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Background Endothelial activation and damage is commonly observed in patients with systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) and is related to development of atherosclerosis and cardiovascular diseases. Different components of the immune system seem to participate in the endothelial injury, such as generation of autoantibodies and formation of immune complexes (ICs). Microparticles (MPs) and their immune complexes (MPs-ICs) are increased in the circulation of patients with SLE and RA; therefore, we propose these extracellular vesicles could interact and modulate the function of endothelial cells. Hence, the effect of MPs and MPs-ICs from patients with SLE and RA in endothelial cells was evaluated. Methods Macrovascular and microvascular endothelial cells were exposed to MPs and MPs-ICs from healthy donors and patients with SLE and RA. Vesicles uptake/binding, expression of adhesion molecules, cytokine and chemokine production, monocyte adherence, and alterations of endothelial monolayer were evaluated by flow cytometry and fluorescence microscopy. Results Endothelial cells internalized MPs and MPs-ICs and increased CD54 and CD102 expression and CCL2, CCL5, and IL-6 production after the treatment with these extracellular vesicles, which led to an increase in the adherence of classic monocytes. These vesicles also induced low expression of VE-cadherin in membrane, depolymerization of actin filaments, and formation of intercellular spaces, which led to endothelial death and increased permeability after MPs and MPs-ICs exposure. Conclusions MPs and MPs-ICs from patients with SLE and RA increase adhesion molecules expression, chemokine production, and structural alterations in macrovascular and microvascular endothelial cells. Therefore, high counts of these vesicles in patients would promote endothelial alterations and secondary tissue leukocyte infiltration.

Neurological disorders are prevalent worldwide. Cerebrovascular diseases (CVDs), which account for 55% of all neurological diseases, are the leading cause of permanent disability, cognitive and motor disorders and dementia. Stroke affects the function and structure of blood-brain barrier, the loss of cerebral blood flow regulation, oxidative stress, inflammation and the loss of neural connections. Currently, no gold standard treatments are available outside the acute therapeutic window to improve outcome in stroke patients. Some promising candidate targets have been identified for the improvement of long-term recovery after stroke, such as Rho GTPases, cell adhesion proteins, kinases, and phosphatases. Previous studies by our lab indicated that Rho GTPases (Rac and RhoA) are involved in both tissue damage and survival, as these proteins are essential for the morphology and movement of neurons, astrocytes and endothelial cells, thus playing a critical role in the balance between cell survival and death. Treatment with a pharmacological inhibitor of RhoA/ROCK blocks the activation of the neurodegeneration cascade. In addition, Rac and synaptic adhesion proteins (p120 catenin and N-catenin) play critical roles in protection against cerebral infarction and in recovery by supporting the neurovascular unit and cytoskeletal remodeling activity to maintain the integrity of the brain parenchyma. Interestingly, neuroprotective agents, such as atorvastatin, and CDK5 silencing after cerebral ischemia and in a glutamate-induced excitotoxicity model may act on the same cellular effectors to recover neurovascular unit integrity. Therefore, future efforts must focus on individually targeting the structural and functional roles of each effector of neurovascular unit and the interactions in neural and non-neural cells in the post-ischemic brain and address how to promote the recovery or prevent the loss of homeostasis in the short, medium and long term.

