Search Results (6,381)

Background/Objectives: The aim of this systematic review was to compare the effectiveness of virtual reality (VR)-based training with conventional exercise programs in improving functional outcomes related to fall risk among older adults with various health conditions. Methods: The review was conducted in accordance with the PRISMA 2020 guidelines and registered in PROSPERO (registration number CRD42022345678). The databases Scopus, PubMed, Web of Science, and EBSCO were searched up to 31 March 2025. Randomized controlled trials (RCTs) were included if they involved participants aged ≥60 years, a VR intervention lasting ≥6 weeks, and a control group performing traditional exercises or receiving usual care. Methodological quality was assessed using the PEDro scale, and a narrative synthesis was performed across four outcome domains: balance, mobility, cognitive function, and fall risk. Results: Seven RCTs were included in the analysis (totaling 664 participants). VR training was found to be at least as effective as conventional exercise in improving balance (e.g., Berg Balance Scale) and mobility (e.g., Timed Up and Go), with some studies showing superior effects of VR. One RCT demonstrated that combining VR with balance exercises (MIX) yielded the greatest improvements in muscle strength and physical performance. Additionally, two studies reported cognitive benefits (e.g., MoCA) and a 42% reduction in fall incidence within six months following VR intervention. The methodological quality of the included studies was moderate to high (PEDro score 5–9/10). Conclusions: VR-based training represents a safe and engaging supplement to geriatric rehabilitation, effectively improving balance, mobility, and, in selected cases, cognitive function, while also reducing fall risk. Full article

Brown algae such as Ascophyllum nodosum (AN) and Fucus vesiculosus (FV) are gaining considerable attention as functional feed additives due to their health-beneficial properties. This study evaluated the antioxidant potential of AN and FV extracts in intestinal epithelial cells and the in vivo model Caenorhabditis elegans (C. elegans). Aqueous AN and FV extracts were characterized for total phenolic content (TPC), antioxidant capacity (TEAC, FRAP), and phlorotannin composition using LC-HRMS/MS. Antioxidant effects were assessed in vitro, measuring AAPH-induced ROS production in Caco-2 and IPEC-J2 cells via H₂DCF-DA, and in vivo, evaluating the effects of paraquat-induced oxidative stress and AN or FV treatment on worm motility, GST-4::GFP reporter expression, and gene expression in C. elegans. FV exhibited higher total phenolic content, antioxidant capacity (TEAC, FRAP), and a broader phlorotannin profile (degree of polymerization [DP] 2–9) than AN (DP 2–7), as determined by LC-HRMS/MS. Both extracts attenuated AAPH-induced oxidative stress in epithelial cells, with FV showing greater efficacy. In C. elegans, pre-treatment with AN and FV significantly mitigated a paraquat-induced motility decline by 22% and 11%, respectively, compared to PQ-stressed controls. Under unstressed conditions, both extracts enhanced nematode healthspan, with significant effects observed at 400 µg/g for AN and starting at 100 µg/g for FV. Gene expression analysis indicated that both extracts modulated antioxidant pathways in unstressed worms. Under oxidative stress, pre-treatment with AN and FV significantly reduced GST-4::GFP expression. In the nematode, AN was more protective under acute stress, whereas FV better supported physiological function in the absence of stressors. These findings demonstrate that AN and FV counteract oxidative stress in intestinal epithelial cells and in C. elegans, highlighting their potential as stress-reducing agents in animal feed. Full article

Background: Drug–drug interactions (DDIs) may occur when two or more drugs are taken together, leading to undesired side effects or potential synergistic effects. Most clinical effects of drug combinations have not been assessed in clinical trials. Therefore, predicting DDIs can provide better patient management, avoid drug combinations that can negatively affect patient care, and exploit potential synergistic combinations to improve current therapies in women’s healthcare. Methods: A DDI prediction model was built to describe relevant drug combinations affecting reproductive treatments. Approved drug features (chemical structure of drugs, side effects, targets, enzymes, carriers and transporters, pathways, protein–protein interactions, and interaction profile fingerprints) were obtained. A unified predictive score revealed unknown DDIs between reproductive and commonly used drugs and their associated clinical effects on reproductive health. The performance of the prediction model was validated using known DDIs. Results: This prediction model accurately predicted known interactions (AUROC = 0.9876) and identified 2991 new DDIs between 192 drugs used in different female reproductive conditions and other drugs used to treat unrelated conditions. These DDIs included 836 between drugs used for in vitro fertilization. Most new DDIs involved estradiol, acetaminophen, bupivacaine, risperidone, and follitropin. Follitropin, bupivacaine, and gonadorelin had the highest discovery rate (42%, 32%, and 25%, respectively). Some were expected to improve current therapies (n = 23), while others would cause harmful effects (n = 11). We also predicted twelve DDIs between oral contraceptives and HIV drugs that could compromise their efficacy. Conclusions: These results show the importance of DDI studies aimed at identifying those that might compromise or improve their efficacy, which could lead to personalizing female reproductive therapies. Full article

