AIM:To construct eukaryotic expression vector expressing double shRNA sections targeting Survivin gene.METHODS: Eukaryotic expression vector expressing double shRNA sections targeting Survivin gene were designed and chemically synthesized.They were directionally inserted into plasmid pGenesil-1 with respectively U6 promoter and termination code,the common green fluorescence protein(EGFP) gene and Neo gene. In this way,the vector of pGenesil-1 shRNA containing 2 sections of Survivin shRNA were constructed and they were transfected into the pancreatic cancer cell Bx-PC3.Transfection was detected by fluorescence microscope.The inhibition expression of Survivin mRNA was measured by RT-PCR.RESULTS: HE1 and HE2 plasmids were identified by the biocatalyst cut which confirmed the exactitude and were analyzed by the sequence analysis which verified the perfect clone plasmid inserted by them.CONCLUSION: A eukaryotic expression vector of double short hairpin RNA for Survivin gene is successfully constructed.The pancreatic cancer cells Bx-PC3 succeed to be transfected and expression of Survivin mRNA is inhibited obviously.

This thesis found the female homicide cases present its own characteristics because of differences in psychological, physiology characteristics, character between males and females through the statistical analysis of age distribution, the relationship between criminal suspects and victims, the classification of crime scenes, injury tools and the mortal wound positions of 116 female homicide cases in Lianyungang between 1993 to 2014. Mostly, the relationship between criminal suspects and victims is family, especially couple or valentine; the crime scenes are frequently indoor, especially bedrooms. They always choose production and life tools when victims are in deep sleep or drunk time to make the victims asphyxia, posioning or drowning. The injuries focus on vitals and usually hit many times lead to mortal wound, agonal trauma and postmortem injury.

Objective:To explore the value of machine-learning models based on magnetic resonance imaging(MRI)radiomics features for the preoperative prediction of the blood supply in pituitary neuroendocrine tumors.Methods:A retrospective analysis was performed on the clinical and imaging data of 136 patients with pathologically confirmed pituitary neuroendocrine tumors(diameter>10 mm)from April 2013 to April 2023 at Yi Jishan Hospital of Wannan Medical College.Based on the intraoperative findings,the patients were assigned into richly vascularized(n=50)and normally vascularized(n=86)groups.All patients were allocated randomly in a 7:3 ratio into a training(n=96)or a validation group(n=40).Three machine-learning algorithms,multivariate Logistic regression(LR),random forest(RF),and support vec-tor machine(SVM),were used to establish radiomics prediction models.Receiver operating characteristic(ROC)curves were plotted to eval-uate the diagnostic performance of the models;decision curve analysis(DCA)was used to assess the net clinical benefit of the models.Res-ults:The clinical model achieved areas under the ROC curve(AUC)of 0.74 and 0.82 in the training and validation groups,respectively.The radiomics models using T1-weighted imaging(WI),T2WI,T1WI-enhanced,and combined sequences achieved AUCs of 0.80,0.84,0.82,and 0.84 in the training group and 0.82,0.80,0.85,and 0.83 in the validation group,respectively.The LR,RF,and SVM models had AUCs of 0.85,0.87,and 0.84 in the training group and 0.85,0.85,and 0.83 in the validation group,respectively.All radiomics models demonstrated great-er diagnostic efficacy than the clinical model.DCA indicated that the LR,SVM,and combined-sequence models achieved good net clinical be-nefits;the LR model showed the best results.Conclusions:Machine-learning models based on MRI radiomics exhibit high predictive value,surpassing the clinical judgment of radiologists based on MRI images alone,and offer a favorable net clinical benefit.

Objective:To explore the expression and clinical significance of miR-143 in papillary thyroid cancer (PTC) .Methods:Tumor samples and adjacent tissues from 52 patients with PTC were obtained from Jan. 1st, 2018 to Mar. 31st, 2018 in Thyroid Surgery Department of the Affiliated Yantai Yuhuangding Hospital of Qingdao University. Quantitative reverse-transcriptase PCR (RT-qPCR) was used to measure the expression of miR-143 in those subjects. In addition, the relationship between the expression levels of miR-143 and the clinicopathological characteristics was analyzed.Results:RT-qPCR indicated that the expression of miR-143 was down-regulated in PTC, which was significantly lower than that in adjacent tissues ( t=-21.39, 95% CI: 18.20-15.07, P<0.001) . Low expression of miR-143 was related to the number of lymph node metastasis ≥3 in central compartment ( t=10.13, P=0.012) and lateral neck lymph node metastasis ( t=-4.67, P<0.001) . Conclusion:Downregulation of miR-143 in PTC is linked to the metastasis of PTC and may be a potential target for therapeutic intervention.