In response to brain insults, astrocytes become reactive, promoting protection and tissue repair. However, astroglial reactivity is typical of brain pathologies, including Alzheimer's disease (AD). Considering the heterogeneity of the reactive response, the role of astrocytes in the course of different forms of AD has been underestimated. Colombia has the largest human group known to have familial AD (FAD). This group carries the autosomal dominant and fully penetrant mutation E280A in PSEN1, which causes early‐onset AD. Recently, our group identified an E280A carrier who did not develop FAD. The individual was homozygous for the Christchurch mutation R136S in APOE3 (APOEch). Remarkably, APOE is the main genetic risk factor for developing sporadic AD (SAD) and most of cerebral ApoE is produced by astroglia. Here, we characterized astrocyte properties related to reactivity, glutamate homeostasis, and structural integrity of the gliovascular unit (GVU), as factors that could underlie the pathogenesis or protection of AD. Specifically, through histological and 3D microscopy analyses of postmortem samples, we briefly describe the histopathology and cytoarchitecture of the frontal cortex of SAD, FAD, and APOEch, and demonstrate that, while astrodegeneration and vascular deterioration are prominent in SAD, FAD is characterized by hyperreactive‐like glia, and APOEch displays the mildest astrocytic and vascular alterations despite having the highest burden of Aβ. Notably, astroglial, gliovascular, and vascular disturbances, as well as brain cell death, correlate with the specific astrocytic phenotypes identified in each condition. This study provides new insights into the potential relevance of the gliovasculature in the development and protection of AD. To our knowledge, this is the first study assessing the components of the GVU in human samples of SAD, FAD, and APOEch. The human frontal cortex of SAD, FAD, and APOEch is characterized by specific astrocyte phenotypes which define the integrity of Gliovascular unit. ApoE3ch mutation in an E280A carrier might be related to the promotion of astrocytic and gliovascular homeostatisis despite the massive load of Aβ. This study provides new insights into the potential relevance of the gliovascular unit in the development and protection of AD.

The PSEN1 mutation causes autosomal dominant Alzheimer's disease (ADAD) with predictable onset, enabling presymptomatic studies. Extracellular vesicles (EVs) are emerging biomarkers of cognitive decline, but their role in early ADAD is unclear. The rare apolipoprotein E (APOE3) Christchurch (APOE3 ) variant delays disease onset, yet its effect on EVs is unknown. We analyzed plasma EVs from mild cognitive impairment (MCI) and non-MCI PSEN1 -APOE3 carriers and non-MCI PSEN1 -APOE3 carriers using flow cytometry, proteomics, and co-culture assays. APOE3 -EVs showed reduced vascular activation and inflammatory cargo linked to β-catenin signaling, higher apoE levels, and enrichment in lipid-loaded EVs. They mimicked the protective effect of recombinant ApoE3Ch on endothelial integrity by restoring β-catenin nuclear localization. In contrast, EVs from non-MCI PSEN1 -APOE3 carriers displayed vascular and inflammatory signatures associated with poorer cognition and detrimental astrocyte-endothelium effects. These findings highlight APOE3 -EVs as modulators of vascular and inflammatory pathways with biomarker and therapeutic potential in ADAD.

The objective of this work is to present a methodology that automates the prediction of students’ academic performance at the end of the course using data recorded in the first tasks of the academic year. Analyzing early student records is helpful in predicting their later results; which is useful, for instance, for an early intervention. With this aim, we propose a methodology based on the random Tukey depth and a non-parametric kernel. This methodology allows teachers and evaluators to define the variables that they consider most appropriate to measure those aspects related to the academic performance of students. The methodology is applied to a real case study obtaining a success rate in the predictions of over the 80%. The case study was carried out in the field of Human-computer Interaction.The results indicate that the methodology could be of special interest to develop software systems that process the data generated by computer-supported learning systems and to warn the teacher of the need to adopt intervention mechanisms when low academic performance is predicted.

CDK5 is a serine/threonine kinase that is involved in the normal function of the adult brain and plays a role in neurotransmission and synaptic plasticity. However, its over-regulation has been associated with Tau hyperphosphorylation and cognitive deficits. Our previous studies have demonstrated that CDK5 targeting using shRNA-miR provides neuroprotection and prevents cognitive deficits. Dendritic spine morphogenesis and forms of long-term synaptic plasticity—such as long-term potentiation (LTP)—have been proposed as essential processes of neuroplasticity. However, whether CDK5 participates in these processes remains controversial and depends on the experimental model. Using wild-type mice that received injections of CDK5 shRNA-miR in CA1 showed an increased LTP and recovered the PPF in deficient LTP of APPswe/PS1Δ9 transgenic mice. On mature hippocampal neurons CDK5, shRNA-miR for 12 days induced increased dendritic protrusion morphogenesis, which was dependent on Rac activity. In addition, silencing of CDK5 increased BDNF expression, temporarily increased phosphorylation of CaMKII, ERK, and CREB; and facilitated calcium signaling in neurites. Together, our data suggest that CDK5 downregulation induces synaptic plasticity in mature neurons involving Ca 2+ signaling and BDNF/CREB activation.

Functional data analysis and artificial neural networks are the building blocks of the proposed methodology that distinguishes the movement patterns among c’s patients on different stages of the disease and classifies new patients to their appropriate stage of the disease. The movement patterns are obtained by the accelerometer device of android smartphones that the patients carry while moving freely. The proposed methodology is relevant in that it is flexible on the type of data to which it is applied. To exemplify that, it is analyzed a novel real three-dimensional functional dataset where each datum is observed in a different time domain. Not only is it observed on a difference frequency but also the domain of each datum has different length. The obtained classification success rate of 83 % indicates the potential of the proposed methodology.

Additive manufacturing (AM) with clay and ceramic-based materials is gaining momentum as a sustainable alternative in construction, yet its advancement depends on bridging experimental practice with predictive modeling. This review synthesizes advances in mathematical formulations and numerical tools applied to clay, geopolymers, alumina, and related extrusion-based pastes. Classical rheological models, including the Bingham and Herschel–Bulkley formulations, remain central for characterizing yield stress, structuration, and flow stability. Meanwhile, finite element (FEM) and computational fluid dynamics (CFD) approaches are increasingly supporting predictions of deformation, shrinkage, drying, and sintering. Despite these advances, their application to natural clay systems remains limited due to heterogeneity, moisture sensitivity, and the lack of standardized constitutive parameters. Recent studies emphasize that validation is essential: rheometry, layer stability tests, in situ monitoring, and prototyping provide necessary calibration for reliable simulation. In parallel, parametric and generative design workflows, particularly through Rhino and Grasshopper ecosystems, illustrate how digital methods can link geometric logic, fabrication constraints, and performance criteria. Overall, the literature demonstrates a transition from isolated modeling efforts toward integrated, iterative frameworks where rheology, numerical simulation, and experimental validation converge to improve predictability, reduce trial-and-error, and advance scalable and sustainable clay- and ceramic-based AM.

A relevant goal in human–computer interaction is to produce applications that are easy to use and well-adjusted to their users’ needs. To address this problem it is important to know how users interact with the system. This work constitutes a methodological contribution capable of identifying the context of use in which users perform interactions with a groupware application (synchronous or asynchronous) and provides, using machine learning techniques, generative models of how users behave. Additionally, these models are transformed into a text that describes in natural language the main characteristics of the interaction of the users with the system.

This research analyzes the influence of hydroxy on pure diesel and blends of palm oil and coffee husk biodiesel with percentages of 15% and 20%. The experimental tests were carried out in a stationary diesel engine, where the torque and speed varied from 3–7 Nm and 3000–3600 rpm. Hydroxy was used as a secondary fuel with a volumetric flow injection of 4 and 8 lpm. The injection of hydroxy can reduce the BSFC and increase the BTE of the engine when running on pure diesel and biodiesel blends. The results show a maximum decrease of 11.66%, 11.28%, and 10.94% in BSFC when hydroxy is injected into D100, D85P10C5, and D80P10C10 fuels. In the case of BTE, maximum increases of 13.37%, 12.84%, and 12.34% were obtained for the above fuels. The fuels D100 + 8 lpm, D85P10C5 + 8 lpm, and D80P10C10 + 8 lpm achieved maximum energy efficiencies of 28.16%, 27.58%, and 27.32%, respectively. In the case of exergy efficiency, maximum values of 26.39%, 25.83%, and 25.58% were obtained. The environmental and social costs of CO, CO2, and HC emissions are significantly reduced with the addition of hydroxy in pure diesel and biodiesel blends from palm oil and coffee husk. The injection of a volumetric flow rate of 8 l/min results in reductions of 11.66%, 10.61%, and 10.94% in operational cost when the engine is fueled with D100, D85P10C5, and D80P10C10, respectively, complying with standards essential for safe engine operation. In general, the research conducted indicates that hydroxy injection is a viable alternative for reducing fuel consumption and improving engine efficiency when using biodiesel blends made from palm oil and coffee husk.