Preoperative immunonutrition and its effect on postoperative outcomes in well-nourished and malnourished gastrointestinal surgery patients: a randomised controlled trial

Background/Objectives:
Invasive procedures such as surgery cause immunosuppression, leading to increased risk of complications, infections and extended hospital stay. Emerging research around immune-enhancing nutrition supplements and their ability to reduce postoperative complications and reduce treatment costs is promising. This randomised controlled trial aims to examine the effect of preoperative immunonutrition supplementation on length of hospital stay (LOS), complications and treatment costs in both well-nourished and malnourished gastrointestinal surgery patients.

Subjects/Methods:
Ninety-five patients undergoing elective upper and lower gastrointestinal surgery were recruited. The treatment group (n=46) received a commercial immuno-enhancing supplement 5 days preoperatively. The control group (n=49) received no supplements. The primary outcome measure was LOS, and secondary outcome measures included complications and cost.

Results:
A nonsignificant trend towards a shorter LOS within the treatment group was observed (7.1±4.1 compared with 8.8±6.5 days; P=0.11). For malnourished patients, this trend was greater with hospital stay reduced by 4 days (8.3±3.5 vs 12.3±9.5 days; P=0.21). Complications and unplanned intensive care admission rates were very low in both the groups. The average admission cost was reduced by AUD1576 in the treatment group compared with the control group (P=0.37).

Conclusions:
Preoperative immunonutrition therapy in gastrointestinal surgery has the potential to reduce the LOS and cost, with greater treatment benefit seen in malnourished patients; however, there is a need for additional research with greater patient numbers.

Investigating well-being, work limitations and preferences for self-management education and peer support among younger people with hip and knee osteoarthritis: protocol for a cross-sectional study

Introduction: 

From ecological creativity to an ecology of well-being: 'flows & catchments' as a case study of NVivo

This paper’s research question concerns how the ecological creativity of the Volcanic Plains region of Western Victoria may be transformed into an ecology of well-being of benefit to the local community. Drawing on the philosophies of Spinoza and Gilles Deleuze, we argue that community well-being results from the richness of connections and relationships made within a place. The case study for our investigation is ‘Flows & Catchments’, which is an ongoing, collaborative, creative-arts research project auspiced by Deakin University. Its modus operandi is Practice-Based Research (PBR), and its aim is to promote community well-being in Western Victoria. However, while the whole metier of the creative arts is to make the novel connections and relationships that should bring about community wellbeing, the various artists of ‘Flows & Catchments’ have proved slightly reluctant to make connections outside of their individual or small-group sub-projects. In this way, ecological creativity has not reached its full potential as an ecology of well-being because the rich connections and relationships essential to this well-being have not yet been fully realised. This paper explores the potential of using the NVivo qualitative analysis software package to bring together the creative-arts sub-projects of ‘Flows & Catchments’, as a way of fostering an ecology of well-being out of a currently dispersed ecological creativity.

Nonorthogonal approximate joint diagonalization with well-conditioned diagonalizers

To make the results reasonable, existing joint diagonalization algorithms have imposed a variety of constraints on diagonalizers. Actually, those constraints can be imposed uniformly by minimizing the condition number of diagonalizers. Motivated by this, the approximate joint diagonalization problem is reviewed as a multiobjective optimization problem for the first time. Based on this, a new algorithm for nonorthogonal joint diagonalization is developed. The new algorithm yields diagonalizers which not only minimize the diagonalization error but also have as small condition numbers as possible. Meanwhile, degenerate solutions are avoided strictly. Besides, the new algorithm imposes few restrictions on the target set of matrices to be diagonalized, which makes it widely applicable. Primary results on convergence are presented and we also show that, for exactly jointly diagonalizable sets, no local minima exist and the solutions are unique under mild conditions. Extensive numerical simulations illustrate the performance of the algorithm and provide comparison with other leading diagonalization methods. The practical use of our algorithm is shown for blind source separation (BSS) problems, especially when ill-conditioned mixing matrices are involved.