Objective To explore the value of dynamic nomogram constructed by multi spiral computed tomography(MSCT)features combined with inflammatory indicators in predicting the status of microvascular invasion(MVI)of hepatocellular carcinoma(HCC)before surgery.Methods The clinical and imaging data of 137 patients with postoperative pathologically confirmed HCC were analyzed retrospectively.According to the status of the MVI,they were divided into positive group(44 cases)and negative group(93 cases).Multivariate logistic regression analysis was used to screen independent risk factors for predicting the MVI status of HCC patients,and a joint prediction model was constructed,which was displayed in the form of a dynamic nomogram.The receiver operating characteristic(ROC)curve,calibration curve and Hosmer-Lemeshow test were used to evaluate the diagnostic efficiency,calibration and goodness of fit of the model,Akaike information criterion(AIC)and Bayesian information criterion(BIC)were used for comparison between the models,and a 5-fold cross-validation and decision curve analysis(DCA)were also used to evaluate the stability and clinical applicability of the model.Results Multivariate logistic regression analysis showed that necrosis and delayed-phase enhancement(DEd),and alkaline phosphatase to lymphocyte ratio(ALR)were independent risk factors for predicting MVI status in HCC patients.The area under the curve(AUC)of the dynamic nomogram was 0.721,with the sensitivity of 0.705 and the specificity of 0.656.The AIC and BIC values were 152.372 and 158.212,respectively.The calibration curve and the Hosmer-Lemeshow test showed that the model had a high degree of calibration and goodness of fit(χ2=2.372,P=0.967),the average AUC of the 5-fold cross-validation was 0.787,and the DCA showed that the nomogram model had a good clinical applicability.Conclusion The dynamic nomogram model constructed by MSCT features combined with inflammatory indicators is feasible to predict the MVI status of HCC patients before surgery,and the dynamic nomogram can directly generate the prediction results of different individuals.

Objective To investigate the value of a nomogram model based on contrast-enhanced CT radiomics in predicting the histological type of thymoma.Methods A total of 154 patients(101 in low-risk group and 53 in high-risk group)with thymoma confirmed by pathology were retrospectively selected.The cases were randomly divided into training set(n=107)and validation set(n=47)at a ratio of 7∶3.The three-dimensional volume of interest(VOI)of the whole lesion on the image from the arterial phase of contrast-enhanced CT was manually delineated,and the radiomics features were extracted.Based on the selected radiomics features,the radiomics model was constructed and the model Radiomics score(Radscore)was calculated.Clinical risk factors were screened to construct a clinical model,and a nomogram model was constructed by fusing Radscore and clinical risk factors.The receiver operating characteristic(ROC)curve,area under the curve(AUC),accuracy,sensitivity and specificity were compared to analyze the predictive efficacy and difference of different models for high-risk and low-risk thymoma.The decision curve and calibration curve were drawn to evaluate the clinical value and fitting performance of the nomogram model.Results Eleven radiomics features were selected to construct the radiomics model,and five clinical risk factors[myasthenia gravis(MG),morphology,border,surrounding tissue invasion and CT value in arterial phase]were used to construct the clinical model.In the training set,the AUC of the nomogram model(0.88)was higher than that of the radiomics model(0.80)and the clinical model(0.79),and the difference was statistically significant(Z=2.233,2.713,P=0.026,0.007,respectively).In the validation set,the AUC of the nomogram model was higher than that of the radiomics and clinical models,but the difference was not statistically significant.The calibration curve showed that the nomogram model had good fitting performance,and the decision curve showed that the nomogram model had high clinical benefit.Conclusion The nomogram model based on contrast-enhanced CT can effectively predict high-risk and low-risk thymoma,which is helpful to guide clinicians to make relevant decisions.

