Ischemia–reperfusion injury (IRI) is a complex and clinically significant pathophysiological process that occurs when blood flow is restored to an organ following ischemia. While reperfusion is essential for tissue survival, it paradoxically exacerbates damage through mechanisms such as oxidative stress, inflammation, mitochondrial dysfunction, and endothelial injury. This phenomenon contributes significantly to morbidity and mortality in various clinical settings, including organ transplantation, myocardial infarction, stroke, and liver and kidney injuries. Despite increasing knowledge of the molecular pathways involved, effective treatment options remain limited. Recent scientific advances have highlighted the potential of natural bioactive compounds to reduce oxidative and inflammatory responses during IRI. Hass avocado (Persea americana Mill.) has garnered considerable attention as a rich source of phytochemicals with strong antioxidant and anti-inflammatory properties, including carotenoids, tocopherols, polyphenols, and monounsaturated fatty acids (MUFAs). These compounds may act synergistically to neutralize reactive oxygen species, inhibit proinflammatory mediators, and modulate key cellular pathways involved in IRI. This comprehensive review explores the current evidence regarding the protective effects of Hass avocado bioactive compounds against IRI. We examined their molecular mechanisms of action and discussed the therapeutic implications of incorporating these natural agents into clinical practice. To our knowledge, this is the first review specifically addressing the role of Hass avocado phytochemicals in reducing oxidative damage and inflammation in IRI, offering a novel perspective for future research and clinical application.
Climate change is reshaping species distributions worldwide, with potential consequences for biodiversity and ecosystem services. In Mexico, the agave weevil (Scyphophorus acupunctatus), a pest of ecologically and economically important agave species, poses a threat to both wild populations and cultivated systems. In this study, we employed an ecological niche modeling framework to assess the present and future potential distributions of the agave weevil and seven significant Agave species (A. americana, A. tequilana, A. salmiana, A. angustifolia, A. cupreata, A. karwinskii, and A. potatorum) for the 2041-2060 period. Based on bioclimatic variables and two shared socioeconomic pathways (SSPs), we projected shifts in species distributions and evaluated the potential overlap between the weevil and its host plants. Our findings revealed divergent responses: while suitable habitats for several Agave species are projected to decline, the climatic suitability for S. acupunctatus is likely to expand, particularly under high-emission scenarios. Niche overlap analysis predicts an increased co-occurrence between the weevil and economically critical species such as A. tequilana and A. americana, representing potential risks to the tequila and mezcal industries. This study establishes a robust bioclimatic baseline for conservation planning and adaptive management, identifying regions where monitoring and mitigation should be prioritized under climate change, and emphasizing the need for integrated approaches, such as biological control and habitat conservation, to safeguard the cultural and economic heritage tied to these emblematic plants.
Cardiovascular diseases (CVD) are among the leading causes of mortality worldwide due to genetic predisposition and lifestyle factors. Proper diagnosis of cardiovascular diseases is crucial to provide early-stage treatments. Conventional diagnostic methods such as stress tests, electrocardiograms, and echocardiography detect valuable insights into rhythm abnormalities, structural anomalies, or other cardiovascular conditions. However, their reliability heavily depends on human expertise, and they may not always detect early-stage signs of disease. In recent years, Machine Learning (ML) models have emerged as alternative diagnosis tools, capable of identifying CVD with higher accuracy. ML enables automated and precise detection based on data relationships, capturing hidden, complex patterns that are not apparent through traditional diagnostics. Most ML approaches employ supervised learning, which requires labeled data that are not always available in medical records. Under such circumstances, unsupervised learning has been explored as a suitable alternative. In this paper, a hybrid unsupervised approach combines the neural network structure of Self-Organizing Maps (SOM) with the dimensionality reduction technique of Principal Component Analysis (PCA) for unsupervised analysis for clustering CVD across different severity levels. Considering a data compression mechanism, the synergy among these methods leverages the ability to map unsupervised complex, high-dimensional data into lower-dimensional space. The proposed approach significantly improves the detection of hidden structures within large, high-dimensional medical cardiovascular datasets, providing insights into cardiovascular risk factors and improving the overall diagnostic process. Experimental evaluation on the UCI Cleveland Heart Disease dataset shows that the proposed PCA-SOM model achieves a Silhouette score of 0.94 (train) and 0.79 (test), and a Davies-Bouldin index of 0.08 (train) and 0.16 (test), outperforming baseline clustering methods such as K-means, hierarchical clustering, Gaussian Mixture and Spectral clustering highlighting its potential for supporting CVD detection.
Trauma exposure and posttraumatic stress (PTS) symptoms are well-documented health disparities in Latinx migrants, explaining a diverse array of physical and mental health complaints as well as role limitations for both youth and adults. Few studies have examined the influence of the migration journey on PTS in Latinx migrants. We examined the added effect of migration-related predictors, above and beyond general trauma exposure, of PTS in two samples of Latinx migrants with the broad aim of uncovering unique predictors of PTS in this high-risk population. Both studies investigated predictors of posttraumatic distress using information collected about individuals’ demographics (e.g., age, gender, country of birth) and migration journey to the U.S., while controlling for pre-migration trauma exposure. The current studies used one sample of Latinx adult migrants seeking asylum (N = 276) collected at the Texas-Mexico border and one sample of recently immigrated Latinx youth (N = 69) collected at an urban school in the Southwestern United States. Across both samples, hierarchical regression analyses revealed that witnessing or experiencing something frightening during migration (p = 0.009 in youth; p <0.001 in adults) predicted PTS, even after controlling for general trauma exposure. Our results underscore the importance of routinely screening Latinx migrants for migration-related trauma in clinical and community settings. As the U.S. halts asylum processing as part of its sweeping immigration enforcement actions, our findings highlight the urgent need to expand legal paths to entry to prevent migrants from being forced into traumatic and dangerous routes.
Noise pollution poses a serious threat to human health and well-being, especially in educational environments where concentration and learning are essential. While urban noise has been widely studied, its effects within university settings remain underexplored. This study investigates environmental noise and student perceptions on two campuses of the University of Guadalajara, Mexico-one located in an urban area and the other in a semi-rural setting. Noise levels were measured using the CESVA-SC260 integrating instrument (CESVA Instruments, SLU, Barcelona, Spain), and student perceptions were gathered through a survey. A total of 731 students participated, with 357 from the urban campus and 374 from the semi-rural one. Results showed that noise levels on both campuses frequently exceeded the WHO's recommended limit of 55 dB(A) for educational facilities, with readings between 40.9 and 85.0 dB(A); 89% of measurements surpassed the threshold. Major sources of noise included vehicular traffic, student gatherings, and construction-related machinery. Survey responses indicated that 41% of students perceived noise as a health risk, and 96% reported adverse effects on well-being and identified it as a disruptor of academic tasks. These findings underscore the pressing need for targeted noise management strategies in university environments and call for further research into effective, context-specific interventions that enhances learning conditions.
