Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and memory loss. While the precise causes of AD remain unclear, emerging evidence suggests that messenger RNA (mRNA) dysregulation contributes to AD pathology and risk. This study examined exosomal mRNA expression profiles of 15 individuals diagnosed with AD and 15 healthy controls from Barranquilla, Colombia. Utilizing advanced bioinformatics and machine learning (ML) techniques, we identified differentially expressed mRNAs and assessed their predictive power for AD diagnosis and AD age of onset (ADAOO). Our results showed that ENST00000331581 (CADM1) and ENST00000382258 (TNFRSF19) were significantly upregulated in AD patients. Key predictors for AD diagnosis included ENST00000311550 (GABRB3), ENST00000278765 (GGTLC1), ENST00000331581 (CADM1), ENST00000372572 (FOXJ3), and ENST00000636358 (ACY1), achieving > 90% accuracy in both training and testing datasets. For ADAOO, ENST00000340552 (LIMK2) expression correlated with a delay of ~12.6 years, while ENST00000304677 (RNASE6), ENST00000640218 (HNRNPU), ENST00000602017 (PPP5D1), ENST00000224950 (STN1), and ENST00000322088 (PPP2R1A) emerged as the most important predictors. ENST00000304677 (RNASE6) and ENST00000602017 (PPP5D1) showed promising predictive accuracy in unseen data. These findings suggest that mRNA expression profiles may serve as effective biomarkers for AD diagnosis and ADAOO, providing a cost-efficient and minimally invasive tool for early detection and monitoring. Further research is needed to validate these results in larger, diverse cohorts and explore the biological roles of the identified mRNAs in AD pathogenesis.

The Moral Disengagement Scale (MDS) is a widely employed tool for assessing moral neutralization strategies in the context of bullying. However, studies conducted globally exhibit inconsistencies in both the factor structure and the number of items comprising the MDS. This study was conducted to assess the reliability and factor structure of the MDS within a sample of Colombian adolescents ( N = 375; M age = 13.3; SD = 1.69) and to compare the use of MD mechanisms across victims, perpetrators, non-involved students, and bystanders of school bullying. To begin, a confirmatory factor analysis (CFA) compared the fit indices of theoretical models proposed in the existing literature. The results revealed that all previously suggested models exhibited similar fit indices, indicating that these structures could be equally applicable for evaluating various dimensions of moral disengagement among adolescent populations. In order to identify the most suitable model for our culturally specific sample, an exploratory factor analysis (EFA) pointed towards a more concise 13-item version of the MDS as the optimal solution. Individuals meeting the scores for “bullies” employed more MD mechanisms compared to victims and non-involved students. There were no significant differences in MDS scores between pro-victim bystanders and those remaining passive or supporting aggressors. In light of these findings, targeting cognitive mechanisms that neutralize moral self-sanctions in perpetrators and potential bystanders of bullying may prove to be a valuable strategy in enhancing the effectiveness of anti-bullying interventions.

Se buscó analizar las propiedades psicométricas del BSCS en colombianos que residían en Barranquilla - norte de Colombia, teniendo en cuenta la categorización en estratos socioeconómicos de los barrios en la ciudad. Se quería observar si el modelo teórico de cuatro factores se mantenía o no en este contexto, y si la variable estrato socioeconómico de los barrios podía afectar su puntuación. Se encuestó a 858 personas: 491 mujeres, 367 hombres con edades de 18 a 82 años (M =31.2 años, DE= 14.6). Se utilizó un análisis factorial exploratorio y confirmatorio para examinar la estructura de los factores, y la igualdad de los parámetros del BSCS entre los grupos de barrios con alta y baja categorización socioeconómica. Se encontró que, de tres modelos en comparación, el modelo de un factor de segundo orden que explica las cuatro dimensiones del sentido de comunidad (SC), brindó el mejor ajuste a los datos y responde mejor al modelo de McMillan y Chavis (1986). Las medidas de confiabilidad interna fueron ω de .92 y α de .91. Se propone una norma de corrección calculada para la matriz de correlación Policórica, donde las puntuaciones más altas indican un mayor SC. Se termina identificando que el SC es mucho mayor en los barrios de estratos socioeconómicos altos y medios con respecto a los barrios de estratos bajos.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive decline and complex molecular changes. Extracellular vesicles (EVs), particularly exosomes, play a key role in intercellular communication and disease progression, transporting proteins, lipids, and nucleic acids. While altered exosomal mRNA profiles have emerged as potential biomarkers for AD, the relationship between mRNA expression and AD neuropsychological deficits remains unclear. Here, we investigated the correlation between exosomx10-derived mRNA signatures and neuropsychological performance in a cohort from Barranquilla, Colombia. Expression profiles of 16,585 mRNAs in 15 AD patients and 15 healthy controls were analysed using Generalized Linear Models (GLMs) and the Predictive Power Score (PPS). We identified significant correlations between specific mRNA signatures and key neuropsychological variables, including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Functional Assessment Screening Tool (FAST), Boston Naming Test, and Rey–Osterrieth Figure test. These mRNAs were in key AD-associated genes (i.e., GABRB3 and CADM1), while other genes are novel (i.e., SHROOM3, SLC7A2, GJB4, and XBP1). PPS analyses further revealed predictive relationships between mRNA expression and neuropsychological variables, accounting for non-linear patterns and asymmetric associations. If replicated in more extensive and heterogeneous studies, these findings provide critical insights into the molecular basis governing the natural history of AD, potential personalized and non-invasive diagnosis, prognosis, follow-up, and potential targets for future therapies.

Social safety and social security are concepts that help explain a community's well-being by assessing how it manages and mitigate existing, or perceived, risks. However, these terms are often conflated, which can limit understanding, particularly in areas with unstable living conditions. This study explores both concepts in the context of residents living in slums. As the number of displaced people grows, slums and informal settlements are becoming increasingly common worldwide, making it essential to clarify these concepts. Residents of slums face numerous hazards, including crime, violence, inadequate housing, overcrowding, and limited access to essential services. To measure social safety and social security, we developed two survey instruments. These were administered in Khayelitsha Site C, a post-apartheid township in Cape Town that includes both formal and informal settlements. We analyzed data from 514 participants using exploratory and confirmatory factor analyses, which confirmed the multidimensional nature of both concepts. The social safety instrument was refined to include ten items focused on perceptions of environmental risk, crime, and infrastructure, achieving a reliability score of (Ω = 0.90). The social security instrument, refined to eleven items, addresses environmental prevention, community leadership support, crime prevention, and health protection, with a reliability score of (Ω = 0.80). Both instruments demonstrated strong statistical validity through internal consistency, as well as evidence of convergent and discriminant validity. This research provides new, reliable tools for examining the challenges of social safety and social security in slums, offering actionable insights for policymakers and community development efforts.