Intraoperative complications in kidney tumor surgery: critical grading for the European Association of Urology intraoperative adverse incident classification

IntroductionThe European Association of Urology committee in 2020 suggested a new classification, intraoperative adverse incident classification (EAUiaiC), to grade intraoperative adverse events (IAE) in urology.AimsWe applied and validated EAUiaiC, for kidney tumor surgery.Patients and methodsA retrospective multicenter study was conducted based on chart review. The study group comprised 749 radical nephrectomies (RN) and 531 partial nephrectomies (PN) performed in 12 hospitals in Finland during 2016–2017. All IAEs were centrally graded for EAUiaiC. The classification was adapted to kidney tumor surgery by the inclusion of global bleeding as a transfusion of ≥3 units of blood (Grade 2) or as ≥5 units (Grade 3), and also by the exclusion of preemptive conversions.ResultsA total of 110 IAEs were recorded in 13.8% of patients undergoing RN, and 40 IAEs in 6.4% of patients with PN. Overall, bleeding injuries in major vessels, unspecified origin and parenchymal organs accounted for 29.3, 24.0, and 16.0% of all IEAs, respectively. Bowel (n = 10) and ureter (n = 3) injuries were rare. There was no intraoperative mortality. IAEs were associated with increased tumor size, tumor extent, age, comorbidity scores, surgical approach and indication, postoperative Clavien–Dindo (CD) complications and longer stay in hospital. 48% of conversions were reactive with more CD-complications after reactive than preemptive conversion (43 vs. 25%).ConclusionsThe associations between IAEs and preoperative variables and postoperative outcome indicate good construct validity for EAUiaiC. Bleeding is the most important IAE in kidney tumor surgery and the inclusion of transfusions could provide increased objectivity.</p

EEG and MEG brain-computer interface for tetraplegic patients

Item does not contain fulltextWe characterized features of magnetoencephalographic (MEG) and electroencephalographic (EEG) signals generated in the sensorimotor cortex of three tetraplegics attempting index finger movements. Single MEG and EEG trials were classified offline into two classes using two different classifiers, a batch trained classifier and a dynamic classifier. Classification accuracies obtained with dynamic classifier were better, at 75%, 89%, and 91% in different subjects, when features were in the 0.5-3.0-Hz frequency band. Classification accuracies of EEG and MEG did not differ

Hyaluronan in the Tumor Microenvironment

The extracellular matrix is part of the microenvironment and its functions are associated with the physical and chemical properties of the tissue. Among the extracellular components, the glycosaminoglycan hyaluronan is a key component, defining both the physical and biochemical characteristics of the healthy matrices. The hyaluronan metabolism is strictly regulated in physiological conditions, but in the tumoral tissues, its expression, size and binding proteins interaction are dysregulated. Hyaluronan from the tumor microenvironment promotes tumor cell proliferation, invasion, immune evasion, stemness alterations as well as drug resistance. This chapter describes data regarding novel concepts of hyaluronan functions in the tumor. Additionally, we discuss potential clinical applications of targeting HA metabolism in cancer therapy.Fil: Spinelli, Fiorella Mercedes. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires; ArgentinaFil: Vitale, Daiana Luján. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires; ArgentinaFil: Sevic, Ina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires; ArgentinaFil: Alaniz, Laura Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires; Argentin