Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by widespread immune dysregulation and the production of autoantibodies targeting nuclear, cytoplasmic, and cell surface antigens. These autoantibodies are central to disease pathogenesis, contribute to immune complex formation and organ damage, and serve as essential diagnostic and prognostic markers. Their detection supports disease classification, guides clinical decision-making, and offers insight into disease activity and therapeutic response. Traditional markers such as anti-nuclear antibodies (ANA), anti-dsDNA, and anti-Sm antibodies remain diagnostic cornerstones, but growing attention is given to anti-C1q, anti-nucleosome antibodies (ANuA), anti-ribosomal P, antiphospholipid, and anti-cytokine antibodies due to their associations with specific disease phenotypes and activity. These markers may reflect disease activity, specific organ involvement, or predict flares. The mechanisms underlying their persistence include B cell tolerance failure and long-lived plasma cell activity. The aim of this review is to summarize current knowledge on the major autoantibodies in SLE, appraise available detection methods, highlight their clinical utility and limitations and present evidence on the association between antibodies and disease phenotypes.

Diabetic nephropathy (DN) is a leading cause of end-stage renal disease (ESRD) globally. Beyond metabolic and haemodynamic stress, the complement system has emerged as a contributor to glomerular and tubulointerstitial injury. In type 1 diabetes mellitus (T1DM), complement proteins contribute through autoimmune mechanisms, while in type 2 diabetes mellitus (T2DM) they are linked to insulin resistance. In both, complement activation promotes micro- and macrovascular complications through inflammatory pathways that accelerate DN progression. This review summarises the current evidence on the role of complement activation in diabetic nephropathy (DN). First, we outline the mechanisms by which the complement system is activated through the lectin pathway (in which mannoses bind to modified glycosylation structures), the classical pathway (in which C1q recognises immune complexes/damaged self), and the alternative pathway (in which C3 ticks over and amplifies on damaged renal surfaces). Next, we consider the roles of their effector molecules (C3a, C5a, and C5b-9/MAC), and the consequences of regulatory dysfunction (e.g., CD59 dysfunction). When integrated with findings from renal histology, blood and urine biomarkers enable us to evaluate the correlation between prognosis, disease severity, and progression. We will also discuss therapeutic implications, including the rationale behind selective complement inhibition and future intervention strategies.

Background/Objectives: The purpose of this study was to evaluate numerical changes in immune cells after successful kidney transplantation and associate their recovery with clinical and laboratory factors. Methods: In 112 kidney transplant recipients, we performed flow cytometry to evaluate counts of CD4+, CD8+, and regulatory T cells (Tregs), as well as natural killer (NK) cells, before kidney transplantation (T0) and three (T3), six (T6), and twelve (T12) months later. The results were associated with the recipient’s age, cold ischemia time (CIT), the type of donor, dialysis method and vintage, and graft function in one year. Results: Total and CD8+ T cell counts increased gradually one year post transplantation in comparison with pre-transplantation levels, whereas the number of CD4+ T cells and Tregs increased, and the number of NK cells decreased in the first three months and remained stable thereafter. The recipient’s age was negatively correlated with total, CD4+, and Treg counts at T12, whereas CIT affected only total and CD4+ T cell count. Moreover, recipients receiving kidneys from living donors presented better recovery of all T cell subsets at T12 in comparison with recipients receiving kidneys from cadaveric donors. Patients on peritoneal dialysis had increased numbers of total and CD8+ T cells, as well as NK cells. Finally, estimated glomerular filtration rate was positively correlated with Treg level and potentially CD4+ T cells one-year post transplantation. Conclusions: Successful kidney transplantation results in the recovery of most T cell subsets. Lower recipient age and better graft function contribute to increased T cell counts, whereas donor type and dialysis modality are the most important modifiable factors for optimal immune recovery.

