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Background: At Eurotransplant (ET), kidneys are transferred to 'rescue allocation' (RA), whenever the standard allocation (SA) algorithms Eurotransplant kidney allocation system (ETKAS) and Eurotransplant senior program (ESP) fail. We analyzed the outcome of RA. Methods: Retrospective patient clinical and demographic characteristics association analyses with graft outcomes for 2,421 recipients of a deceased donor renal transplantation (DDRT) after RA versus 25,475 after SA from 71 centers across all ET countries from 2006 to 2018.Results: Numbers of DDRTs after RA increased over the time, especially in Germany. RA played a minor role in ESP vs. ETKAS (2.7% vs. 10.4%). RA recipients and donors were older compared to SA recipients and donors, cold ischemia times were longer, waiting times were shorter, and the incidence of primary non-function was comparable. Among ETKASrecipients, HLA matching was more favorable in SA (mean 3.7 vs. 2.5). In multivariate modeling, the incidence of death with a functioning graft (DwFG) in ETKAS was reduced in RA compared to SA (subdistribution hazard ratio 0.70, 95% confidence interval [0.60-0.81], p<0.001) whereas other outcomes (mortality, graft loss) were not significantly different. None of the three outcomes were significantly different when comparing RA with SA within the ESP program.Conclusions: Facing increased waiting times and mortality on dialysis due to donor shortage, this study reveals encouragingly positive DDRT outcomes following RA. This supports the extension of RA to more patients and as an alternative tool to enable transplantation in patients in countries with prohibitively long waiting times or at risk of deterioration.

Increased local blood supply is thought to be one of the mechanisms underlying oxidative adaptations to interval training regimes. The relationship of exercise intensity with local blood supply and oxygen availability has not been sufficiently evaluated yet. The aim of this study was to examine the effect of six different intensities (40-90% peak oxygen uptake, VO ) on relative changes in oxygenated, deoxygenated and total haemoglobin (ΔO Hb, ΔHHb, ΔTHb) concentration after exercise as well as end-exercise ΔHHb/ΔVO as a marker for microvascular O distribution. Seventeen male subjects performed an experimental protocol consisting of 3 min cycling bouts at each exercise intensity in randomized order, separated by 5 min rests. ΔO Hb and ΔHHb were monitored with near-infrared spectroscopy of the vastus lateralis muscle, and VO was assessed. ΔHHb/ΔVO increased significantly from 40% to 60% VO peak and decreased from 60% to 90% VO peak. Post-exercise ΔTHb and ΔO Hb showed an overshoot in relation to pre-exercise values, which was equal after 40-60% VO and rose significantly thereafter. A plateau was reached following exercise at ≥80% VO . The results suggest that there is an increasing mismatch of local O delivery and utilization during exercise up to 60% VO . This insufficient local O distribution is progressively improved above that intensity. Further, exercise intensities of ≥80% VO induce highest local post-exercise O availability. These effects are likely due to improved microvascular perfusion by enhanced vasodilation, which could be mediated by higher lactate production and the accompanying acidosis.

For several years, the detection of gait has been popularly implemented using wearable sensors, especially in the sports and medical areas. They are unobtrusive devices which allow to monitor individuals without the need of any ambulatory technology. Despite the fact, the optimal location of the sensor remains uncertain and dependent on the type of measurement. Ear-worn sensors provide a tactical position, robust against movement, that might be significant for gait classification. The purpose of this paper is to demonstrate the accuracy and reliability of in-ear accelerometer sensor to perform gait classification, between the activities walking and running. The data was collected from fourteen participants using an in-ear sensor called 'Cosinussº One', which contains a three-dimensional accelerometer sensor. The main characteristics between these two activities were detected using 17 time domain features, as for instance the maximums and standard deviations of the 3-axes, and 3 different window sizes were evaluated: 3.75s, 2s and 1s. Support vector machine (SVM) and k-nearest neighbors (KNN) classifiers were implemented and later compared. The total number of features was reduced to 6 for SVM and 12 for KNN preserving the same results. An accuracy over 99% for both classifiers was achieved, outperforming most of the previous studies.