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Paraneoplastic nephropathy is rarely associated with human tumors. Little is known about the pathogenetic background of this relationship. To our knowledge, no conclusive study of the association of potentially ‘immunogenic’ renal cell carcinoma (RCC) and paraneoplastic nephropathy has been published. For this reason, we performed an immunohistochemical analysis of native resected kidneys of 60 patients with RCC, paying special attention to their pre- and postoperative records. Sixteen (27%) of the 60 tumor patients had immune complex nephropathy (11 IgA nephropathy [IgA NP] and 5 focal segmental glomerulosclerosis [FSGS]). Preoperative proteinuria and/or hematuria observed in 11 of 16 cases disappeared in 6 IgA NP patients within a 2- to 3-month follow-up after nephrectomy. Eleven of 16 tumors stained with the anti human immunoglobulin (IgA or IgM) of the same isotype as that present in glomerular immune complexes. In 3 IgA NP patients RCC-associated von Hippel-Lindau (VHL) protein and IgA staining were found simultaneously in the tumor and glomeruli, with the clinical and laboratory findings disappearing after nephrectomy. Immune injury of the glomeruli due to a tumor-induced antigen-antibody response was demonstrated in these 3 IgA NP patients.

Background: Renal function is a major predictor of vascular function and cardiovascular diseases. Little information exists about the effect of specific renal diseases on vascular function in chronic kidney diseases (CKD). Methods: One hundred and twenty patients (60 with IgA nephropathy, IgAN, and 60 with polycystic kidney disease, PKD) with CKD stages 1–4 were studied and compared. Pulse-wave velocity was measured by the digital volume pulse (DVP) method and stiffness index (SI_DVP) was derived. Results: All CKD (IgAN and PKD) patients had increased SI_DVP compared to controls (10.39 vs. 8.87 ± 1.79 m/s, p = 0.008). PKD patients had increased SI_DVP compared to IgAN and controls (11.14 ± 2.19, 9.66 ± 2.02 and 8.87 ± 1.79 m/s, respectively, p < 0.001). An inverse correlation was found between SI_DVP and glomerular filtration rate in all CKD (IgAN and PKD) patients (p = 0.001) and in IgAN alone (p < 0.01), but not in PKD. With multivariate regression analysis, only age and 24-hour systolic blood pressure exerted independent effects on SI_DVP. Conclusions: Compared to controls, arterial stiffness was increased in CKD patients. However, arterial stiffening was more pronounced in PKD than in IgAN, suggesting that vascular function is not similarly altered in etiologically different CKD groups. The fact that blood pressure was an independent risk factor underscores a therapeutic opportunity.

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Nowadays, the solution to many practical problems relies on machine learning tools. However, compiling the appropriate training data set for real-world classification problems is challenging because collecting the right amount of data for each class is often difficult or even impossible. In such cases, we can easily face the problem of imbalanced learning. There are many methods in the literature for solving the imbalanced learning problem, so it has become a serious question how to compare the performance of the imbalanced learning methods. Inadequate validation techniques can provide misleading results (e.g., due to data shift), which leads to the development of methods designed for imbalanced data sets, such as stratified cross-validation (SCV) and distribution optimally balanced SCV (DOB-SCV). Previous studies have shown that higher classification performance scores (AUC) can be achieved on imbalanced data sets using DOB-SCV instead of SCV. We investigated the effect of the oversamplers on this difference. The study was conducted on 420 data sets, involving several sampling methods and the DTree, kNN, SVM, and MLP classifiers. We point out that DOB-SCV often provides a little higher F1 and AUC values for classification combined with sampling. However, the results also prove that the selection of the sampler–classifier pair is more important for the classification performance than the choice between the DOB-SCV and the SCV techniques.