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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]

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]

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era. [ABSTRACT FROM AUTHOR]

Having their position gained among the smallest bulky DNA lesions recognized by the nucleotide excision repair (NER) enzyme, purine 5′,8-cyclo-2′-deoxynucleosides (5′,8-cPu) are increasingly attracting the interest in the field of genome integrity in health and diseases. Exclusively generated by one of the most harmful of the reactive oxygen species, the hydroxyl radical, 5′,8-cPu can be utilized also for highly valuable information regarding the oxidative status nearby the area where the genetic information is stored. Herein, we have collected the most recently reported biological studies, focusing on the repair mechanism of these lesions and their biological significance particularly in transcription. The LC-MS/MS quantification protocols that appeared in the literature are discussed in details, along with the reported values for the four 5′,8-cPu produced by in vitro γ-radiolysis experiments with calf thymus DNA. Mechanistic insights in the formation of the purine 5′,8-cyclo-2′-deoxynucleosides and their chemical stability are also given in the light of their potential to be utilized as DNA biomarkers of oxidative stress. [ABSTRACT FROM AUTHOR]

Increased rates of reactive oxygen/nitrogen species (ROS/RNS) are involved in almost all cancer types, associated with tumor development and progression, causing damage to biomolecules such as proteins, nucleic acids and membrane lipids, in different biological compartments. We used a human tumor xenograft mouse model to evaluate for the first time in parallel the remodeling of fatty acid moieties in erythrocyte membrane phospholipids and the level of ROS-induced DNA lesions in liver and kidney tissues. Using liquid chromatography tandem mass spectrometry the 5′R and 5′S diastereoisomers of 5′,8-cyclo-2′-deoxyadenosine and 5′,8-cyclo-2′-deoxyguanosine, together with 8-oxo-7,8-dihydro-2′-deoxyadenosine, were determined in mice at young (4- and 5-weeks) and old (17-weeks) ages and compared with control SCID mice without tumor implantation. Tumor-bearing mice showed a higher level of ROS-damaged nucleosides in genomic DNA as the age and tumor progress, compared to controls (1.07–1.53-fold in liver and 1.1–1.4-fold in kidney, respectively). The parallel fatty acid profile of erythrocyte membranes showed a profound lipid remodeling during tumor and age progression consisting of PUFA consumption and SFA enrichment (ca 28% and 58%, respectively, in late stage tumor-bearing mice), markers of enhanced oxidative and proliferative processes, respectively. Membrane lipid remodeling and ROS-induced DNA lesions may be combined to afford an integrated scenario of cancer progression and ageing, reinforcing a holistic vision among molecular markers rather than the biomarker identification in a single compartment. [ABSTRACT FROM AUTHOR]