Ana G. Méndez University

This article examines Gaza from a geography-centered position, foregrounding human agency facing oppressive structures. It offers a diagram that depicts two viewpoints-colonized and colonizer-across three interconnected layers, each forming spaces: liberation or illusion in the upper layer, catastrophe or crisis in the middle, and compensation or deviation in the lower. These spaces capture Gaza's complexity, shaped by decades of siege, cyclical war, external oversight, dependencies, and localized governance, while allowing multiperspectivism in one location. Bringing together academic analyses of colonized spaces, key geographic debates on Gaza, with testimonies from Gaza's residents, United Nations Relief and Works Agency representatives, and other stakeholders, the analysis reveals spatio-political complexity arising from overlapping experiences of occupation, resilience, and attempts at external control. By incorporating multiple co-existing temporalities, the framework highlights daily life's relationship to immediate survival and entrenched colonial conditions. Our modest contribution demonstrates how geography can guide a deeper understanding of Gaza's contested reality-one that can ultimately inform a renewed, positive, and peaceful vision for a more equitable future in Gaza and beyond.

Objectives/Goals: To integrate proteomic and metabolomic profiling with genetic literacy and patient experience data to uncover biological mechanisms, community perceptions, and barriers to care in Puerto Ricans affected by Huntington’s disease. Methods/Study Population: We conducted a mixed-methods, multi-omic study combining quantitative proteomic and metabolomic analyses with surveys and interviews exploring genetic literacy and access to care. Participants included patients, first-degree relatives, and controls recruited through PR Huntington Foundation (Fundación Huntington Puerto Rico). Plasma samples were analyzed via mass spectrometry to identify differential protein and metabolite expression associated with CAG repeat length, clinical stage, and symptom burden. Qualitative data captured perceptions of genetics, stigma, and support networks. Results/Anticipated Results: Multi-omic analysis revealed disruptions in energy metabolism, oxidative stress response, and mitochondrial pathways, including altered creatine kinase, apolipoproteins, and kynurenine metabolites. These signatures correlated with CAG repeat length and disease severity. Qualitative findings showed limited genetic literacy, lack of formal counseling, and reliance on community organizations for support. Participants emphasized the need for accessible education and culturally adapted care models. Discussion/Significance of Impact: Integrating omics with community insights highlights both biological and social determinants of disease burden. The findings underscore the significance of equitable precision medicine, culturally grounded education, and community partnerships in enhancing care for underserved Huntington’s disease populations.

Internet gaming disorder (IGD) is an emerging mental health concern characterized by compulsive gaming that disrupts daily functioning. IGD is increasingly understood as rooted in psychosocial, rather than purely behavioral, factors. This cross-sectional study (n = 159, Puerto Rican adolescents) employed convenience sampling through an online survey to examine IGD’s relationship to psychosocial factors (e.g., loneliness, coping, emotional psychopathology). IGD showed minor associations (r < 20) with loneliness (p = .485), and its social (p = .747), romantic (p = .827), and family (p = .315) dimensions. Stronger correlations (r > .40) emerged with anxiety (p < .001), depression (p < .001), stress (p < .001), and maladaptive coping (p < .001). These findings emphasize IGD’s bidirectional ties to emotional psychopathology and its complex relationship with loneliness. Adolescents may use gaming to cope with loneliness and emotional psychopathology, emphasizing the need for prevention efforts that improve social and coping skills.

Antimicrobial resistance (AMR) poses an escalating global health crisis driven by multidrug-resistant ESKAPE pathogens and emerging fungal threats such as Candida auris (C. auris). In response to this urgent need for new therapeutic strategies, antimicrobial peptides (AMPs) represent a mechanistically distinct alternative to conventional antibiotics due to their membrane-targeting mechanisms and a reduced propensity for resistance development; however, clinical translation has been hindered by toxicity, instability and manufacturing constraints. Recent advances in artificial intelligence (AI) are reshaping AMP discovery and optimization. Machine learning (ML), deep learning (DL) and transformer-based protein language models now enable improved prediction of antimicrobial activity, selectivity, protease stability and host toxicity. Generative approaches, including variational autoencoders, diffusion models and reinforcement learning, facilitate de novo multi-objective peptide design and pathogen-directed optimization against resistant bacteria and multidrug-resistant fungal pathogens. Integrated design-test-learn pipelines are accelerating iterative peptide engineering by tightly coupling computational prediction with experimental validation. Clinically used peptide-derived antibiotics such as polymyxins and daptomycin demonstrate the therapeutic feasibility of peptide-based antimicrobials, while investigational peptides, including pexiganan, illustrate ongoing translational progress. Although no fully AI-designed AMP has yet achieved regulatory approval, the accelerating convergence of computational modeling and experimental validation suggests a rapidly evolving translational landscape. Advancing scalable, surveillance-informed AI frameworks that integrate resistance data, predictive safety modeling and delivery optimization will be essential to accelerate the clinical translation of next-generation, multi-objective AMPs against high-risk resistant pathogens.

This opinion piece critically examines the integration of artificial intelligence (AI) into education, arguing that it must be guided by clearly articulated educational purposes rather than by technological enthusiasm alone. Drawing on my experience as a practitioner–researcher, I highlight the persistent gap between policy narratives of modernisation and the realities of classroom practice, where teachers confront new pressures of workload, identity, and accountability. I propose a critical differentiation between AI literacy, the essential knowledge for students and teachers about what AI is, how it works, and its ethical implications, and AI in education, the integration of AI systems into teaching, learning, and leadership. Conflating these two complementary domains, I argue, risks misdirecting policy and practice. Drawing on the broader ‘alignment problem’ in AI ethics, I contend that education, as a purpose-driven field, requires particular vigilance: while how AI is integrated matters, the fundamental question remains why it is integrated. I illustrate this argument through implications for learners’ cognitive development, teacher workload, and the risks of automation bias and digital colonialism. The article concludes by suggesting principles for purposeful AI integration, indicative recommendations for educators and policymakers, and a research agenda to explore potential long-term consequences.