No abstract

In mainland China, "adenocarcinoma" and "non-smoker" are independent predictors for EGFR mutations. Response to gefitinib favors patients with EGFR mutations. The clinical selected populations for gefitinib are non-smokers with adenocarcinoma.

Purpose: Studies have shown that mutations in the epidermal growth factor receptor (EGFR) tyrosine kinase domain are associated with response of lung cancer to gefitinib (Iressa, AstraZeneca Corp., Shanghai, China). A higher incidence of EGFR mutation was observed in non^small cell lung cancer (NSCLC) patients of Japanese origin compared with those of American origin. However, no data about such mutations in Chinese patients with NSCLC could be obtained. Methods: Primary NSCLC tissues were obtained for analysis of mutations in exons 18 to 21 of EGFR from a total of 76 patients, of whom 54 did not receive gefitinib therapy and 22 did. PCR products were sequenced directly and mutations were confirmed by an independent PCR and sequence analysis. All types of mutation were cloned and sequenced. Results: A total of 10 types of mutation were found in the series of patients, including two different silent mutations in exon 20 from 11patients. More than half of the silent mutations (6 of 11) in exon 20 coexisted with other mutations. Mutations were more frequent in adenocarcinoma (17 of 35; 48.6%) compared with squamous carcinoma (1 of 19; 5.3%) among untreated patients. Similar mutations were observed in all seven gefitinib-treated patients with partial response, and no mutations were detected in all eight patients with progressive disease (P < 0.001), except two silent mutations. Three mutations were observed in seven patients with stable disease. Conclusions: Mutations in the epidermal growth factor receptor tyrosine kinase domain in lung adenocarcinomas from Chinese patients were more frequent than reported previously in lung adenocarcinomas from American patients. Such mutations were well correlated with tumor response to gefitinib.

The ultrasonic echo signal of wear debris is influenced by many matters. It causes so much more noise. Therefore, it puts forward an improved online de-noising method for ultrasonic echo signal of wear debris in oil. In the dual-tree complex wavelet transform (DTCWT) field, a method, which combines the new non-linear threshold function with adaptive threshold, utilizes particle swarm optimization (PSO) for optimizing the parameter of non-linear threshold function to get the optimal solution. The result of de-noising method can be evaluated. Experimental results show that the proposed method has obvious effect on signal de-noising.