The aim of this study was to investigate the association between purinergic receptor P2X7 ( P2RX7 ) gene rs1718125 polymorphism and analgesic effect of fentanyl after surgery among patients with lung cancer in a Chinese Han population. A total of 238 patients with lung cancer who received resection were enrolled in our study. The genotype distributions of P2RX7 rs1718125 polymorphism were detected by polymerase chain reaction and direct sequencing. Postoperative analgesia was performed by patient-controlled intravenous analgesia, and the consumption of fentanyl was recorded. The postoperative pain was measured by visual analog scale (VAS). Differences in postoperative VAS score and postoperative fentanyl consumption for analgesia in different genotype groups were analyzed by analysis of variance assay. The frequencies of GG, GA, and AA genotypes were 46.22%, 44.96%, and 8.82%, respectively. After surgery, the postoperative VAS score of GA group was significantly high in the period of analepsia after general anesthesia and at 6 hours after surgery ( P = .041 and P = .030, respectively), while AA group exhibited obviously high in the period of analepsia after general anesthesia ( P < .001), at postoperative 6 hours ( P = .006) and 24 hours ( P = .016). Moreover, the patients carrying GA and AA genotypes needed more fentanyl to control pain within 48 hours after surgery ( P < .05 for all). P2RX7 gene rs1718125 polymorphism is significantly associated with postoperative pain and fentanyl consumption in patients with lung cancer.
Isolation Forest or iForest is one of the outstanding outlier detectors proposed in recent years. Yet, in the model setting, it is mainly based on the technique of randomization and, as a result, it is not clear how to select a proper attribute and how to locate an optimized split point on a given attribute while building the isolation tree. Aiming to the two issues, we propose an improved computational framework which allows us to seek the most separable attributes and spot corresponding optimized split points effectively. According to the experimental results, the proposed model is able to achieve overall better performance in the accuracy of outlier detection compared with the original model and its related variants.
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