Background: B cells have a significant role in transplantation. We examined the distribution of memory subpopulations (MBCs) and naïve B cell (NBCs) phenotypes in patients soon after kidney transplantation. Unsupervised machine learning cluster analysis is used to determine the association between the cellular phenotypes and renal function. Methods: MBC subpopulations and NBCs from 47 stable renal transplant recipients were characterized by flow cytometry just before (T0) and 6 months after (T6) transplantation. T0 and T6 measurements were compared, and clusters of patients with similar cellular phenotypic profiles at T6 were identified. Two clusters, clusters 1 and 2, were formed, and the glomerular filtration rate was estimated (eGFR) for these clusters. Results: A significant increase in NBC frequency was observed between T0 and T6, with no statistically significant differences in the MBC subpopulations. Cluster 1 was characterized by a predominance of the NBC phenotype with a lower frequency of MBCs, whereas cluster 2 was characterized by a high frequency of MBCs and a lower frequency of NBCs. With regard to eGFR, cluster 1 showed a higher value compared to cluster 2. Conclusions: Transplanted kidney patients can be stratified into clusters based on the combination of heterogeneity of MBC phenotype, NBCs and eGFR using unsupervised machine learning.

Rheumatoid arthritis (RA) is a well-known autoimmune inflammatory disease that affects the diarthrodial joints. Inflammation increases the production of reactive oxygen species (ROS), which may explain why RA is one of the diseases that induce oxidative stress. This study aimed to evaluate the potential differences in biochemical, hematological, and oxidative stress markers in the early stages of RA and after different treatment regimens. The study involved 111 patients, 28 men and 83 women aged 34 to 59 years, who were divided based on their c-reactive protein (CRP) levels into inactive RA patients (IRA) with CRP < 1.3 (n = 57, 22 men and 35 women) and active RA patients (ARA) with CRP ≥ 1.3 (n = 54, 6 men and 48 women). The study participants were divided into two groups, A and B, based on their treatment regimen. Group A, 90% of which were IRA patients, received methotrexate (MTX) monotherapy. Group B, which comprised 90% ARA patients, received a combination of leflunomide, a conventional disease-modifying antirheumatic drug (DMARD), and a biologic DMARD. The hematological, biochemical, oxidative stress, and RA-specific biomarkers were measured twice in groups A and B in the early stage of the disease, before and 3 months post-treatment, using conventional colorimetric, fluorometric, and immunological assays. According to the results of our study, glutathione peroxidase (GPx), ROS, calcium (Ca) and phosphorus (P) ions, vitamin C and D, and lipid profiles could serve as potential diagnostic markers in the early stages of the disease. Both treatment options were equally effective at improving the overall health of the patients. However, treatment resulted in a further increase in ROS levels and a decrease in antioxidant markers.

Background: B and T regulatory cells, also known as Bregs and Tregs, are involved in kidney transplantation. The purpose of this study is to monitor changes in the frequency and absolute numbers of Tregs (CD3+CD4+CD25+FoxP3+), transitional Bregs (tBregs) (CD24++CD38++), memory Bregs (mBregs) (CD24++CD27+), and plasmablasts before (T0) and six months (T6) after transplantation. Additionally, we aim to investigate any correlation between Tregs and tBregs, mBregs, or plasmablasts and their relationship with graft function. Methods: Flow cytometry was used to immunophenotype cells from 50 kidney recipients who did not experience rejection. Renal function was assessed using the estimated glomerular filtration rate (eGFR). Results: At T6, there was a significant decrease in the frequency of Tregs, plasmablasts, and tBregs, as well as in the absolute number of tBregs. The frequency of mBregs, however, remained unchanged. Graft function was found to have a positive correlation with the frequency of tBregs and plasmablasts. A significant correlation was observed between the frequency and absolute number of tBregs only when the eGFR was greater than 60 but not at lower values. At an eGFR greater than 60, there was a positive correlation between the absolute numbers of Tregs and mBregs but not between Tregs and tBregs. No correlation was observed for any cell population in dialysis patients. Conclusions: The data show a correlation between the frequency and absolute number of tBregs and the absolute number of Tregs and mBregs with good renal function in the early post-transplant period.