Search Results (83)

M8OI is a cytotoxic methylimidazolium ionic liquid solvent through its binding to the ubiquinone binding site on complex I of the mitochondrial electron transport chain. Given the overlap in terms of toxic mechanism of action with the pesticide rotenone, the potential neurotoxic effects of M8OI were examined. In vitro, cytotoxicity and mitochondrial function were assessed in SH-SY5Y cells by measuring MTT reduction and oxygen consumption/extracellular acidification using a Seahorse analyser. SH-SY5Y cells were sensitised to M8OI toxicity by replacing medium glucose with galactose. Glucose protected the cells from M8OI toxicity, whereas galactose showed no clear dose–response protection. M8OI induced a dose-dependent reduction in oxygen consumption rate with a compensatory increase in extracellular acidification rate, consistent with inhibition of mitochondrial oxidative phosphorylation and a shift toward glycolysis. In vivo, rats were orally exposed via drinking water for 20 weeks and assessed using behavioural tests. In addition, the concentrations of M8OI and its metabolites were quantified by LC–MS in rat brain and other tissues. In rats, M8OI concentrations were ~30-fold higher in kidney than brain, and brain levels were at least 100-fold lower than the concentrations that affected SH-SY5Y cell viability in vitro. However, based on open field tests, M8OI exposure suppressed motor activity without any anxious behaviours. The cytotoxicity of M8OI in SH-SY5Y neuroblastoma cells was associated with metabolic mitochondrial dysfunction. However, the neurobehavioural changes observed in orally exposed rats occurred at significantly lower brain concentrations than would be predicted to lead to neural cell death. Nevertheless, direct comparisons between acute in vitro exposures and chronic in vivo outcomes should be interpreted cautiously. Full article

Reactive oxygen species (ROS) are an essential component for the maintenance of cellular function. However, if produced in excess, ROS can drive cellular dysfunction and compromise cell viability. Indeed, uncontrolled ROS production plays a pivotal role in the pathogenesis of type 2 diabetes (T2D), contributing to the loss of β-cell function and the impairment in insulin signalling, as well as driving the development of diabetic complications, which can severely compromise quality of life. T2D is characterised by persistent hyperglycaemia, which is a leading contributor to ROS overproduction in this disease state. This enhanced, almost uncontrolled, increase in glucose metabolism upregulates several ROS-producing pathways, including the hexosamine pathway, protein kinase C, NADPH oxidase and the mitochondrial electron transport chain. There is accumulating evidence to suggest that in a bid to preserve redox homeostasis, ROS acts to suppress glucose metabolism by inactivating several enzymes involved in the regulation of glycolytic flux, including glucokinase, glyceraldehyde 3-phosphate dehydrogenase, phosphofructokinase-1 and pyruvate kinase. Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) is a multi-faceted transcription factor, with a central role in ROS signalling and redox homeostasis. Whilst NF-κB mediates the transcriptional regulation of many pro-oxidants, NF-κB activity is also regulated by the oxidative status, with ROS having both inhibitory and stimulatory roles in these signalling pathways. Interestingly, NF-κB is also involved in controlling the delicate balance between glycolytic flux and mitochondrial respiration. This review will summarise the interplay linking hyperglycaemia with ROS formation, emphasising the role of glucose metabolism in the process, and the crosstalk of these pathways with NF-κB. Full article

Autumn overseeding with cool-season turfgrass species is a widely adopted practice under Mediterranean climatic conditions to mitigate winter dormancy and loss of green color in bermudagrass (Cynodon dactylon). This study evaluated, over two consecutive winter seasons (2022–2023 and 2023–2024), the performance of five cool-season turfgrass cultivars used for autumn overseeding on bermudagrass (‘Arden 15’) in Valencia, eastern Spain. The cultivars included Lolium multiflorum ‘Upstart’, Lolium perenne ‘CT7’ and ‘Sirtaky’, Poa pratensis ‘Liberator’, and Poa trivialis ‘Dasas’. Turf performance was assessed weekly from December to April using visual green color ratings, normalized difference vegetation index (NDVI) measured with two hand-held sensors (GreenSeeker and CropCircle), and normalized difference red edge index (NDRE). The area under the progress curve (AUPC) was calculated as an integrative indicator of turf performance over time. Winter temperature differences significantly influenced bermudagrass dormancy duration and overseeding response. Among the evaluated cultivars, ‘CT7’ consistently showed the highest winter greenness and vigor but exhibited a darker green color than bermudagrass, potentially reducing visual uniformity. The L. perenne ‘Sirtaky’ and P. pratensis ‘Liberator’ cultivars provided a closer chromatic match, although ‘Liberator’ established more slowly. The NDVI and NDRE measurements supported the visual assessments, though correlations between sensors varied among cultivars and seasons, with the GreenSeeker sensor detecting larger cultivar differences than the CropCircle sensor, particularly during colder winters. In addition, the AUPC proved to be an effective integrative metric for comparing cultivar performance over a defined period. Overall, overseeding effectively reduced winter discoloration of bermudagrass, with ‘Sirtaky’ emerging as the most balanced option for Mediterranean sports overseeding management on C. dactylon (‘Arden 15’). Full article