Goji berries (Lycium barbarum L.) are considered a functional food due to their high content of bioactive compounds with demonstrated health benefits. This study evaluated four cultivars (G3, G4, G5, and G7) grown under Mediterranean climate conditions, focusing on their physicochemical properties (total soluble solids, titratable acidity, and pH), bioactive compound (sugars and organic acids, total and individual phenolic and carotenoid compounds, and antioxidant activities (DPPH and CUPRAC assay)), and aromatic profiles (by GC-MS) to assess their suitability for fresh consumption or incorporation into food products. G4 exhibited the most favorable physicochemical characteristics, with the highest total soluble solids (20.2 °Brix) and sugar content (92.8 g 100 g⁻¹ dw). G5 stood out for its lower titratable acidity (0.34%) and highest ripening index (54.8), indicating desirable flavor attributes. Concerning bioactive compounds, G3 and G4 showed the highest total phenolic content (17.9 and 19.1 mg GAE g⁻¹ dw, respectively), with neochlorogenic acid being predominant. G4 was notable for its high carotenoid content, particularly zeaxanthin (1722.6 μg g⁻¹ dw). These compounds significantly contributed to antioxidant activity. Volatile organic compound (VOC) profiles revealed alcohols and aldehydes as the dominant chemical families, with hexanal being the most abundant. G5 and G7 exhibited the highest total VOC concentrations. Principal component analysis grouped G3 and G4 based on their high sugar and phenolic content, while G5 and G7 were characterized by their complex aromatic profiles. Therefore, G3 and G4 are promising candidates for fresh consumption and potential functional applications, while G5 and G7 are particularly suitable for new product development due to their nutraceutical and aromatic value. Full article

While the gut microbiota of obese children in Mexico has been studied, its relationship with depressive and anxiety symptoms in obese adults remains unexplored. The aim of this study was to describe the gut microbiota profile of Mexican adults with obesity and its association with depression and anxiety. We sequenced the V3-V4 region of the 16S rRNA gene from stool samples of obese adults categorized into four groups: control (OCG), with depressive symptoms (OD), with anxiety symptoms (OAx), or with both (ODAx). Alpha diversity was assessed using t-tests, beta diversity was assessed with PERMANOVA, and taxonomic differences was assessed with LEfSe. Associations between bacterial genera and clinical variables were analyzed using the Maaslin2 library. Bacteroidota was the most prevalent phylum, and Prevotella was the dominant enterotype across all groups. Although overall diversity did not differ significantly, 30 distinct taxonomic biomarkers were identified among groups as follows: 4 in OCG (Firmicutes), 5 in OD (Firmicutes, Bacteroidota), 13 in OAx (Firmicutes, Bacteroidetes, Fusobacteroidota, Proteobacteria), and 8 in ODAx (Firmicutes). This is the first study to identify distinct gut microbiota profiles in obese Mexican adults with depressive and anxiety symptoms. These findings suggest important microbial biomarkers for improving the diagnosis and treatment of mental health conditions in obesity. Full article

Ultrafine bubbles (UFBs) represent an emerging technology with unique physicochemical properties. This study investigated the effects of air-filled UFBs infused in drinking water on gut microbiota composition and the associated health markers in Sprague Dawley rats over a 12-week period. Using a two-phase design, UFB concentration was increased from 1.7 × 10⁶ to 6.5 × 10⁹ UFBs/mL at week 7 to assess dose-dependent effects. Administration of UFBs in drinking water induced significant shifts in gut microbiome populations, characterized by increased Bacteroidetes (+122% weeks 8–12) and decreased Firmicutes (−43% weeks 8–12) compared to controls. These microbial shifts coincided with enhanced short-chain fatty acid production (butyrate +56.0%, p ≤ 0.001; valerate +63.1%, p ≤ 0.01) and reduced inflammatory markers (TNF-α −84.0%, p ≤ 0.05; IL-1β −41.0%, p ≤ 0.05; IL-10 −69.8%, p ≤ 0.05). UFB effects demonstrated systematic concentration-dependent threshold responses, with 85.7% of parameters exhibiting directional reversals between low (1.7 × 10⁶ UFBs/mL) and high (6.5 × 10⁹ UFBs/mL) concentration phases rather than linear dose–response relationships. The systematic nature of these threshold effects, with 71.4% of parameters achieving statistical significance (p ≤ 0.05), indicates concentration-dependent biological mechanisms rather than random effects on gut biology. Despite current metagenomic techniques identifying only 25% of the total gut microbiome, the observed changes in characterized species and metabolites demonstrate UFB technology’s therapeutic potential for conditions requiring microbiome modulation, providing new insights into UFB influence on complex biological systems. Full article

In the face of growing demand for food and climate change, ensuring the stability and height of crop yields is becoming a key challenge for modern agriculture. One of the solutions supporting the sustainable development of crop production is the use of certified seed. The aim of this study was to assess the impact of using certified seed on the level and stability of yields of three cereal species: winter wheat, winter triticale and spring barley, in temperate climate conditions. Data came from surveys conducted on over 8000 farms in six agroecoregions of Poland in 2021–2023. The analysis showed significantly higher yields on farms using certified seed for all species studied. Additionally, greater yield stability (lower values of Shukla variance and Wricke ecovalence) was noted in the case of using certified seeds, especially in region IV. This indicates the positive impact of certified seeds (e.g., genetic purity, health, and vigor) on the efficiency and resilience of agricultural systems. This phenomenon is of particular importance in the context of climate change and may be an important element of risk management strategies in agriculture. Full article

The association between COVID-19 vaccination and thromboembolic events has garnered significant research attention, particularly with the advent of vaccines based on adenoviral vectors, including AstraZeneca’s and Johnson & Johnson’s vaccines. This review underscores the uncommon occurrence of venous thromboembolism (VTE), arterial thromboembolism (ATE), and vaccine-induced thrombotic thrombocytopenia (VITT) following COVID-19 vaccination. Although these complications are extremely rare compared to the heightened risk of thrombosis from COVID-19 infection, elements like age, biological sex, type of vaccine and underlying health conditions may contribute to their development. In addition, rare renal complications such as acute kidney injury and thrombotic microangiopathy have been documented, broadening the spectrum of potential vaccine-associated thrombotic manifestations. Current guidelines emphasize early detection, individualized risk assessment, and use of anticoagulation therapy to mitigate risks. Despite these events, the overwhelming majority of evidence supports the continued use of COVID-19 vaccines, given their proven efficacy in reducing severe illness and mortality. In addition, recent comparative data confirm that mRNA-based vaccines are associated with a significantly lower risk of serious thrombotic events compared to adenoviral vector platforms. Ongoing research is essential to further refine preventive and therapeutic strategies, particularly for at-risk populations. Full article

Male infertility is a multifactorial condition often associated with disruptions in sperm metabolism and mitochondrial function, yet traditional semen analysis provides limited insight into these molecular mechanisms. Understanding sperm bioenergetics and metabolic dysfunctions is crucial for improving the diagnosis and treatment of conditions such as asthenozoospermia and azoospermia. This systematic review synthesizes recent literature, focusing on advanced tools and techniques—including omics technologies, advanced imaging, spectroscopy, and functional assays—that enable comprehensive molecular assessment of sperm metabolism and development. The reviewed studies highlight the effectiveness of metabolomics, proteomics, and transcriptomics in identifying metabolic biomarkers linked to male infertility. Non-invasive imaging modalities such as Raman and magnetic resonance spectroscopy offer real-time metabolic profiling, while the seminal microbiome is increasingly recognized for its role in modulating sperm metabolic health. Despite these advances, challenges remain in clinical validation and implementation of these techniques in routine infertility diagnostics. Integrating molecular metabolic assessments with conventional semen analysis promises enhanced diagnostic precision and personalized therapeutic approaches, ultimately improving reproductive outcomes. Continued research is needed to standardize biomarkers and validate clinical utility. Furthermore, these metabolic tools hold significant potential to elucidate the underlying causes of previously misunderstood and unexplained infertility cases, offering new avenues for diagnosis and treatment. Full article

