The astrocyte population, around 50% of human brain cells, plays a crucial role in maintaining the overall health and functionality of the central nervous system (CNS). Astrocytes are vital in orchestrating neuronal development by releasing synaptogenic molecules and eliminating excessive synapses. They also modulate neuronal excitability and contribute to CNS homeostasis, promoting neuronal survival by clearance of neurotransmitters, transporting metabolites, and secreting trophic factors. Astrocytes are highly heterogeneous and respond to CNS injuries and diseases through a process known as reactive astrogliosis, which can contribute to both inflammation and its resolution. Recent evidence has revealed remarkable alterations in astrocyte transcriptomes in response to several diseases, identifying at least two distinct phenotypes called A1 or neurotoxic and A2 or neuroprotective astrocytes. However, due to the vast heterogeneity of these cells, it is limited to classify them into only two phenotypes. This review explores the various physiological and pathophysiological roles, potential markers, and pathways that might be activated in different astrocytic phenotypes. Furthermore, we discuss the astrocyte heterogeneity in the main neurodegenerative diseases and identify potential therapeutic strategies. Understanding the underlying mechanisms in the differentiation and imbalance of the astrocytic population will allow the identification of specific biomarkers and timely therapeutic approaches in various neurodegenerative diseases.

Alzheimer’s disease (AD) is the main neurodegenerative disorder characterized by several pathophysiological features, including the misfolding of the tau protein and the amyloid beta (Aβ) peptide, neuroinflammation, oxidative stress, synaptic dysfunction, metabolic alterations, and cognitive impairment. These mechanisms collectively contribute to neurodegeneration, necessitating the exploration of therapeutic approaches with multiple targets. Physical exercise has emerged as a promising non-pharmacological intervention for AD, with demonstrated effects on promoting neurogenesis, activating neurotrophic factors, reducing Aβ aggregates, minimizing the formation of neurofibrillary tangles (NFTs), dampening inflammatory processes, mitigating oxidative stress, and improving the functionality of the neurovascular unit (NVU). Overall, the neuroprotective effects of exercise are not singular, but are multi-targets. Numerous studies have investigated physical exercise’s potential in both AD patients and animal models, employing various exercise protocols to elucidate the underlying neurobiological mechanisms and effects. The objective of this review is to analyze the neurological therapeutic effects of these exercise protocols in animal models and compare them with studies conducted in AD patients. By translating findings from different approaches, this review aims to identify opportune, specific, and personalized therapeutic windows, thus advancing research on the use of physical exercise with AD patients.

Alzheimer’s disease (AD) is the most common neurodegenerative disorder worldwide. Histopathologically, AD presents two pathognomonic hallmarks: (1) neurofibrillary tangles, characterized by intracellular deposits of hyperphosphorylated tau protein, and (2) extracellular amyloid deposits (amyloid plaques) in the brain vasculature (cerebral amyloid angiopathy; CAA). It has been proposed that vascular amyloid deposits could trigger neurovascular unit (NVU) dysfunction in AD. The NVU is composed primarily of astrocytic feet, endothelial cells, pericytes, and basement membrane. Although physical exercise is hypothesized to have beneficial effects against AD, it is unknown whether its positive effects extend to ameliorating CAA and improving the physiology of the NVU. We used the triple transgenic animal model for AD (3xTg-AD) at 13 months old and analyzed through behavioral and histological assays, the effect of voluntary physical exercise on cognitive functions, amyloid angiopathy, and the NVU. Our results show that 3xTg-AD mice develop vascular amyloid deposits which correlate with cognitive deficits and NVU alteration. Interestingly, the physical exercise regimen decreases amyloid angiopathy and correlates with an improvement in cognitive function as well as in the underlying integrity of the NVU components. Physical exercise could represent a key therapeutic approach in cerebral amyloid angiopathy and NVU stability in AD patients.

Can ethical dilemmas be resolved through computational models? In this paper I propose a novel integration of the Drift Diffusion Model (DDM) with a value-based framework for moral reasoning. Based on insights from neuroethics and cognitive neuroscience, I argue that moral decisions —especially under conditions of uncertainty, irreversibility, and emotional interference— can be modeled as processes of noisy evidence accumulation. I introduce the concept of an ethical balance mainly composed of three evaluative variables: sentience, intentionality, and innocence. These inputs are mapped to DDM parameters such as drift rate, decision threshold, and starting point bias. Through illustrative moral scenarios, I show how this framework can both predict and simulate moral judgments. The goal is not to replace normative ethics, but to demonstrate how scientific modeling can enhance our understanding of how moral reasoning unfolds in the brain. This maybe opens the path to a computational ethics grounded in real cognitive processes.

Las teorías de la cognición de doble proceso, popularizadas por el psicólogo y ganador del premio Nobel en Economía Daniel Kahneman y su colaborador Amos Tvesrky, describen la toma de decisiones como la interacción entre un sistema rápido e intuitivo (sistema uno) y un sistema lento y deliberativo (sistema dos). A pesar de las grandes descripciones cualitativas, una simple formalización matemática de su interacción sigue siendo difícil de alcanzar. Por lo tanto, en este artículo, propongo un modelo algebraico novedoso que captura la sinergia no lineal entre estos dos sistemas a través de una identidad fundamental que involucra la diferencia de cuadrados centrada en su activación promedio. Esta formulación cuantifica el equilibrio cognitivo y la integración, mostrando que la toma de decisiones óptima surge de la participación estrecha y simétrica de ambos sistemas. El modelo ofrece un marco lento pero poderoso para simular la dinámica cognitiva, explicar las variaciones en la calidad de las decisiones y explorar los sesgos que surgen del dominio o la fatiga del sistema. Finalmente, discuto las posibles aplicaciones en neurociencia e inteligencia artificial, destacando la capacidad del modelo para unir la teoría cualitativa con la predicción cuantitativa.

El personalismo es una doctrina cuya tesis central es el valor intrínseco de la persona humana, sin embargo, las más recientes aplicaciones e implicaciones de la inteligencia artificial representan un desafío al personalismo. Pese al gran éxito de la inteligencia artificial en diversos ámbitos de la vida humana, tres de las amenazas detectadas son: la despersonalización, la abdicación intelectual y la delegación de la responsabilidad moral. La caja negra que representa el Deep learning, o esta pereza intelectual de la que adolecen las nuevas generaciones al realizar todas las tareas cognitivas con ayuda de dispositivos inteligentes, son un reflejo de que las ciencias de la computación y sus aplicaciones, lejos de ser eliminadas o suprimidas, deberían ser dominadas y enseñadas. Así como una pronta educación en el tema a las generaciones venideras, parece ser de urgencia. Con el diálogo se dará el entendimiento, y sólo la persona humana, podrá presentar una solución que haga sinergia entre esta doctrina y la tecnología.