Objective To evaluate the effect and safety of combined therapy of paclitaxel and gemcitabine for anthracycine(ANT)-resistant advanced breast cancer(ABC).MethodsFrom May 2000 to Aug 2004,twenty six patients with ANT-resistant ABC were treated with this combined regime.The median chemotherapy cycles were 2.5(range from 2 to 3 cycles).ResultsOf 26 patients, there were 3 complete(11.5%) and 11 partial(42.3%) responses for an overall response rate of 53.8%. Eight cases remained stable (30.8%) and 4 progressive (15.4%). The median survival time was 18 months. The median time to progression was 7 months. The main toxic reaction included bone marrow depression, liver function damage, alopecia, mucositis and peripheral neurotoxicity. ConclusionsCombined medication of gemcitabine and paclitaxel is effective in therapy of ANT-resistant advanced breast cancer with acceptable toxicity.

Objective To investigate the expression of sphingosine kinase 1(SphK1)and NF-κB in colon carcinoma tissues and their correlation with clinicopathologic features.Methods Sixty-six paraffinembedded colon carcinoma samples and 66 fresh colon carcinoma samples were tested using immunohistochemistry,RT-PCR and Western blot,respectively.Results In 66 fresh colon carcinoma samples,the positive rate of SphK1 and NF-κB mRNA expression were 84.85％(56/66)and 74.24％(49/66),while the positive rate of SphK1 and NF-κB protein detected by Western blot were 78.79％(52/66)and 69.70％(46/66).The positive rates were higher than those in the adjacent tissues[mRNA:63.64％(42/66),48.49％(32/66);protein:57.58％(38/66),45.45％(30/66)]and the normal mucosa [mRNA:42.42％(28/66),25.76％(17/66); protein:36.36％(24/66),24.24％(16/66)],with statistical significances(all P values ＜ 0.05).The mean expressive levels of SphK1 and NF-kB mRNA and protein in colon carcinoma were both significantly higher than those in the adjacent tissues and the normal mucosa(mRNA:0.55±0.06 vs0.35 ±0.05 vs0.25±0.05,0.75 ±0.06 vs0.43±0.05 vs0.30±0.04 ; protein:0.77 ± 0.05 vs 0.38 ± 0.06 vs 0.12 ± 0.03,0.45 ± 0.08 vs 0.23 ± 0.05 vs 0.13 ± 0.03 ;all P values ＜ 0.05).There was a close correlation between SphK1 and NF-kB expression levels (r =0.459,P =0.036).The results of immunohistochemistry were similar to those of RT-PCR and Western blot.Overexpression of SphK1 and NF-κB in colon carcinoma was related with depth of invasion,distant and lymph node metastasis and Dukes'stages(all P values ＜0.05).The expression of SphK1 was also related with differentiation(P ＜ 0.05).Conclusions Overexpression of SphK1 and NF-κB may be involved in the pathogenesis and progression of colon carcinoma.Moreover,SphK1 and NF-κB may be correlated with the invasion and metastasis of colon carcinoma.

BACKGROUND:Unexplained infertility is associated with a higher abortion rate and lower fertilization rate,implantation rate,clinical pregnancy rate and cumulative live birth rate.It is urgent to establish a clinical prediction model related to infertility of unknown cause to solve the problems of clinical prognosis and individualized medical services,and finally achieve the purpose of increasing the cumulative live birth rate of patients with infertility of unknown cause. OBJECTIVE:To construct and verify the prediction model of high-quality blastocyst formation in patients with unexplained infertility during in vitro fertilization. METHODS:A total of 419 patients with unknown infertility who underwent in vitro fertilization in the Assisted Reproduction Department of Changzhou Maternal and Child Health Care Hospital from March 2017 to June 2022 were retrospectively analyzed,including 317 patients with high-quality blastocysts and 102 patients without high-quality blastocysts.A prediction model was established and used as the model group.The model group was sampled 1 000 times by the Bootstrap method as the validation group.Firstly,the univariate analysis was used to screen the influencing factors of high-quality blastocyst formation of unknown infertility,and the best matching factors were selected by the least absolute shrinkage and selection operator(LASSO)algorithm.Multiple factors were included in the progressive Logistic regression to find out the independent influencing factors and draw a column graph.Finally,the subject working curve,calibration curve,clinical decision curve and clinical impact curve were used to verify the differentiation and accuracy of the prediction model as well as the clinical application efficiency. RESULTS AND CONCLUSION:(1)Univariate analysis of the factors influencing the formation of high-quality blastocyst of unknown infertility were age,insemination method,antimullerian hormone level,basal follicle-stimulating hormone level,basal luteinizing hormone level,human chorionic gonadotropin injection day follicle-stimulating hormone level,human chorionic gonadotropin day estradiol level,progesterone level on human chorionic gonadotropin day,the number of high-quality cleavage embryo(day 3)and the number of blastocyst formation(P<0.05).(2)The best matching factors further screened by LASSO regression were age,insemination method,antimullerian hormone level,basal luteinizing hormone level,human chorionic gonadotropin injection day follicle-stimulating hormone level,human chorionic gonadotropin day estradiol level,the number of high-quality cleavage embryo(day 3)and the number of blastocyst formation(P<0.05).Multifactor stepwise Logistic regression results showed that independent influencing factors on the formation of high-quality blastocysts for unexplained infertility were age,insemination method,antimullerian hormone level,the number of high-quality cleavage embryo(day 3),and the number of blastocyst formation.(3)Receiver operating characteristic curve exhibited that the area under the curve was 0.880(0.834,0.926)in the model group and 0.889(0.859,0.918)in the validation group.It showed that the prediction model had good differentiation.The average absolute error of the calibration curve was 0.036,indicating that the model had good accuracy.The Hosmer-Lemeshow test showed that there was no statistical difference between the prediction probability of blastocyst formation and the actual probability of blastocyst formation(P>0.05).The clinical decision curve and clinical impact curve showed that the model group and the validation group had the maximum clinical net benefit when the threshold probability value was(0.16-0.96)and(0.08-0.93),respectively,and had better clinical application efficacy within the threshold probability range.These findings concluded that age,insemination method,antimullerian hormone,the number of high-quality cleavage embryos(day 3),and the number of blastocyst formation were independent factors influencing the formation of the fine blastocyst in patients with unexplained infertility.The clinical prediction model constructed by these factors has good clinical prediction value and clinical application efficiency and can provide a basis for clinical prognosis and intervention as well as the formulation of individual medical programs.

Objective:To investigate the value of CT radiomic model based on analysis of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer in differentiating stage T1-2 from stage T3-4 gastric cancer.Methods:This study was a case-control study. Totally 465 patients with gastric cancer treated in Affiliated People′s Hospital of Jiangsu University from December 2011 to December 2019 were retrospectively collected. According to postoperative pathology, they were divided into 2 groups, one with 150 cases of T1-2 tumors and another with 315 cases of T3-4 tumors. The cases were divided into a training set (326 cases) and a test set (139 cases) by stratified sampling method at 7∶3. There were 104 cases of T1-2 stage and 222 cases of T3-4 stage in the training set, 46 cases of T1-2 stage and 93 cases of T3-4 stage in the test set. The axial CT images in the venous phase during one week before surgery were selected to delineate the region of interest (ROI) at the primary lesion and the extramural gastric adipose tissue adjacent to the cancer areas. The radiomic features of the ROIs were extracted by Pyradiomics software. The least absolute shrinkage and selection operator was used to screen features related to T stage to establish the radiomic models of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer. Independent sample t test or χ2 test were used to compare the differences in clinical features between T1-2 and T3-4 patients in the training set, and the features with statistical significance were combined to establish a clinical model. Two radiomic signatures and clinical features were combined to construct a clinical-radiomics model and generate a nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate the efficacy of each model in differentiating stage T1-2 from stage T3-4 gastric cancer. The calibration curve was used to evaluate the consistency between the T stage predicted by the nomogram and the actual T stage of gastric cancer. And the decision curve analysis was used to evaluate the clinical net benefit of treatment guided by the nomogram and by the clinical model. Results:There were significant differences in CT-T stage and CT-N stage between T1-2 and T3-4 patients in the training set ( χ2=10.59, 15.92, P=0.014, 0.001) and the clinical model was established. After screening and dimensionality reduction, the 5 features from primary gastric cancer and the 6 features from the adipose tissue outside the gastric wall beside cancer established the radiomic models respectively. In the training set and the test set, the AUC values of the primary gastric cancer radiomic model were 0.864 (95% CI 0.820-0.908) and 0.836 (95% CI 0.762-0.910), and the adipose tissue outside the gastric wall beside cancer radiomic model were 0.782 (95% CI 0.731-0.833) and 0.784 (95% CI 0.702-0.866). The AUC values of the clinical model were 0.761 (95% CI 0.705-0.817) and 0.758 (95% CI 0.671-0.845), and the nomogram were 0.876 (95% CI 0.835-0.917) and 0.851 (95% CI 0.781-0.921). The calibration curve reflected that there was a high consistency between the T stage predicted by the nomogram and the actual T stage in the training set ( χ2=1.70, P=0.989). And the decision curve showed that at the risk threshold 0.01-0.74, a higher clinical net benefit could be obtained by using a nomogram to guide treatment. Conclusions:The CT radiomics features of primary gastric cancer lesions and the adipose tissue outside the gastric wall beside cancer can effectively distinguish T1-2 from T3-4 gastric cancer, and the combination of CT radiomic features and clinical features can further improve the prediction accuracy.