Eco-anxiety—emotional distress arising from awareness of environmental collapse—has become a critical dimension of social sustainability, linking mental well-being, professional functioning, institutional trust, and climate governance. This study investigates how higher education professionals (HEPs) experience and interpret eco-anxiety within their professional contexts, situating it as a lens on institutional legitimacy from the perspective of those who produce, teach, and steward climate knowledge. A cross-sectional mixed-methods survey of 556 HEPs was conducted across a month in 2023, combining an adapted climate anxiety scale with open-ended narratives. Quantitative analyses identified perceived governmental inadequacy as the strongest correlate of climate worry (β = 0.48, p < 0.001), accounting for 26% of the variance, whereas institutional inadequacy had a weaker effect. Qualitative findings revealed pervasive emotions of moral injury, solastalgia, and exhaustion when sustainability rhetoric outpaced genuine action, with many respondents describing governmental and institutional “betrayal.” Integrating Cognitive Appraisal Theory with concepts of moral legitimacy, the study conceptualises eco-anxiety as a relational and ethically grounded emotion reflecting the perceived misalignment between knowledge and governance. Addressing it requires transparent climate leadership, participatory governance, and organisational care infrastructures to sustain motivation and trust within universities. Eco-anxiety thus may function not only as a personal pathology but also as a psychosocial response that can illuminate HEPs’ perceptions of institutional misalignment with sustainability commitments, with implications for higher education’s contribution to the Sustainable Development Goals. Full article

Chemotherapy-related headaches pose a significant challenge to the well-being and treatment adherence of cancer patients. Despite their prevalence, the underpinning mechanisms and pathobiology remain elusive, limiting treatment options. Herein, we review emerging causes, molecular and functional processes, and mechanisms at play, and discuss research and clinical gaps. We consider the iatrogenic and psychogenic effects of chemotherapy and highlight the need to distinguish chemotherapy-related headaches from primary headache disorders in cancer patients, including migraines or tension-type headaches. We discuss evolving biomarkers and mechanistic models that could facilitate the differential diagnosis and development of effective interventions. Given the global rise of cancer burden and better outcomes of chemotherapy with longer life expectancy, recognition of the detrimental impact of chemotherapy-related headaches and their integration into management plans are expected to improve treatment adherence and post-treatment life quality. Full article

Severe combined immunodeficiency (SCID) and spinal muscular atrophy (SMA) are being added to the Newborn Bloodspot Screening (NBS) programme in the Republic of Ireland. To support this expansion, we conducted a scoping review to identify reported timeframes for implementing national, regional or state-wide expanded NBS programmes. We performed a scoping review of the literature published between 2015 and 2025. Eligible articles described the timeframes for implementation of expanded NBS programmes for SCID, SMA or additional metabolic conditions. Sources included PubMed, Embase, citation searching, the International Journal of Neonatal Screening and grey literature. A narrative synthesis was undertaken. Fourteen articles met the inclusion criteria, describing the addition of new conditions—SCID (N = 7), SMA (N = 4), or multiple conditions (N = 3) to expanded NBS programmes in the United States (US), Europe (Belgium, Catalonia, the Czech Republic, Estonia, Germany, Norway, Poland, Portugal, Slovakia, Slovenia, Sweden, and Tuscany), Hong Kong and New Zealand. In most jurisdictions, the implementation of NBS programmes for new conditions took two to six years. The implementation of NBS for new conditions requires considerable time and coordinated efforts. Further research providing greater detail on the specific implementation steps, along with associated timelines, would provide valuable guidance for jurisdictions aiming to expand NBS programmes globally. Full article

