Comparative evaluation of seven resistance interpretation algorithms and their derived genotypic inhibitory quotients for the prediction of 48 week virological response to darunavir-based salvage regimens

Background: the darunavir genotypic inhibitory quotient (gIQ) has been suggested as one of the predictors of virological response to darunavir-containing salvage regimens. Nevertheless, which resistance algorithm should be used to optimize the calculation of gIQ is still debated. The aim of our study was to compare seven different free-access resistance algorithms and their derived gIQs as predictors of 48 week virological response to darunavir-based salvage therapy in the clinical setting. Methods: patients placed on two nucleoside reverse transcriptase inhibitors\u200a+\u200a600/100 mg of darunavir/ritonavir twice daily \u200a\ub1\u200a enfuvirtide were prospectively evaluated. Virological response was assessed at 48 weeks. Darunavir resistance interpretation was performed according to seven different algorithms, of which two were weighted algorithms. Analysis of other factors potentially associated with virological response at 48 weeks was performed. Results: fifty-six treatment-experienced patients were included. Overall, 35 patients (62.5%) had a virological response at 48 weeks. Receiver operator characteristic curve analysis showed that De Meyer's weighted score (WS) and its derived gIQ (gIQ WS) were the most accurate parameters defining virological response, and related cut-offs showed the best sensitivity/specificity pattern. In univariate logistic regression analysis, baseline log viral load (P = 0.028), optimized background score 65 2 (P = 0.048), WS >5 (P = 0.001) and WS gIQ 65 600 (P\u200a<\u200a0.0001) were independently associated with virological response. In multivariate analysis, only baseline log viral load (P = 0.008) and WS gIQ 65 600 (P < 0.0001) remained in the model. Conclusions: in our study, although different resistance interpretation algorithms and derived gIQs were associated with virological response, gIQ WS was the most accurate predictive model for achieving a successful virological response

Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage

Food product safety is one of the most promising areas for the application of electronic noses. During the last twenty years, these sensor-based systems have made odour analyses possible. Their application into the area of food is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network model has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population. Comparison results against advanced machine learning schemes indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology