Search

The progressive aging of the global population and the high impact of neurodegenerative diseases, such as Alzheimer's disease (AD), underscore the urgent need for innovative diagnostic and therapeutic strategies. AD, the most prevalent neurodegenerative disorder among the elderly, is expected to affect 75 million people in developing countries by 2030. Despite extensive research, the precise etiology of AD remains elusive due to its heterogeneity and complexity. The key pathological features of AD, including amyloid-beta plaques and hyperphosphorylated tau protein, are established years before clinical symptoms appear. Recent studies highlight the pivotal role of neuroinflammation in AD pathogenesis, with the chronic activation of the brain's immune system contributing to the disease's progression. Pro-inflammatory cytokines, such as TNF- α , IL-1 β , and IL-6, are elevated in AD and mild cognitive impairment (MCI) patients, suggesting a strong link between peripheral inflammation and CNS degeneration. There is a pressing need for minimally invasive, cost-effective diagnostic methods. Buccal mucosa cells and saliva, which share an embryological origin with the CNS, show promise for AD diagnosis and prognosis. This study integrates cellular observations with advanced data processing and machine learning to identify significant biomarkers and patterns, aiming to enhance the early diagnosis and prevention strategies for AD. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia in the elderly, which is characterized by progressive cognitive impairment. Herein, we undertake a sophisticated computational analysis by integrating single-cell RNA sequencing (scRNA-seq) data from multiple brain regions significantly affected by the disease, including the entorhinal cortex, prefrontal cortex, superior frontal gyrus, and superior parietal lobe. Our pipeline combines datasets derived from the aforementioned tissues into a unified analysis framework, facilitating cross-regional comparisons to provide a holistic view of the impact of the disease on the cellular and molecular landscape of the brain. We employed advanced computational techniques such as batch effect correction, normalization, dimensionality reduction, clustering, and visualization to explore cellular heterogeneity and gene expression patterns across these regions. Our findings suggest that enabling the integration of data from multiple batches can significantly enhance our understanding of AD complexity, thereby identifying key molecular targets for potential therapeutic intervention. This study established a precedent for future research by demonstrating how existing data can be reanalysed in a coherent manner to elucidate the systemic nature of the disease and inform the development of more effective diagnostic tools and targeted therapies. [ABSTRACT FROM AUTHOR]

Analysis of blood-based indicators of brain health could provide an understanding of early disease mechanisms and pinpoint possible intervention strategies. By examining lipid profiles in extracellular vesicles (EVs), secreted particles from all cells, including astrocytes and neurons, and circulating in clinical samples, important insights regarding the brain's composition can be gained. Herein, a targeted lipidomic analysis was carried out in EVs derived from plasma samples after removal of lipoproteins from individuals with Alzheimer's disease (AD) and healthy controls. Differences were observed for selected lipid species of glycerolipids (GLs), glycerophospholipids (GPLs), lysophospholipids (LPLs) and sphingolipids (SLs) across three distinct EV subpopulations (all-cell origin, derived by immunocapture of CD9, CD81 and CD63; neuronal origin, derived by immunocapture of L1CAM; and astrocytic origin, derived by immunocapture of GLAST). The findings provide new insights into the lipid composition of EVs isolated from plasma samples regarding specific lipid families (MG, DG, Cer, PA, PC, PE, PI, LPI, LPE, LPC), as well as differences between AD and control individuals. This study emphasizes the crucial role of plasma EV lipidomics analysis as a comprehensive approach for identifying biomarkers and biological targets in AD and related disorders, facilitating early diagnosis and potentially informing novel interventions. [ABSTRACT FROM AUTHOR]

The central nervous system (CNS) and the immune system collectively coordinate cellular functionalities, sharing common developmental mechanisms. Immunity-related molecules exert an influence on brain development, challenging the conventional view of the brain as immune-privileged. Chronic inflammation emerges as a key player in the pathophysiology of Alzheimer's disease (AD), with increased stress contributing to the disease progression and potentially exacerbating existing symptoms. In this study, the most significant gene signatures from selected RNA-sequencing (RNA-seq) data from AD patients and healthy individuals were obtained and a functional analysis and biological interpretation was conducted, including network and pathway enrichment analysis. Important evidence was reported, such as enrichment in immune system responses and antigen processes, as well as positive regulation of T-cell mediated cytotoxicity and endogenous and exogenous peptide antigen, thus indicating neuroinflammation and immune response participation in disease progression. These findings suggest a disturbance in the immune infiltration of the peripheral immune environment, providing new challenges to explore key biological processes from a molecular perspective that strongly participate in AD development. [ABSTRACT FROM AUTHOR]

Simple Summary: Current global statistics indicate that approximately 1 in 13 individuals will develop post-traumatic stress disorder (PTSD) at some point in their lives. PTSD is a complex psychological disorder that emerges as a result of exposure to traumatic events, and its intricate nature poses challenges for diagnosis. The focus should be directed towards identifying the catalytic factors of PTSD, which can serve as molecular or functional biomarkers. To address this, an extensive review of research sources to compile a comprehensive list of catalytic factors at both the molecular and functional levels was conducted. A mathematical model encompassing all the significant catalytic factors was proposed, with the future aim of developing a holistic model of PTSD that will incorporate a panel of characteristics. Furthermore, the behavior of these factors at the genetic level, considering both ontological and network perspectives was explored, enabling the gain of a deeper understanding of PTSD by capturing their interconnectivity and biological roles. To the best of our knowledge, this study represents the first comprehensive examination and modeling of catalytic factors in PTSD, taking into account their interconnected associations. Post-traumatic stress disorder (PTSD) is a complex psychological disorder that develops following exposure to traumatic events. PTSD is influenced by catalytic factors such as dysregulated hypothalamic-pituitary-adrenal (HPA) axis, neurotransmitter imbalances, and oxidative stress. Genetic variations may act as important catalysts, impacting neurochemical signaling, synaptic plasticity, and stress response systems. Understanding the intricate gene networks and their interactions is vital for comprehending the underlying mechanisms of PTSD. Focusing on the catalytic factors of PTSD is essential because they provide valuable insights into the underlying mechanisms of the disorder. By understanding these factors and their interplay, researchers may uncover potential targets for interventions and therapies, leading to more effective and personalized treatments for individuals with PTSD. The aforementioned gene networks, composed of specific genes associated with the disorder, provide a comprehensive view of the molecular pathways and regulatory mechanisms involved in PTSD. Through this study valuable insights into the disorder's underlying mechanisms and opening avenues for effective treatments, personalized interventions, and the development of biomarkers for early detection and monitoring are provided. [ABSTRACT FROM AUTHOR]