The role of petting zoo animals in the dissemination of disease has been widely studied, yet understanding the potential reservoir of antimicrobial resistance (AMR) in these centres has not been explored in the United Kingdom (UK). To understand the carriage of AMR pathogens within petting zoos, this study aimed to identify AMR in E. coli and Staphylococcus intermedius group (SIG) isolated from faeces and skin, respectively, including selective cultures for ESBL-E. coli and methicillin-resistant staphylococci. Faecal samples and skin swabs were collected from 166 petted mammals across eight UK centres to recover E. coli and coagulase-positive staphylococci (CoPS), respectively, through enrichment culture methods, plating onto non-selective (tryptone bile-x agar, mannitol salt agar) and selective media (ESBL ChromID, mannitol salt agar with 6 mg/L oxacillin). Antimicrobial susceptibility was assessed using Kirby-Bauer disc diffusion, covering eight classes of antimicrobials. Antimicrobial usage records from the past 12-months were obtained from 7/8 centres. Overall, 145/166 faecal samples yielded 223 E. coli isolates, with an overall AMR prevalence of 42.6%. Thirteen E. coli isolates (from 8.5% of animals) were classified as multidrug-resistant. ESBL-producing E. coli were detected in 5/166 faecal samples. From 166 skin swabs, 84 yielded CoPS isolates, with S. aureus (n = 70), SIG (n = 13) and S. hyicus (n = 1) identified. Overall, 25.3% of SIG isolates exhibited resistance to at least one antimicrobial. Antimicrobial usage correlated positively with AMR prevalence for E. coli (p < 0.001), though was not associated with multidrug-resistance. This study demonstrates for the first time the presence of AMR within bacteria isolated from UK petting zoo animals, highlighting this reservoir of AMR bacteria. Full article

Assessing the impacts of future changes in rainfall, temperature, and land use on streamflow and nutrient loads is critical for long-term watershed management, particularly in the unglaciated Driftless Area with steep slopes, erodible soils, and karst geology. This study evaluates the Kickapoo watershed in southwestern Wisconsin to examine how projected climate change and cropland expansion may affect hydrology during the mid- (post-2050) and late century (post-2070). Climate projections suggest temperature increase, wetter springs, and drier summers over the century. Annual average streamflow is projected to decline by 5–40% relative to 2000–2020, primarily due to a 5–15% reduction in groundwater discharge. While land use changes from prairie to cropland had a limited additional impact on streamflow, it increased annual average total phosphorus (TP) by 5.67–10.08%, total nitrogen (TN) by 1.08–2.34%, and sediment by 3.11–6.07%, frequently exceeding total maximum daily load (TMDL) thresholds in comparison to the climate change scenario. These findings suggest that although land use changes exacerbate nutrient and sediment pollution, climate change remains the dominant driver of hydrologic alteration in this watershed. Instead, converting 18% (~290 km²) of cropland to grassland could enhance baseflow (0.84–14%), and reduce TP (30–45%), TN (3–5%), sediment (80–90%), and meeting TMDL 90% of the time. These findings underscore the importance of nature-based solutions, such as prairie restoration, supporting adaptive management to reduce nutrient load, sustaining low flows, and strengthening hydrologic resilience, that support key Sustainable Development Goals. This approach offers valuable insights for other unglaciated watersheds globally. Full article

Technological innovation has transformed educational settings, enabling artificial intelligence (AI)-driven teaching and learning processes. While AI is still in its embryonic stage in education, generative artificial intelligence has evolved rapidly, significantly shifting the teaching and learning context. With no clarity about the impacts of generative artificial intelligence on education, there is a need to synthesise research findings to demystify generative artificial intelligence and address concerns regarding its application in the teaching and learning process. This paper systematically synthesises studies on generative artificial intelligence in teaching and learning to understand key arguments and stakeholders’ perceptions of generative artificial intelligence in teaching and learning. The systematic review reveals five main domains of research within the field: (i) current awareness (understanding) of generative artificial intelligence, (ii) stakeholder perceptions, (iii) mechanisms for adopting generative artificial intelligence, (iv) issues and challenges of implementing generative artificial intelligence, and (v) contributions of generative artificial intelligence to student performance. This review examines the practical and policy implications of generative artificial intelligence, providing recommendations to address the concerns and challenges associated with generative artificial intelligence-driven teaching and learning processes. Full article

