Background: Endoscopic internal drainage (EID) with double pigtail stents and low negative pressure endoscopic vacuum therapy (EVT) are treatment options for leakages after upper GI oncologic surgery. We aimed to compare the effectiveness of these techniques. Patients and methods: Between 2016 and 2019, patients treated with EID in five centers in France and with EVT in Göttingen, Germany were included and retrospectively analyzed using univariate analysis. Pigtails were changed every 4 weeks, EVT was repeated every 3-4 days until leak closure. Results: 35 EID and 27 EVT patients were included, with a median leak size of 0.75 cm (0.5-1.5). Overall treatment success was 100% [CI 90; 100] in EID vs. 85.2% [CI 66.3; 95.8] in EVT, p=0.03. The median number of endoscopic procedures was 2 (2; 3) vs. 3 (2; 6.5), p<0.01 and the median treatment duration was 42 (28; 60) vs. 17 days (7.5; 28), p<0.01, for EID vs. EVT, respectively. Conclusion: EID and EVT provide high closure rates for upper GI anastomotic leakages. EVT provides a shorter treatment duration at the cost of a higher number of procedures.

BackgroundOne aim of the in silico characterization of proteins is to identify all residue-positions, which are crucial for function or structure. Several sequence-based algorithms exist, which predict functionally important sites. However, with respect to sequence information, many functionally and structurally important sites are hard to distinguish and consequently a large number of incorrectly predicted functional sites have to be expected. This is why we were interested to design a new classifier that differentiates between functionally and structurally important sites and to assess its performance on representative datasets.ResultsWe have implemented CLIPS-1D, which predicts a role in catalysis, ligand-binding, or protein structure for residue-positions in a mutually exclusive manner. By analyzing a multiple sequence alignment, the algorithm scores conservation as well as abundance of residues at individual sites and their local neighborhood and categorizes by means of a multiclass support vector machine. A cross-validation confirmed that residue-positions involved in catalysis were identified with state-of-the-art quality; the mean MCC-value was 0.34. For structurally important sites, prediction quality was considerably higher (mean MCC = 0.67). For ligand-binding sites, prediction quality was lower (mean MCC = 0.12), because binding sites and structurally important residue-positions share conservation and abundance values, which makes their separation difficult. We show that classification success varies for residues in a class-specific manner. This is why our algorithm computes residue-specific p-values, which allow for the statistical assessment of each individual prediction. CLIPS-1D is available as a Web service at http://www-bioinf.uni-regensburg.de/.ConclusionsCLIPS-1D is a classifier, whose prediction quality has been determined separately for catalytic sites, ligand-binding sites, and structurally important sites. It generates hypotheses about residue-positions important for a set of homologous proteins and focuses on conservation and abundance signals. Thus, the algorithm can be applied in cases where function cannot be transferred from well-characterized proteins by means of sequence comparison.

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We consider the distribution of the age of an individual picked uniformly at random at some fixed time in a linear birth-and-death process. By exploiting a bijection between the birth-and-death tree and a contour process, we derive the cumulative distribution function for this distribution. In the critical and supercritical cases, we also give rates for the convergence in terms of the total variation and other metrics towards the appropriate exponential distribution.