Objective·To develop an early diagnostic model for small cell lung cancer(SCLC)based on differences in serum metabolite expression profiles between patients with SCLC and those with benign pulmonary diseases,using machine learning algorithms.Methods·Serum samples were collected from 29 SCLC patients and 67 patients with benign lung diseases at Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine,as the training cohort.An independent external validation cohort included 20 SCLC patients and 40 patients with benign lung diseases from Gansu Provincial Cancer Hospital.A total of 69 serum metabolites were quantitatively analyzed using liquid chromatography-tandem mass spectrometry(LC-MS/MS).The XGBoost Classifier was employed to rank metabolite importance,and a forward feature selection strategy based on XGBoost was used to identify a subset of key metabolites.Diagnostic models were constructed using AdaBoost,random forest(RF),and light gradient boosting machine(LGBM)algorithms.Model performance was assessed using receiver operating characteristic(ROC)curves and the area under the curve(AUC),and validated on the external test cohort.Results·Principal component analysis(PCA)and orthogonal projections to latent structures-discriminant analysis(OPLS-DA)of the training cohort revealed distinct metabolic profiles between SCLC and benign lung disease patients.Based on feature importance rankings,six key metabolites were selected to construct the MTB-6 diagnostic model.Among the models,AdaBoost achieved the best performance,with an AUC of 0.943,sensitivity of 75.0%,and specificity of 90.9%in the training cohort.In the external test cohort,the model demonstrated robust performance with an AUC of 0.921,sensitivity of 80.0%,and specificity of 87.5%.Conclusion·The MTB-6 model,based on six serum metabolites and the AdaBoost algorithm,exhibits excellent diagnostic performance and holds potential for the differential diagnosis of SCLC and benign pulmonary diseases.

Objective·To develop an early diagnostic model for small cell lung cancer(SCLC)based on differences in serum metabolite expression profiles between patients with SCLC and those with benign pulmonary diseases,using machine learning algorithms.Methods·Serum samples were collected from 29 SCLC patients and 67 patients with benign lung diseases at Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine,as the training cohort.An independent external validation cohort included 20 SCLC patients and 40 patients with benign lung diseases from Gansu Provincial Cancer Hospital.A total of 69 serum metabolites were quantitatively analyzed using liquid chromatography-tandem mass spectrometry(LC-MS/MS).The XGBoost Classifier was employed to rank metabolite importance,and a forward feature selection strategy based on XGBoost was used to identify a subset of key metabolites.Diagnostic models were constructed using AdaBoost,random forest(RF),and light gradient boosting machine(LGBM)algorithms.Model performance was assessed using receiver operating characteristic(ROC)curves and the area under the curve(AUC),and validated on the external test cohort.Results·Principal component analysis(PCA)and orthogonal projections to latent structures-discriminant analysis(OPLS-DA)of the training cohort revealed distinct metabolic profiles between SCLC and benign lung disease patients.Based on feature importance rankings,six key metabolites were selected to construct the MTB-6 diagnostic model.Among the models,AdaBoost achieved the best performance,with an AUC of 0.943,sensitivity of 75.0%,and specificity of 90.9%in the training cohort.In the external test cohort,the model demonstrated robust performance with an AUC of 0.921,sensitivity of 80.0%,and specificity of 87.5%.Conclusion·The MTB-6 model,based on six serum metabolites and the AdaBoost algorithm,exhibits excellent diagnostic performance and holds potential for the differential diagnosis of SCLC and benign pulmonary diseases.

Objective To compare the effect of TNF-α preconditioning and ischemic preconditioning on hepatic ischemia-reperfusion injury ( IRI) and investigate the underlying mechanisms of TNF-αpreconditioning. Methods Fourty healthy male Wistar rats were random-ly divided into four groups which were Sham-operated group ( SO) ,ischemia-reperfusion group ( IR group:produced by total inflow occlusion for 30 min) ,ischemic preconditioning group ( IPC group:induce with 10 min hepatic ischemic and open 10 min before IR) and TNF-αpre-conditioning group ( TPC group:intraperitoneal injection with 1 μg/kg TNF-a 30 min prior to IR) . The sample of blood and hepatic tissue of all groups were taken after experiment. The protein levels of NF-κBp65 and Bcl-2 in the hepatic tissue were detected by immunohistochemis-try. Results There was significant difference (P<0. 05) between IR group and IPC group,TPC group on the level of ALT,AST and the expression of NF-κBp65 and Bcl-2,apoptosis index in hepatic tissue. There was no significance difference (P>0. 05) between IPC group and TPC group. Conclusion TNF-α preconditioning decreased the intensity of hepatic IRI,just as ischemic preconditioning,by induces an de-crease in the NF-κBp65 expression and an increase in the Bcl-2 expression.

Aim To explore inhibitory effects and mechanism of engineering endostatin on growth and metastasis of melanoma cells in mice. Methods Melanoma cells(2× 106/mouse)were inoculated sabcutaneously to C57BL/6 mice. After tumorigenesis,endostatin(8mg/kg.d)was administrated to tumor-bearing mice,once a day ,twenty-one in all.Dietetic state and weight change of the tumor-bearing mice were observed and tumorous sige was measured during administration of endostatin. On 26thday,the tumor-bearing mice were sacrificed,subcutaneously tumorous weight was weighed and brain,lung ,liver,spleen and kidney were excised and sections were made to supply the pathological examination. Results Area under curve in the endostatin-treated group was obviously less than that in tamor control group(P∨ 0.01). Pathological study revealed that lavge areal necrosis arose in tumor and newborn cappillaries around the tumor disapeared. Conclusion Endostatin possosses strongly inhibitory effects on growth and metastasis of mouse melanoma and formation of newborn capillaries around tumor.