Background:The demand for reconstructive breast procedures of various types has accelerated in recent years. Coupled with increased patient expectations, it has fostered the development of oncoplastic and reconstructive techniques in breast surgery. In the setting of postmastectomy reconstruction, patient satisfaction and quality of life are the most significant outcome variables when evaluating surgical success. The aim of this study was to evaluate the quality of life after implant breast reconstruction compared with autologous breast reconstruction.Materials and Methods:A cross-sectional study design was used. A total of 65 women who had completed postmastectomy implant-based or autologous reconstruction in the participating center were asked to complete the BREAST-Q (Reconstruction Module).Results:Data analysis demonstrated that women with autologous breast reconstruction were significantly more satisfied with their breasts (P = 0.0003) and with the overall outcome (P = 0.0001) compared with women with implant breast reconstruction. All other BREAST-Q parameters that were considered and observed were not significantly different between the 2 patient groups.Conclusions:Through statistical analysis, our results showed that patients who underwent autologous tissue reconstruction had better satisfaction with the reconstructed breast and the outcome, while both techniques appear to equally improve psychosocial well-being, sexual well-being, and chest satisfaction.

BackgroundAdditional urinary biomarkers for diabetic nephropathy (DN) are needed, providing early and reliable diagnosis and new insights into its mechanisms. Rigorous selection criteria and homogeneous study population may improve reproducibility of the proteomic approach.MethodsLong-term type 1 diabetes patients without metabolic comorbidities were included, 11 with sustained microalbuminuria (MA) and 14 without MA (nMA). Morning urine proteins were precipitated and resolved by 2D electrophoresis. Principal component analysis (PCA) and Projection to latent structures discriminatory analysis (PLS-DA) were adopted to assess general data validity, to pick protein fractions for identification with mass spectrometry (MS), and to test predictive value of the resulting model.ResultsProteins (n = 113) detected in more than 90% patients were considered representative. Unsupervised PCA showed excellent natural data clustering without outliers. Protein spots reaching Variable Importance in Projection score above 1 in PLS (n = 42) were subjected to MS, yielding 33 positive identifications. The PLS model rebuilt with these proteins achieved accurate classification of all patients (R2X = 0.553, R2Y = 0.953, Q2 = 0.947). Thus, multiple earlier recognized biomarkers of DN were confirmed and several putative new biomarkers suggested. Among them, the highest significance was met in kininogen-1. Its activation products detected in nMA patients exceeded by an order of magnitude the amount found in MA patients.ConclusionsReducing metabolic complexity of the diseased and control groups by meticulous patients’ selection allows to focus the biomarker search in DN. Suggested new biomarkers, particularly kininogen fragments, exhibit the highest degree of correlation with MA and substantiate validation in larger and more varied cohorts.Electronic supplementary materialThe online version of this article (doi:10.1186/s12882-017-0519-4) contains supplementary material, which is available to authorized users.

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