Ana Gabriela Esparza Redrobán

Early childhood represents a critical period for neurocognitive development, during which biological systems exhibit heightened plasticity and vulnerability to environmental influences. In recent years, increasing evidence has highlighted the gut microbiota as a key modulator of brain development through the microbiota–gut–brain axis. This review aims to synthesize current scientific evidence examining the relationship between early-life gut dysbiosis and neurocognitive development in children. A narrative review methodology grounded in the scientific method was employed to analyze experimental and human studies addressing microbial colonization patterns, biological mechanisms, and neurodevelopmental outcomes during infancy and early childhood. The findings indicate that early-life gut dysbiosis is consistently associated with alterations in cognitive performance, behavioral regulation, and stress responsivity, particularly when microbial disruption occurs during sensitive developmental windows. Immune modulation, microbial metabolite signaling, and hypothalamic–pituitary–adrenal axis regulation emerge as central mechanisms linking gut microbiota alterations to neurodevelopmental trajectories. Clinical and environmental factors such as antibiotic exposure, nutrition, prematurity, and socioeconomic context play a significant role in shaping early microbial ecosystems and related neurocognitive outcomes. Although the current evidence primarily supports associative relationships, the convergence of mechanistic and developmental data underscores the biological plausibility and clinical relevance of early-life gut dysbiosis in pediatric neurodevelopment. These findings highlight the importance of microbiome-informed perspectives in pediatric research and support future longitudinal and interdisciplinary studies aimed at optimizing neurocognitive outcomes in children.

Borderline Personality Disorder (BPD) is a complex psychiatric condition characterized by emotional dysregulation, impulsivity, and heightened sensitivity to stress. In recent years, growing attention has been directed toward the role of neuroinflammation as a biological process potentially contributing to the pathophysiology of BPD. This narrative review synthesizes current evidence on immune and inflammatory mechanisms associated with BPD, with particular emphasis on their conceptual and biological overlap with neuroinflammatory pathways described in Alzheimer’s disease. Findings from empirical studies, systematic reviews, and neuroimaging research indicate that cytokine dysregulation, microglial activation, and central–peripheral immune interactions are recurrent features across psychiatric disorders and are also evident in BPD. The integration of Alzheimer’s disease as a reference model provides a framework for understanding how sustained neuroinflammation may influence neural connectivity, stress responsivity, and potential cognitive vulnerability without implying neurodegenerative progression. The review further discusses the implications of immune modulation as a complementary perspective within current treatment models, while emphasizing the continued centrality of psychotherapeutic interventions. By situating BPD within a broader neuroimmune framework, this work contributes to an integrative understanding of psychiatric disorders and supports future research and educational approaches that bridge psychiatry, neuroscience, and immunology.

Clinically significant drug–drug interactions (DDIs) remain a major source of preventable harm in modern pharmacotherapy, particularly in patients exposed to polypharmacy and complex medication regimens. Traditional rule-based clinical decision support systems have contributed to improved medication safety but are frequently limited by alert fatigue, inadequate prioritization, and limited support for actionable clinical decisions. In recent years, deprescribing has emerged as a structured, patient-centered strategy to reduce medication burden and optimize therapeutic outcomes. This review examines the convergence of deprescribing principles with algorithm-driven decision support, a concept referred to as smart deprescribing, aimed at proactively reducing clinically significant DDIs. Through an integrative analysis of international evidence, this study synthesizes findings on rule-based systems, machine learning, and deep learning approaches, highlighting their respective strengths, limitations, and roles within deprescribing-oriented frameworks. The results indicate that algorithm-assisted deprescribing can improve prioritization of high-risk interactions, reduce low-value alerts, and support medication optimization when grounded in robust clinical frameworks. The review further explores implementation pathways across diverse healthcare contexts, including middle-income settings, emphasizing the importance of scalability, interpretability, and workflow integration. Overall, smart deprescribing represents a promising evolution in medication safety that complements clinical judgment while supporting rational prescribing and medical education.