Objective： To evaluate the clinical course of patients with small cell lung cancer （SCLC） as second primary malignancy. Methods： Among the 355 patients diagnosed with SCLC at Helen and Harry Gray Cancer Center of ...Objective： To evaluate the clinical course of patients with small cell lung cancer （SCLC） as second primary malignancy. Methods： Among the 355 patients diagnosed with SCLC at Helen and Harry Gray Cancer Center of Hartford Hospital Connecticut USA between 1988 and 1998, the records of 48 patients, which had been diagnosed with other malignancies before their diagnosis of SCLC, were retro- spectively reviewed. Results： Forty-eight patients （13.5%） were diagnosed with other malignancies prior to their SCLC among which 43 had documented smoking history and 93% of them （40/43） were current/former smokers. Of the 28-second primary SCLC patients who were treated with standard method, 11 （39.3%） achieved CR. 12 （42.8%） achieved PR, and the RR was 82.1%. The median survival of the 28 treated with standard method was 11.3 months （5.1-77.7 months）, while that of the rest 19 untreated patients （1 of 20 was lost to follow-up） was only 2.0 months （0.5 34.0 months）. There was no significant difference in the median survival and RR between 165 treated first primary SCLC （13.5 months and 77.6% respectively） and 28 treated secondary primary SCLC （11.3 months and 82.1% respectively） （P〉0.05）. The patients who had prostate cancer were older and subjected to less treatments than those with skin cancer, so their survival was shorter than the latter （3.5 months vs. 15 months, P〈0.05）. Conclusion： The response and survival of the treated patients with SCLC as a second malignancy showed no difference as compared to the treated ones with SCLC only. Therefore, an active medical treatment is important to relieve symptom and prolong survival of the second primary SCLC patients.展开更多
AIM to determine whether cyclooxygenase-2(COX-2) and prostaglandin E1 receptor(EP1) contribute to disease and whether they help predict prognosis.METHODS We retrospectively reviewed the records of 116 patients with he...AIM to determine whether cyclooxygenase-2(COX-2) and prostaglandin E1 receptor(EP1) contribute to disease and whether they help predict prognosis.METHODS We retrospectively reviewed the records of 116 patients with hepatocellular carcinoma(HCC) who underwent surgery between 2008 and 2011 at our hospital. Expression of COX-2 and EP1 receptor was examined by immunohistochemistry of formalin-fixed, paraffinembedded tissues using polyclonal antibodies. Possible associations between immunohistochemical scores and survival were determined.RESULTS Factors associated with poor overall survival(OS) were alpha-fetoprotein > 400 ng/m L, tumor size ≥ 5 cm, and high EP1 receptor expression, but not high COX-2 expression. Disease-free survival was not significantly different between patients with low or high levels of COX-2 or EP1. COX-2 immunoreactivity was significantly higher in well-differentiated HCC tissues(Edmondson grade Ⅰ-Ⅱ) than in poorly differentiated tissues(Edmondson grade Ⅲ-Ⅳ)(P = 0.003). EP1 receptor immunoreactivity was significantly higher in poorly differentiated tissue than in well-differentiated tissue(P = 0.001).CONCLUSION COX-2 expression appears to be linked to early HCC events(initiation), while EP1 receptor expression may participate in tumor progression and predict survival.展开更多
Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machin...Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.展开更多
文摘Objective： To evaluate the clinical course of patients with small cell lung cancer （SCLC） as second primary malignancy. Methods： Among the 355 patients diagnosed with SCLC at Helen and Harry Gray Cancer Center of Hartford Hospital Connecticut USA between 1988 and 1998, the records of 48 patients, which had been diagnosed with other malignancies before their diagnosis of SCLC, were retro- spectively reviewed. Results： Forty-eight patients （13.5%） were diagnosed with other malignancies prior to their SCLC among which 43 had documented smoking history and 93% of them （40/43） were current/former smokers. Of the 28-second primary SCLC patients who were treated with standard method, 11 （39.3%） achieved CR. 12 （42.8%） achieved PR, and the RR was 82.1%. The median survival of the 28 treated with standard method was 11.3 months （5.1-77.7 months）, while that of the rest 19 untreated patients （1 of 20 was lost to follow-up） was only 2.0 months （0.5 34.0 months）. There was no significant difference in the median survival and RR between 165 treated first primary SCLC （13.5 months and 77.6% respectively） and 28 treated secondary primary SCLC （11.3 months and 82.1% respectively） （P〉0.05）. The patients who had prostate cancer were older and subjected to less treatments than those with skin cancer, so their survival was shorter than the latter （3.5 months vs. 15 months, P〈0.05）. Conclusion： The response and survival of the treated patients with SCLC as a second malignancy showed no difference as compared to the treated ones with SCLC only. Therefore, an active medical treatment is important to relieve symptom and prolong survival of the second primary SCLC patients.
基金Supported by National Natural Science Foundation of China,No.81260331Key Laboratory for High-Incidence Tumor Prevention and Treatment,Ministry of Education,No.GKE2015-ZZ05
文摘AIM to determine whether cyclooxygenase-2(COX-2) and prostaglandin E1 receptor(EP1) contribute to disease and whether they help predict prognosis.METHODS We retrospectively reviewed the records of 116 patients with hepatocellular carcinoma(HCC) who underwent surgery between 2008 and 2011 at our hospital. Expression of COX-2 and EP1 receptor was examined by immunohistochemistry of formalin-fixed, paraffinembedded tissues using polyclonal antibodies. Possible associations between immunohistochemical scores and survival were determined.RESULTS Factors associated with poor overall survival(OS) were alpha-fetoprotein > 400 ng/m L, tumor size ≥ 5 cm, and high EP1 receptor expression, but not high COX-2 expression. Disease-free survival was not significantly different between patients with low or high levels of COX-2 or EP1. COX-2 immunoreactivity was significantly higher in well-differentiated HCC tissues(Edmondson grade Ⅰ-Ⅱ) than in poorly differentiated tissues(Edmondson grade Ⅲ-Ⅳ)(P = 0.003). EP1 receptor immunoreactivity was significantly higher in poorly differentiated tissue than in well-differentiated tissue(P = 0.001).CONCLUSION COX-2 expression appears to be linked to early HCC events(initiation), while EP1 receptor expression may participate in tumor progression and predict survival.
文摘Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.