Background: Parkinson’s disease (PD) is a progressive neurodegenerative condition that impairs motor and non-motor functions. Early and accurate diagnosis is critical for effective management and care. Leveraging machine learning (ML) techniques, this study aimed to develop a robust prediction system for PD using a stacked ensemble learning approach, addressing challenges such as imbalanced datasets and feature optimization. Methods: An open-access PD dataset comprising 22 vocal attributes and 195 instances from 31 subjects was utilized. To prevent data leakage, subjects were divided into training (22 subjects) and testing (9 subjects) groups, ensuring no subject appeared in both sets. Preprocessing included data cleaning and normalization via min–max scaling. The synthetic minority oversampling technique (SMOTE) was applied exclusively to the training set to address class imbalance. Feature selection techniques—forward search, gain ratio, and Kruskal–Wallis test—were employed using subject-wise cross-validation to identify significant attributes. The developed system combined support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN), and decision tree (DT) as base classifiers, with logistic regression (LR) as the meta-classifier in a stacked ensemble learning framework. Performance was evaluated using both recording-wise and subject-wise metrics to ensure clinical relevance. Results: The stacked ensemble learning model achieved realistic performance with a recording-wise accuracy of 84.7% and subject-wise accuracy of 77.8% on completely unseen subjects, outperforming individual classifiers including KNN (81.4%), RF (79.7%), and SVM (76.3%). Cross-validation within the training set showed 89.2% accuracy, with the performance difference highlighting the importance of proper validation methodology. Feature selection results showed that using the top 10 features ranked by gain ratio provided optimal balance between performance and clinical interpretability. The system’s methodological robustness was validated through rigorous subject-wise evaluation, demonstrating the critical impact of validation methodology on reported performance. Conclusions: By implementing subject-wise validation and preventing data leakage, this study demonstrates that proper validation yields substantially different (and more realistic) results compared to flawed recording-wise approaches. The findings underscore the critical importance of validation methodology in healthcare ML applications and provide a template for methodologically sound PD classification research. Future research should focus on validating the model with larger, multi-center datasets and implementing standardized validation protocols to enhance clinical applicability. Full article

Urban microclimates, which include phenomena such as urban heat islands (UHIs) as well as cooler environments created by shaded areas and green spaces, significantly affect social behavior and contribute to varying levels of social isolation in cities. UHIs, driven by heat-absorbing materials like concrete and asphalt, can increase urban temperatures by up to 12 °C, discouraging outdoor activities, especially among vulnerable populations like the elderly and those with chronic health conditions. In contrast, shaded areas and green spaces, where temperatures can be 2–5 °C cooler, encourage outdoor engagement and foster social interaction. This narrative review aims to synthesize current literature on the relationship between urban microclimates and social isolation, focusing on how UHIs and shaded areas influence social engagement. A comprehensive literature review was conducted, selecting sources based on their relevance to the effects of localized climate variations on social behavior, access to green spaces, and the impact of urban design interventions. A total of 142 articles were initially identified, with 103 included in the final review after applying inclusion/exclusion criteria. Key studies from diverse geographical and cultural contexts were analyzed to understand the interplay between environmental conditions and social cohesion. The review found that UHIs exacerbate social isolation by reducing outdoor activities, particularly for vulnerable groups such as the elderly and individuals with chronic health issues. In contrast, shaded areas and green spaces significantly mitigate isolation, with evidence showing that in specific study locations such as urban parks in Copenhagen and Melbourne, such areas increase outdoor social interactions by up to 25%, reduce stress, and enhance community cohesion. Urban planners and policymakers should prioritize integrating shaded areas and green spaces in city designs to mitigate the negative effects of UHIs. These interventions are critical for promoting social resilience, reducing isolation, and fostering connected, climate-adaptive communities. Future research should focus on longitudinal studies and the application of smart technologies such as IoT sensors and urban monitoring systems to track the social benefits of microclimate interventions. Full article

Epigenetic dysregulation has emerged as an important player in the pathobiology of neurodegenerative diseases (NDDs), such as Alzheimer’s, Parkinson’s, and Huntington’s diseases. Aberrant DNA methylation, histone modifications, and dysregulated non-coding RNAs have been shown to contribute to neuronal dysfunction and degeneration. These alterations are often exacerbated by environmental toxins, which induce oxidative stress, inflammation, and genomic instability. Reversing epigenetic aberrations may offer an avenue for restoring brain mechanisms and mitigating neurodegeneration. Herein, we revisit the evidence suggesting the ameliorative effects of epigenetic modulators in toxin-induced models of NDDs. The restoration of normal gene expressions, the improvement of neuronal function, and the reduction in pathological markers by histone deacetylase (HDAC) and DNA methyltransferase (DNMT) inhibitors have been demonstrated in preclinical models of NDDs. Encouragingly, in clinical trials of Alzheimer’s disease (AD), HDAC inhibitors have caused improvements in cognition and memory. Combining these beneficial effects of epigenetic modulators with neuroprotective agents and the clearance of misfolded amyloid proteins may offer synergistic benefits. Reinforced by the emerging methods for more effective and brain-specific delivery, reversibility, and safety considerations, epigenetic modulators are anticipated to minimize systemic toxicity and yield more favorable outcomes in NDDs. In summary, although still in their infancy, epigenetic modulators offer an integrated strategy to address the multifactorial nature of NDDs, altering their therapeutic landscape. Full article

Although there are studies on the use of different tea extracts in animal diets, there are no studies on the use of fermented black tea factory production waste (FTFW) in rodent diets. This study aims to evaluate the effects of FTFW, considered an environmentally hazardous waste, in standard rat diets regarding nutritional, histopathological, and biochemical parameters. In this study, 40 male Wistar albino rats (70 days old, with a live weight of 200–250 g) were divided into four groups, each containing 10 rats. They were fed standard rat diets for 63 days, with varying amounts (0%, 3%, 5%, and 10%) of tea production waste. At the end of the experiment, biochemical analysis of blood and histopathological analysis of all organs were performed. There was no significant difference between the groups in terms of body weight, internal organ weights, or serum biochemical parameters (p > 0.05). No pathological findings were observed in any of the groups. The group receiving 10% tea waste, which had the highest total polyphenol level (1.42 ppm), also showed the highest serum total antioxidant status (TAS) (p < 0.05). In contrast, this group had the lowest total oxidant status (TOS) and oxidative stress index (OSI) levels (p < 0.05). The study concluded that FTFW can be included in the pellet diet of rats without changing their general health status and that a potential environmental pollutant can be used in rat diets. Full article

Artificial Intelligence (AI) is rapidly transforming orthodontic care by providing personalized treatment plans that enhance precision and efficiency. This narrative review explores the current applications of AI in orthodontics, particularly its role in predicting tooth movement, fabricating custom aligners, optimizing treatment times, and offering real-time patient monitoring. AI’s ability to analyze large datasets of dental records, X-rays, and 3D scans allows for highly individualized treatment plans, improving both clinical outcomes and patient satisfaction. AI-driven aligners and braces are designed to apply optimal forces to teeth, reducing treatment time and discomfort. Additionally, AI-powered remote monitoring tools enable patients to check their progress from home, decreasing the need for in-person visits and making orthodontic care more accessible. The review also highlights future prospects, such as the integration of AI with robotics for performing orthodontic procedures, predictive orthodontics for early intervention, and the use of 3D printing technologies to fabricate orthodontic devices in real-time. While AI offers tremendous potential, challenges remain in areas such as data privacy, algorithmic bias, and the cost of adopting AI technologies. However, as AI continues to evolve, its capacity to revolutionize orthodontic care will likely lead to more streamlined, patient-centered, and effective treatments. This review underscores the transformative role of AI in modern orthodontics and its promising future in advancing dental care. Full article

The integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering new opportunities to enhance diagnostic accuracy, personalize treatment plans, and improve service delivery. This review provides a comprehensive overview of the current applications and future potential of AI in ophthalmology. AI algorithms, particularly those utilizing machine learning (ML) and deep learning (DL), have demonstrated remarkable success in diagnosing conditions such as diabetic retinopathy (DR), age-related macular degeneration, and glaucoma with precision comparable to, or exceeding, human experts. Furthermore, AI is being utilized to develop personalized treatment plans by analyzing large datasets to predict individual responses to therapies, thus optimizing patient outcomes and reducing healthcare costs. In surgical applications, AI-driven tools are enhancing the precision of procedures like cataract surgery, contributing to better recovery times and reduced complications. Additionally, AI-powered teleophthalmology services are expanding access to eye care in underserved and remote areas, addressing global disparities in healthcare availability. Despite these advancements, challenges remain, particularly concerning data privacy, security, and algorithmic bias. Ensuring robust data governance and ethical practices is crucial for the continued success of AI integration in ophthalmology. In conclusion, future research should focus on developing sophisticated AI models capable of handling multimodal data, including genetic information and patient histories, to provide deeper insights into disease mechanisms and treatment responses. Also, collaborative efforts among governments, non-governmental organizations (NGOs), and technology companies are essential to deploy AI solutions effectively, especially in low-resource settings. Full article