We investigated whether human beta2 adrenoceptor (beta2AR) gene polymorphisms are associated with the pressor response to laryngoscopy and tracheal intubation. Ninety-two patients undergoing elective surgery under general anaesthesia were enrolled into this study. Arterial systolic pressure, heart rate and rate pressure product were measured before induction of anaesthesia and 1 min following laryngoscopy and tracheal intubation. Genomic DNA was then used to identify the beta2AR-16 and beta2AR-27 genes using an allele-specific polymerase chain reaction method. Using multiple linear regression models, controlling for age, sex, weight, baseline blood pressure, heart rate and rate pressure product, we found that patients who possessed the glutamic acid homozygote of beta2AR-27 produced significantly greater changes in mean arterial pressure and rate pressure products than patients with the glutamine homozygote of beta2AR-27 (beta coefficient for mean blood pressure = 11.81, beta coefficient for pulse-pressure product = 8.76, both p-values = 0.023). These findings suggest that genetic variability in the human beta2AR gene polymorphisms may be associated with the pressor response to laryngoscopy and tracheal intubation.

Dynamic Ensemble Selection (DES) is a special type of ensemble modeling that selects different subsets of base classifiers for different test sample cases. In this process, multiple base classifiers are compared in terms of their competence as an ensemble to the best possible prediction for a given test sample case. Traditional DES methods rely on the Euclidean distancebased k-Nearest Neighbor (kNN) algorithm to identify the most relevant reference data points in such a way that the base classifiers correctly classifying them can be considered for an optimal ensemble. However, this approach is sensitive to the local structure of the data and the presence of noisy or irrelevant attributes. This study proposes a novel distribution-based DES (DDES) framework that takes the data structure into consideration when selecting reference data points. The experimental results for 30 classification problems indicate that the proposed approach yielded the best accuracy among the competing DES methods. Additionally, we discuss the correlation between the data complexity and the improvement in classification performance.

Since publication of the original article, the authors have noticed that there were errors in the labelling of Figures 6D and 6E. The correct figure and its legend are reproduced here. The authors wish to apologise for any inconvenience caused.

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