Refine
Year of publication
- 2014 (2) (remove)
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Institute
- Medizin (2)
- Ernst Strüngmann Institut (1)
- MPI für Hirnforschung (1)
BACKGROUND: Recent findings support the idea that interleukin (IL)-22 serum levels are related to disease severity in end-stage liver disease. Existing scoring systems--Model for End-Stage Liver Disease (MELD), Survival Outcomes Following Liver Transplantation (SOFT) and Pre-allocation-SOFT (P-SOFT)--are well-established in appraising survival rates with or without liver transplantation. We tested the hypothesis that IL-22 serum levels at transplantation date correlate with survival and potentially have value as a predictive factor for survival.
MATERIAL AND METHODS: MELD, SOFT, and P-SOFT scores were calculated to estimate post-transplantation survival. Serum levels of IL-22, IL-6, IL-10, C-reactive protein (CRP), and procalcitonin (PCT) were collected prior to transplantation in 41 patients. Outcomes were assessed at 3 months, 1 year, and 3 years after transplantation.
RESULTS: IL-22 significantly correlated with MELD, P-SOFT, and SOFT scores (Rs 0.35, 0.63, 0.56 respectively, p<0.05) and with the discrimination in post-transplantation survival. IL-6 showed a heterogeneous pattern (Rs 0.40, 0.63, 0.57, respectively, p<0.05); CRP and PCT did not correlate. We therefore added IL-22 serum values to existing scoring systems in a generalized linear model (GLM), resulting in a significantly improved outcome prediction in 58% of the cases for both the P-SOFT (p<0.01) and SOFT scores (p<0.001).
CONCLUSIONS: Further studies are needed to address the concept that IL-22 serum values at the time of transplantation provide valuable information about survival rates following orthotopic liver transplantation.
Cross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pathological brain function, the standard CFC analysis and physiological interpretation come with fundamental problems. For example, apparent CFC can appear because of spectral correlations due to common non-stationarities that may arise in the total absence of interactions between neural frequency components. To provide a road map towards an improved mechanistic understanding of CFC, we organize the available and potential novel statistical/modeling approaches according to their biophysical interpretability. While we do not provide solutions for all the problems described, we provide a list of practical recommendations to avoid common errors and to enhance the interpretability of CFC analysis.