Background Previous studies suggest that approximately 30-50% of patients with MS exhibit impairments in one or more domains of social cognition. This study aimed to identify the factors associated with social cognition impairment (SCI) in Mexican patients with MS. Methods The study used a cross-sectional design. We included 100 MS patients (47 MS with SCI and 53 MS without SCI). We used the SDMT, CVLT-II, BVMT-R, WCST, Faux-Pas Test, Emotion Recognition Test, and Interpersonal Reactivity Index for neuropsychological assessment. We used logistic regression to identify factors associated with SCI. Results Factors associated with SCI in the crude analysis were age at MS diagnosis ≥50 years (OR 5.09, 95% CI=1.32-19.57, p=0.018), EDSS≥4 at the last neurologist consultation (OR 3.72, 95% CI=1.37-10.50, p=0.009), disability pension as result of MS (OR 3.74, 95% CI=1.10-12.71, p=0.034), relapses by MS≥4 (OR 9.89, 95% CI=3.07-31.86, p<0.001) and cognitive impairment (OR 7.20, 95% CI=2.86-18.14, p<0.001). In the adjusted analysis. The factors associated with SCI were relapses by MS≥4 (OR 7.95, 95% CI=2.19-28.68, p=0.001) and cognitive impairment (OR 5.02, 95% CI=1.79-14.05, p<0.001). Conclusion This study demonstrated that 47% of patients had SCI. After adjustment for potential confounders, ≥4 MS relapses, and cognitive impairment remained associated with SCI.
The collaborative governance and co-creation literature contends that leadership, especially in its facilitative form, is pivotal to attaining common goals. However, facilitative leadership often remains broadly defined, making it difficult to conduct further conceptualization for more systematic insights. This paper develops a strategy-based framework for analyzing facilitative leadership in co-creation, with particular attention to green innovations that support socio-technical transitions. We conceptualize facilitative leadership as a context-sensitive assembly of strategies through which leaders sustain participation, create institutional support, and empower actors to become co-creators.The study draws on 26 in-depth case studies of localized co-creation projects from 21 countries, analyzed through four facilitative leadership strategies—framing, continuous engagement, institution building/maneuvering, and empowerment—and across different task environments. The findings show that continuous engagement and institution building are core strategies across cases, while framing and empowerment are used more selectively. Framing and empowerment are especially important where conditions for co-creation are weak, shared problem understanding is limited, or stakeholders require support to participate meaningfully. Conversely, the paper finds that in cases with favorable institutional and stakeholder conditions, and state-initiated, framing may be less necessary, although engagement and institution building remain central. State initiation in itself should not be equated with strong conditions for co-creation. The paper contributes to collaborative governance, co-creation, and socio-technical transition research by showing that facilitative leadership is not a fixed set of practices but a context-sensitive configuration of strategies.
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.
