Perfusion of the deep inferior epigastric perforator flap measured by laser Doppler imager.

BACKGROUND: During surgery, circulation changes in the deep inferior epigastric perforator free flap (DIEP). Although blood flow is an important parameter for surgical outcome, little research has been performed on this topic, especially during the process of transplantation. This study examined the pattern of perfusion of DIEP flaps over time. METHODS: In all, 16 flaps were studied in 14 DIEP patients. Flap perfusion was measured with the laser Doppler imager at 4 different time points-before, during, and after surgery. RESULTS: Both central and peripheral perfusion did not alter after dissection, when blood supply became restricted to the abdominal vascular pedicle. After transplantation, blood flow was higher in the central part of the flap compared with the peripheral border. Central flow increased after transplantation, compared with measurements before and during surgery. Peripheral flow, however, decreased after transplantation. CONCLUSIONS: Surprisingly, flap perfusion did not alter after dissection. It only changed after flap transplantation, when central blood flow increased and peripheral flow decreased.

The effectiveness of massage with and without dimethyl sulfoxide in preventing pressure ulcers: a randomized, double-blind cross-over trial in patients prone to pressure ulcers.

BACKGROUND: Although guidelines advise against massage, it is one of the methods widely regarded and used by nurses to prevent pressure ulcers (PU). OBJECTIVES: The purpose of this study was to examine the effectiveness of different variations of massage in preventing pressure ulcers. METHODS: A randomized, double-blind cross-over design, in which patients of nursing homes who are prone to PU underwent two of the three possible interventions; 'position changes only', 'massaging with an indifferent cream' and 'massaging with a dimethyl sulfoxide (DMSO) cream'. RESULTS: The results of three interventions did not differ significantly. DMSO did not fulfil the expectations raised by literature and a previous pilot-study.

Assessing the importance of features for multi-layer perceptrons.

In this paper we establish a mathematical framework in which we develop measures for determining the contribution of individual features to the performance of a classifier. Corresponding to these measures, we design metrics that allow estimation of the importance of features for a specific multi-layer perceptron neural network. It is shown that all measures constitute lower bounds for the correctness that can be obtained when the feature under study is excluded and the classifier rebuilt. We also present a method for pruning input nodes from the network such that most of the knowledge encoded in its weights is retained. The proposed metrics and the pruning method are validated with a number of experiments with artificial classification tasks. The experiments indicate that the metric called replaceability results in the tightest error bounds. Both this metric and the metric called expected influence result in good rankings of the features.