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Abstract Systemic lupus erythematosus (SLE) is a multifactorial disease characterized by the convergence of genetic, immunological, and viral elements resulting in a complex interaction of both internal and external factors. The role of the Epstein-Barr virus (EBV) and human endogenous retroviruses (HERV-E) as triggers and maintenance elements in the pathogenesis of SLE has been widely recognized. Previous studies have independently evaluated the effects of EBV and HERV-E in this disease. In this work, for the first time, these viral factors are jointly investigated in SLE patients. This study aimed at assessing the differential expression of immune regulatory genes and the incidence of specific viral pathogens (EBV and HERV-E), alongside the detailed characterization of surface markers in T- and B-lymphocytes in patients with SLE and control participants. A comparative analysis between patients with SLE and control participants was performed, evaluating the expression of phenotypic markers and genes involved in the immune response (TNF-α, IL-2, IL-6, IL-10, IFNG, TLR3), as well as HERV-E gag and EBV viral genes (LMP1 and BZLF1).A significant association between SLE and EBV was found in this study. A notable increase in EBV LMP1 gene expression was observed in patients with SLE . Also, a significant overexpression of HERV-E was observed, in addition to a considerable increase in the distribution of the cell surface marker CD27 + on T- and B-lymphocytes, observed in individuals with SLE compared to the control group. This study provides evidence regarding the role that EBV virus plays in lymphocytes in the context of SLE, highlighting how both the virus and the host gene expression may influence disease pathogenesis by altering immune regulatory pathways mediated by TNF-α, IFN-γ, and IL-10, as well as parallel overexpression of HERV-E gag. The decrease in TLR3 could indicate a compromised antiviral response, which could facilitate viral reactivation and contribute to disease activity.

Abstract Arterial hypertension (AH) is a multifactorial and asymptomatic disease that affects vital organs such as the kidneys and heart. Considering its prevalence and the associated severe health repercussions, hypertension has become a disease of great relevance for public health across the globe. Conventionally, the classification of an individual as hypertensive or non-hypertensive is conducted through ambulatory blood pressure monitoring over a 24-h period. Although this method provides a reliable diagnosis, it has notable limitations, such as additional costs, intolerance experienced by some patients, and interferences derived from physical activities. Moreover, some patients with significant renal impairment may not present proteinuria. Accordingly, alternative methodologies are applied for the classification of individuals as hypertensive or non-hypertensive, such as the detection of metabolites in urine samples through liquid chromatography or mass spectrometry. However, the high cost of these techniques limits their applicability for clinical use. Consequently, an alternative methodology was developed for the detection of molecular patterns in urine collected from hypertension patients. This study generated a direct discrimination model for hypertensive and non-hypertensive individuals through the amplification of Raman signals in urine samples based on gold nanoparticles and supported by chemometric techniques such as partial least squares-discriminant analysis (PLS-DA). Specifically, 162 patient urine samples were used to create a PLS-DA model. These samples included 87 urine samples from patients diagnosed with hypertension and 75 samples from non-hypertensive volunteers. In the AH group, 35 patients were diagnosed with kidney damage and were further classified into a subgroup termed (RAH). The PLS-DA model with 4 latent variables (LV) was used to classify the hypertensive patients with external validation prediction (P) sensitivity of 86.4%, P specificity of 77.8%, and P accuracy of 82.5%. This study demonstrates the ability of surface-enhanced Raman spectroscopy to differentiate between hypertensive and non-hypertensive patients through urine samples, representing a significant advance in the detection and management of AH. Additionally, the same model was then used to discriminate only patients diagnosed with renal damage and controls with a P sensitivity of 100%, P specificity of 77.8%, and P accuracy of 82.5%.