Exosomes are extracellular vesicles released by cells, both constitutively and after cell activation, and are present in different types of biological fluid. Exosomes are involved in the pathogenesis of diseases, such as cancer, neurodegenerative diseases, pregnancy disorders and cardiovascular diseases, and have emerged as potential non-invasive biomarkers for the detection, prognosis and therapeutics of a myriad of diseases. In this review, we describe recent advances related to the regulatory mechanisms of exosome biogenesis, release and molecular composition, as well as their role in health and disease, and their potential use as disease biomarkers and therapeutic targets. In addition, the advantages and disadvantages of their main isolation methods, characterization and cargo analysis, as well as the experimental methods used for exosome-mediated drug delivery, are discussed. Finally, we present potential perspectives for the use of exosomes in future clinical practice.

Background: Numerous studies have evaluated the effect that mindfulness-based interventions (MBIs) have on multiple health outcomes. For its part, stress is a natural response to environmental disturbances and within the associated metabolic responses, alterations in cortisol levels and their measurement in different tissues are a way to determine the stress state of an individual. Therefore, it has been proposed that MBIs can modify cortisol levels. Methods and results: The objective of this systematic review was to analyze and summarize the different studies that have evaluated the effect of MBIs on cortisol levels. The following databases were consulted: MEDLINE, AMED, CINAHL, Web of Science, Science Direct, PsycINFO, SocINDEX, PubMed, the Cochrane Library and Scopus. The search terms “mindfulness”, “mindfulness-based interventions” and “cortisol” were used (and the search was limited to studies from January 1990 to May 2024). In order to reduce selection bias, each article was scrutinized using the JBI Critical Appraisal Checklist independently by two authors. We included those studies with specified intervention groups with at least one control group and excluded duplicate studies or those in which the intervention or control group was not adequately specified. Significant changes in cortisol following MBIs were found in 25 studies, while 10 found no changes. The small sample size, lack of randomization, blinding, and probable confounding and interaction variables stand out in these studies. Conclusion: MBIs have biological plausibility as a means of explaining a positive effect on cortisol levels; however, the weakness of the studies and the absence of robust designs makes it difficult to establish a causal association between both variables. Registration number: INPLASY2024110017.

Objective: Teleneuropsychology (teleNP) could potentially expand access to services for patients who are confined, have limited personal access to healthcare, or live in remote areas. The emergence of the COVID-19 pandemic has significantly increased the use of teleNP for cognitive assessments. The main objective of these recommendations is to identify which procedures can be potentially best adapted to the practice of teleNP in Latin America, and thereby facilitate professional decision-making in the region. Method: Steps taken to develop these recommendations included (1) formation of an international working group with representatives from 12 Latin American countries; (2) assessment of rationale, scope, and objectives; (3) formulation of clinical questions; (4) evidence search and selection; (5) evaluation of existing evidence and summary; and (6) formulation of recommendations. Levels of evidence were graded following the Oxford Centre for Evidence-Based Medicine system. Databases examined included PubMed, WHO-IRIS, WHO and PAHO-IRIS, Índice Bibliográfico Español en Ciencias de la Salud (IBCS), and LILACS. Results: Working group members reviewed 18,400 titles and 422 abstracts and identified 19 articles meeting the criteria for level of evidence, categorization, and elaboration of recommendations. The vast majority of the literature included teleNP tests in the English language. The working group proposed a series of recommendations that can be potentially best adapted to the practice of teleNP in Latin America. Conclusions: There is currently sufficient evidence to support the use of videoconferencing technology for remote neuropsychological assessments. These recommendations will likely contribute to the advancement of teleNP research and practice in the region.

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.

Children with attention-deficit/hyperactivity disorder (ADHD) present a deficit in inhibitory control. Still, it remains unclear whether it comes from a deficit in reactive inhibition (ability to stop the action in progress), proactive inhibition (ability to exert preparatory control), or both. We compared the performance of 39 children with ADHD and 42 typically developing children performing a Simon choice reaction time task. The Simon task is a conflict task that is well-adapted to dissociate proactive and reactive inhibition. Beyond classical global measures (mean reaction time, accuracy rate, and interference effect), we used more sophisticated dynamic analyses of the interference effect and accuracy rate to investigate reactive inhibition. We studied proactive inhibition through the congruency sequence effect (CSE). Our results showed that children with ADHD had impaired reactive but not proactive inhibition. Moreover, the deficit found in reactive inhibition seems to be due to both a stronger impulse capture and more difficulties in inhibiting impulsive responses. These findings contribute to a better understanding of how ADHD affects inhibitory control in children.

Alzheimer’s disease (AD), a neurodegenerative disorder characterized by progressive cognitive decline, is the most common form of dementia. Currently, there is no single test that can diagnose AD, especially in understudied populations and developing countries. Instead, diagnosis is based on a combination of medical history, physical examination, cognitive testing, and brain imaging. Exosomes are extracellular nanovesicles, primarily composed of RNA, that participate in physiological processes related to AD pathogenesis such as cell proliferation, immune response, and neuronal and cardiovascular function. However, the identification and understanding of the potential role of long non-coding RNAs (lncRNAs) in AD diagnosis remain largely unexplored. Here, we clinically, cognitively, and genetically characterized a sample of 15 individuals diagnosed with AD (cases) and 15 controls from Barranquilla, Colombia. Advanced bioinformatics, analytics and Machine Learning (ML) techniques were used to identify lncRNAs differentially expressed between cases and controls. The expression of 28,909 lncRNAs was quantified. Of these, 18 were found to be differentially expressed and harbored in pivotal genes related to AD. Two lncRNAs, ENST00000608936 and ENST00000433747, show promise as diagnostic markers for AD, with ML models achieving > 95% sensitivity, specificity, and accuracy in both the training and testing datasets. These findings suggest that the expression profiles of lncRNAs could significantly contribute to advancing personalized AD diagnosis in this community, offering promising avenues for early detection and follow-up.