Objective To evaluate the correlation between the expression level of Nanog gene and clinical outcomes of GP regimen in the advanced non-small cell lung cancer (NSCLC).Methods 62 patients of NSCLC were treated by GP method,and the outcomes were investiged between Nanog positive and nagetive patients.The expression level of Nanong was evaluated by RT-PCR and immunohistology.Results 30 out of 62 patients (48.4 ％) were Nanog positive,9 patients (28.1％) were Nanog positive,and 23 out of 32 patients were Nanog negative (71.9 ％) who have the positive effect (CR+PR).However,among 32 treatment nagetive cases,there were 21 cases (70.0 ％) who were Nanog positive and 9 cases (30.0 ％) were Nanog negatve.Survival analysis showed that 5-years lifetime of Nanog positive patients was shorter than Nanong nagetive patients.Conclusion Nanog overexpression decreases the sensitivity of GP regimen and lifetime of NSCLC patient.Nanog expression level may provide a useful factor for clinical treatment and prognosis of NSCLC patient.

Objective To investigate image quality and clinical value of dual-source dual energy virtual non-contrast (VNC) CT of the head. MethodsSixty-two patients suspected of cerebrovascular diseases underwent conventional non-contrast (CNC) CT and dual energy CTA examination of the head with dual-source CT. Virtual non-contrast images were reconstructed using dual energy software. The CT values of gray matter, white matter, cerebrospinal fluid, hyperdense hemorrhagic lesion and hypodense ischemic lesion were compared between CNC and VNC images. A four-score scale was used to assess image quality subjectively. Image noise, radiation dosage and detection rate were compared between CNC and VNC images. Paired t test, Wilcoxon signed ranks test and Chi-square test (McNemar test and Kappa test) were used. Results The CT value on CNC and VNC images, were (43. 3 ± 1.5) and (33. 2 ± 1.3) HU for gray matter (t = 46.98, P ＜ 0. 01), (32. 9 ± 1.3) and (28.8 ± 1.6) HU for white matter(t = 16. 28, P ＜0.01), (9.0 ± 1.4) and (5.3 ± 1.9) HU for cerebrospinal fluid (t=12.41, P＜0.01),(62.8 ±10.0) and (51.3 ± 11.5) HU for hyperdense lesion (Z = -4.37, P ＜ 0.01), (20.7 ±4.7) and (18.0 ±6. 9) HU for hypodense lesion (t = 3. 84, P ＜ 0. 01), respectively. VNC images[(1.63 ±0.34) HU]had more noise than CNC images[(0.99±0.18) HU](Z= -6.41, P＜0.01). VNC [(0. 53 ± 0. 08) mSv]had less effective dose than CNC[(1.37 ± 0. 23) mSy](Z= - 6. 45, P ＜ 0. 01).In subjective assessment, VNC images had more noise (2. 7 ± 0. 5 for VNC and 3.9 ± 0. 3 for CNC,Z = -6. 84, P ＜ 0. 01) and skull base-related artifacts (2. 4 ± 0. 9 for VNC and 3.7 ± 0. 5 for CNC,Z = -6. 15, P ＜0. 01) than CNC images. The gray/white matter contrast (1.3 ± 0. 5 for VNC and 3.3 ±0. 6 for CNC, Z = - 7. 01, P ＜ 0. 01), hyperdense lesion display (3.0 ± 0. 4 for VNC and 4. 0 ± 0. 0 for CNC,Z = -4. 52, P ＜ 0. 01) and hypodense lesion display (3.2 ± 0. 8 for VNC and 3.9 ± 0. 3 for CNC,Z= -3. 12, P ＜0. 01) on VNC images were lower than those on CNC images. In per-patient analysis,29 cases of hyperdense lesion (hemorrhage) were found on VNC images without misdiagnosis. The sensitivity, specificity, positive predictive value and negative predictive value were all 100. 0% (29/29,33/33, 29/29, 33/33). VNC images had the same detection rate of hyperdense lesions as CNC images (P ＞0. 05, Kappa = 1. 000) at per-patient level. Twenty-two patients with hypodense ischemic lesions were found on VNC images with one false positive case and two false negative cases. The sensitivity,specificity, positive predictive value and negative predictive value were 91.3% (21/23), 97.4%(38/39), 95.5% (21/22) and 95.0% (38/40) respectively. No statistical difference was found in detecting hypodense lesions between VNC and CNC images (χ2 = 0. 00, P ＞ 0. 05, Kappa = 0. 895). In per-lesion analysis, 53 hemorrhage lesions were found on VNC images with false negative results of four lesions and no false positive result. The sensitivity, specificity, positive predictive value and negative predictive value were 93.0% (53/57), 100. 0% (38/38), 100. 0% (53/53) and 90. 5% (38/42)respectively. There was no significant difference in detection rate of hyperdense lesion between VNC and CNC images (χ2 =2. 25, P ＞0. 05, Kappa =0. 914). Thirty-eight hypodense lesions were found on VNC images with 2 false positive lesions and 13 false negative lesions. The sensitivity, specificity, positive predictive value and negative predictive value were 73.5% (36/49), 96.4% (53/55), 94. 7% (36/38)and 80. 3% (53/66) respectively. The detection rate of hypodense lesion on VNC images was lower than that on CNC images (χ2 = 6. 67 ,P ＜ 0.01, Kappa = 0. 707). Conclusion Compared with CNC images,head VNC images have reduced image quality and radiation dosage. VNC images can replace CNC images potentially in detecting intracranial hemorrhage and provide information for ischemic cerebrovascular diseases to some extent.

Objective:To retrospectively construct competency-based portfolios for medical students to analyze the factors influencing competency self-assessment, and to explore an approach to efficient portfolio construction.Methods:The participants were randomly selected among medical graduates of 2015 in Shantou University Medical College. Through records collection and an online questionnaire survey, portfolios were built for the participants based on their development data during eight years from college entrance, college education, to post-graduation medical education. The correlation between variables was determined using Kendall's tau-b correlation coefficients. The inter-group differences were analyzed using the Mann-Whitney U test. Results:A total of 126 usable questionnaires for effective developmental portfolios were collected. There were a total of 208 indicators collected, including 79 questionnaire indicators (including 36 for competency self-assessment). The reliability coefficient (Cronbach's α) of the questionnaire was 0.984. The factors related to competency self-assessment were identified. Conclusion:This study provides a basis for the implementation of portfolio assessment, which can promote students' self-assessment and competency development. Optimizing the indicator system, building an online platform, increasing participants' participation motivation, and emphasizing self-reflection and feedback will help improve the efficiency of developmental portfolio construction and its performance.