This study presents the scientific and methodological foundation of Spain’s first national framework for the ethical management of community cat populations: the Action Plan for the Management of Community Cat Colonies (PACF), launched in 2025 under the mandate of Law 7/2023. This pioneering legislation introduces a standardized, nationwide obligation for trap–neuter–return (TNR)-based management of free-roaming cats, defined as animals living freely, territorially attached, and with limited socialization toward humans. The PACF aims to support municipalities in implementing this mandate through evidence-based strategies that integrate animal welfare, biodiversity protection, and public health objectives. Using standardized data submitted by 1128 municipalities (13.9% of Spain’s total), we estimated a baseline population of 1.81 million community cats distributed across 125,000 colonies. These data were stratified by municipal population size and applied to national census figures to generate a model-ready demographic structure. We then implemented a stochastic simulation using Vortex software to project long-term population dynamics over a 25-year horizon. The model integrated eight demographic–environmental scenarios defined by a combination of urban–rural classification and ecological reproductive potential based on photoperiod and winter temperature. Parameters included reproductive output, mortality, sterilization coverage, abandonment and adoption rates, stochastic catastrophic events, and territorial carrying capacity. Under current sterilization rates (~20%), our projections indicate that Spain’s community cat population could surpass 5 million individuals by 2050, saturating ecological and social thresholds within a decade. In contrast, a differentiated sterilization strategy aligned with territorial reproductive intensity (50% in most areas, 60–70% in high-pressure zones) achieves population stabilization by 2030 at approximately 1.5 million cats, followed by a gradual long-term decline. This scenario prioritizes feasibility while substantially reducing reproductive output, particularly in rural and high-intensity contexts. The PACF combines stratified demographic modeling with spatial sensitivity, offering a flexible framework adaptable to local conditions. It incorporates One Health principles and introduces tools for adaptive management, including digital monitoring platforms and standardized welfare protocols. While ecological impacts were not directly assessed, the proposed demographic stabilization is designed to mitigate population-driven risks to biodiversity and public health without relying on lethal control. By integrating legal mandates, stratified modeling, and realistic intervention goals, this study outlines a replicable and scalable framework for coordinated action across administrative levels. It exemplifies how national policy can be operationalized through data-driven, territorially sensitive planning tools. The findings support the strategic deployment of TNR-based programs across diverse municipal contexts, providing a model for other countries seeking to align animal welfare policy with ecological planning under a multi-level governance perspective. Full article

Introduction and Objectives: Risk-sharing agreements (RSAs) have emerged as a key strategy for financing high-cost medical technologies while ensuring financial sustainability. These payment mechanisms mitigate clinical and financial uncertainties, optimizing pricing and reimbursement decisions. Despite their widespread adoption globally, Latin America has reported limited implementation, particularly for high-cost medical devices. This study aims to share insights from designing RSAs in the form of Outcome Protection Programs (OPPs) for medical devices in Latin America from the perspective of a medical devices company. Methods: The report follows a structured approach, defining key OPP dimensions: payment base, access criteria, pricing schemes, risk assessment, and performance incentives. Risks were categorized as financial, clinical, and operational. The framework applied principles from prior models, emphasizing negotiation, program design, implementation, and evaluation. A multidisciplinary task force analyzed patient needs, provider motivations, and payer constraints to ensure alignment with health system priorities. Results: Over two semesters, a panel of seven experts from the manufacturer designed n = 105 innovative payment programs implemented in Argentina (n = 7), Brazil (n = 7), Colombia (n = 75), Mexico (n = 9), Panama (n = 4), and Puerto Rico (n = 3). The programs targeted eight high-burden conditions, including Coronary Artery Disease, atrial fibrillation, Heart Failure, and post-implantation arrhythmias, among others. Private providers accounted for 80% of experiences. Challenges include clinical inertia and operational complexities, necessitating structured training and monitoring mechanisms. Conclusions: Outcome Protection Programs offer a viable and practical risk-sharing approach to financing high-cost medical devices in Latin America. Their implementation requires careful stakeholder alignment, clear eligibility criteria and endpoints, and robust monitoring frameworks. These findings contribute to the ongoing dialogue on sustainable healthcare financing, emphasizing the need for tailored approaches in resource-constrained settings. Full article

