MDF treatment with a Dielectric Barrier Discharge (DBD) torch

International audienceA Dielectric Barrier Discharge (DBD) torch has been designed for the treatment of several wood species and composites. This paper presents some results obtained onto Medium Density Fiberboard (MDF) samples. The aim of the treatments was to improve their wetting properties. Tests made before and after treatments showed improvements of surface wetting and have been correlated with surface chemical composition. It also has been shown that the treatment effect is ephemeral

Development of clinical prediction models for outcomes of complicated intra-abdominal infection.

Methods A multicentre observational study was conducted from August 2016 to February 2017 in the UK. Adult patients diagnosed with cIAI were included. Multivariable logistic regression was performed to develop CPMs for mortality and cIAI relapse. The c-statistic was used to test model discrimination. Model calibration was tested using calibration slopes and calibration in the large (CITL). The CPMs were then presented as point scoring systems and validated further. Results Overall, 417 patients from 31 surgical centres were included in the analysis. At 90 days after diagnosis, 17.3 per cent had a cIAI relapse and the mortality rate was 11.3 per cent. Predictors in the mortality model were age, cIAI aetiology, presence of a perforated viscus and source control procedure. Predictors of cIAI relapse included the presence of collections, outcome of initial management, and duration of antibiotic treatment. The c-statistic adjusted for model optimism was 0.79 (95 per cent c.i. 0.75 to 0.87) and 0.74 (0.73 to 0.85) for mortality and cIAI relapse CPMs. Adjusted calibration slopes were 0.88 (95 per cent c.i. 0.76 to 0.90) for the mortality model and 0.91 (0.88 to 0.94) for the relapse model; CITL was −0.19 (95 per cent c.i. −0.39 to −0.12) and − 0.01 (− 0.17 to −0.03) respectively. Conclusion Relapse of infection and death after complicated intra-abdominal infections are common. Clinical prediction models were developed to identify patients at increased risk of relapse or death after treatment, although these require external validation