001), according to the multinomial regression applied to each of the independent variables. These results emphasise the importance of these factors in shaping consumer expectations. The findings highlight the need for policymakers to integrate ethical and societal values into trade negotiations, ensuring that policies reflect public concerns, promote fair competition, and encourage higher animal welfare standards internationally. Additionally, understanding the perspectives and motivations of livestock industry stakeholders remains critical for implementing effective welfare strategies. By aligning EU trade policy with citizen values and stakeholder practices, it is possible to advance animal welfare globally while balancing economic and ethical considerations and promoting a fair trade. Full article

Intense training loads alter the skin microbiome and defence mechanisms in athletes, yet adaptation profiles remain insufficiently characterised. This study evaluated the relationships between skin bacterial microbiome structure, antimicrobial activity, dermcidin levels, and acne severity in male professional hockey players compared with amateur athletes and non-athletes. One hundred men (18–57 years) were examined and allocated to six subgroups by exercise intensity and acne status. Microbiota composition was assessed by culture-based methods and MALDI-TOF identification, antimicrobial activity measured spectrophotometrically, dermcidin quantified by ELISA, and sweat proteome characterised by HPLC-MS. Staphylococcus epidermidis and Micrococcus luteus predominated in all groups. Exercise intensity, rather than acne, was the main determinant of total bacterial colonisation, which increased approximately tenfold from non-athletes to professional hockey players. In non-athletes, higher antimicrobial activity correlated with greater acne severity, whereas in professionals this relationship was absent and dermcidin levels showed an inverse association with acne severity. Proteomic analysis identified 17 polypeptides; dermcidin and prolactin-inducible protein were dominant in all groups, and calprotectin (S100-A8/A9) was detected exclusively in healthy professionals. Full article