Social media platforms have emerged as valuable sources for mental health research, enabling the detection of conditions such as depression through analyses of user-generated posts. This manuscript offers practical, step-by-step guidance for applying machine learning and deep learning methods to mental health detection on social media. Key topics include strategies for handling heterogeneous and imbalanced datasets, advanced text preprocessing, robust model evaluation, and the use of appropriate metrics beyond accuracy. Real-world examples illustrate each stage of the process, and an emphasis is placed on transparency, reproducibility, and ethical best practices. While the present work focuses on text-based analysis, we discuss the limitations of this approach—including label inconsistency and a lack of clinical validation—and highlight the need for future research to integrate multimodal signals and gold-standard psychometric assessments. By sharing these frameworks and lessons, this manuscript aims to support the development of more reliable, generalizable, and ethically responsible models for mental health detection and early intervention. Full article

Climate change is generally having a negative impact on forest health by inducing drought stress and favouring the spread of pest species, such as bark beetles. The terrestrial monitoring of bark beetle infestation is very time-consuming, especially in the early stages, and therefore not feasible for extensive areas, emphasising the need for a comprehensive approach based on remote sensing. Although numerous studies have researched the use of optical data for this task, radar data remains comparatively underexplored. Therefore, this study uses the weekly and cloud-free acquisitions of Sentinel-1 in the Bavarian Forest National Park. Time series analysis within a Multi-SAR framework using Random Forest enables the monitoring of moisture content loss and, consequently, the assessment of tree vitality, which is crucial for the detection of stress conditions conducive to bark beetle outbreaks. High accuracies are achieved in predicting future bark beetle infestation (R² of 0.83–0.89). These results demonstrate that forest vitality trends ranging from healthy to bark beetle-affected states can be mapped, supporting early intervention strategies. The standard deviation of 0.44 to 0.76 years indicates that the model deviates on average by half a year, mainly due to the uncertainty in the reference data. This temporal uncertainty is acceptable, as half a year provides a sufficient window to identify stressed forest areas and implement targeted management actions before bark beetle damage occurs. The successful application of this technique to extensive test sites in the state of North Rhine-Westphalia proves its transferability. For the first time, the results clearly demonstrate the expected relationship between radar backscatter expressed in the Kennaugh elements K₀ and K₁ and bark beetle infestation, thereby providing an opportunity for the continuous and cost-effective monitoring of forest health from space. Full article

Salmonella is one of the main causes of food-borne illness worldwide. In most cases, Salmonella contamination can be traced back to food processing plants and/or to cross-contamination during food preparation. To avoid food-borne diseases, food processing plants use sanitizers and biocidal to reduce bacterial contaminants below acceptable levels. Despite these preventive actions, Salmonella can survive and consequently affect human health. This study investigates the adaptive capacity of the main Salmonella enterica serotypes isolated from the poultry production line, focusing on their replication, antimicrobial resistance, and biofilm formation under stressors such as acidic conditions, oxidative environment, and high osmolarity. Using growth curve analysis, crystal violet staining, and microscopy, we assessed replication, biofilm formation, and antimicrobial resistance under acidic, oxidative, and osmotic stress conditions. Disinfectant tolerance was evaluated by determining the Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) of sodium hypochlorite. The antibiotic resistance was assessed using the Kirby–Bauer method. The results indicate that, in general, acidic and osmotic stress reduce the growth of Salmonella. However, no significant differences were observed specifically for serotypes Infantis, Heidelberg, and Corvallis. The S. Infantis isolates were the strongest biofilm producers and showed the highest prevalence of multidrug resistance (71%). Interestingly, S. Infantis forming biofilms required up to 8-fold higher concentrations of sodium hypochlorite for eradication. Furthermore, osmotic and oxidative stress significantly induced biofilm production in industrial S. Infantis isolates compared to a reference strain. Understanding how Salmonella responds to industrial stressors is vital for designing strategies to control the proliferation of these highly adapted, multi-resistant pathogens. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article