Incidence, risk factors and prevention of stoma site incisional hernias

Aim: Stoma reversal might lead to a stoma site incisional hernia. Recently, prophylactic mesh reinforcement of the stoma site has gained increased attention, supporting the need for accurate data on the incidence of and risk factors for stoma site incisional hernia and to identify high-risk patients. The aim of this study was to assess incidence, risk factors and prevention of stoma site incisional hernias. Method: Embase, MEDLINE, Web of Science, Cochrane and Google Scholar databases were searched. Studies reporting the incidence of stoma site incisional hernia after stoma reversal were included. Study quality was assessed with the Newcastle–Ottawa Scale and Cochrane risk of bias tool. Data on incidence, risk factors and prophylactic mesh reinforcement were extracted. Results: Of 1440 articles found, 33 studies comprising 4679 reversals were included. The overall incidence of incisional hernia was 6.5% [range 0%–38%, median follow-up 27.5 (17.54–36) months]. Eleven studies assessed stoma site incisional hernia as the primary end-point, showing an incidence of 17.7% [range 1.7%–36.1%, median follow-up 28 (15.25–51.70) months]. Body mass index, diabetes and surgery for malignant disease were found to be independent risk factors, as derived from eight studies. Two retrospective comparative cohort studies showed significantly lower rates of stoma site incisional hernia with prophylactic mesh reinforcement compared with nonmesh controls [6.4% vs 36.1% (P = 0.001); 3% vs 19% (P = 0.04)]. Conclusion: Stoma site incisional hernia should not be underestimated as a long-term problem. Body mass index, diabetes and malignancy seem to be potential risk factors. Currently, limited data are available on the outcomes of prophylactic mesh reinforcement to prevent stoma site incisional hernia

Completing SBGN-AF Networks by Logic-Based Hypothesis Finding

International audienceThis study considers formal methods for finding unknown interactions of incomplete molecular networks using microarray profiles. In systems biology, a challenging problem lies in the growing scale and complexity of molecular networks. Along with high-throughput experimental tools, it is not straightforward to reconstruct huge and complicated networks using observed data by hand. Thus, we address the completion problem of our target networks represented by a standard markup language, called SBGN (in particular, Activity Flow). Our proposed method is based on logic-based hypothesis finding techniques; given an input SBGN network and its profile data, missing interactions can be logically generated as hypotheses by the proposed method. In this paper, we also show empirical results that demonstrate how the proposed method works with a real network involved in the glucose repression of S. cerevisiae