Purpose Explore the predictive value of nomograms based on enhanced CT radiomics for invasiveness of thymic epithelial tumor.Materials and Methods The clinical and imaging data from 155 cases confirmed with thymic epithelial tumors at the First Affiliated Hospital of Wannan Medical College from January 2015 to January 2023 were retrospectively collected.All cases were randomly divided into training(n=108)and validation(n=47)groups in a 7∶3 ratio.The radiomics features from venous phase images were extracted.The least absolute shrinkage and selection operator algorithm for dimensionality reduction were utilized to establish radiomics labels and calculate the Rad-score.Univariate and multivariate regression analyses were conducted to identify independent risk factors.Imaging feature models,Rad-score and imaging omics clinical combined model were constructed to plot the corresponding nomograms.The diagnostic performance and clinical benefits of the models were evaluated via receiver operating characteristic curves and decision curves.The DeLong test was applied to compare area under the curve differences between models and used calibration curves to assess nomograms calibration.Results 16 optimal image omics features were selected by dimensionality reduction.Logistic regression analysis showed that tumor morphology(OR=2.932,P=0.025),peripheral tissue invasion(OR=11.461,P=0.005)and Rad-score(OR=255.27,P=0.002)were independent risk factors.The area under the curve in the training set and the verification set were 0.852 and 0.831,respectively.Compared with the image feature model and Rad-score in the training set,the differences were statistically significant(Z=3.607,2.270,P<0.05).The threshold probability of the column chart model training set was between 0.08 and 0.88 for clinical benefit.Conclusion The combined model nomograms based on enhanced CT radiomics and clinical features can effectively predict thymic epithelial tumor invasiveness and assist clinicians in formulating precise treatment plans before surgery.

Objective:To investigate the application of combined deep learning and radiomic features derived from enhanced arterial phase CT imaging with clinical data to differentiate between T2 and T3 staging in patients with esophageal cancer.Methods:A retrospective study was conducted using clinical and CT data from 388 patients with pathologically confirmed esophageal cancer treated at The First Affiliated Hospital of Wannan Medical College between May 2015 and April 2024.The dataset was randomly divided into a training set(271 cases)and validation set(117 cases)in a 7:3 ratio.Radiomic and deep learning features were extracted from enhanced arterial phase CT images.The least absolute shrinkage and selection operator algorithm was employed for feature reduction and selection,leading to the development of radiomic(Radscore)and deep learning(Deepscore)scores.Univariate and multivariate Logistic regression analyses were conducted to identify independent risk factors,and clinical,radiomic,deep learning,and combined models were constructed.A nomogram was gener-ated for the combined model.The diagnostic performance of the models was evaluated using the area under the receiver operating charac-teristic curve(AUC)and compared using the DeLong test.Clinical net benefit was assessed through decision curve analysis,and model calib-ration was evaluated using calibration curves.Results:Nine radiomicand 12 deep learning features were selected after dimensionality reduc-tion.Multivariate Logistic regression identified tumor length,boundary,Radscore,and Deepscore as independent risk factors for distinguish-ing between T2 and T3 staging.In the training set,the AUC of the combined model was 0.867,which was significantly higher than that of the clinical(0.774,P<0.001),radiomic(0.795,P<0.001),and deep learning(0.821,P=0.001)models.In the validation set,the AUC of the com-bined model was 0.810,which was significantly higher than that of the clinical(0.653,P=0.002),radiomic(0.719,P=0.033),and deep learn-ing(0.750,P=0.009)models.The decision curve analysis indicated that the combined model provided the highest clinical benefit in both datasets.The calibration curves demonstrated a good fit for both datasets(P=0.084,0.053).Conclusion:The integration of deep learning and radiomic features obtained from enhanced arterial phase CT images with clinical data offers a reliable method for accurately distinguishing between preoperative T2 and T3 staging in esophageal cancer,thereby supporting clinical decision-making for treatment planning.

Objective:To explore the clinical significance of recurrent laryngeal nerve inlet zone(RLNIZ) lymph node metastasis in papillary thyroid cancer(PTC).Methods:The clinical data of the clinicopathologic characteristics of 738 cases with papillary thyroid cancer at our centers from Jul 2017 to Jun 2018 was retrospectively reviewed. 108 cases with RLNIZ lymph node dissection for pathological examination were included. The relationship between metastasis of RLNIZ lymph node and clinicopathologic characteristics was analyzed.Results:RLNIZ lymph node was detected in 12.3%(91/738)cases, the mean lymph node number in RLNIZ was 1.5±0.7, and 30.8%(28/91) cases suffered RLNIZ lymph node metastasis. RLNIZ lymph node metastasis(LNM) is associated with tumor size( P=0.028), capsular invasion( P=0.019), No. of central compartment LNM( P<0.001) and lateral neck LNM( P<0.001). No. of central compartment LNM was found to be the independent risk factor of RLNIZ lymph node metastasis. The incidence of dysphagia and inferior parathyroid damage was 0.9%(1/108)respectively. Conclusions:RLNIZ lymph node metastasis is common among PTC patients , therefore, RLNIZ lymph node should be routinely removed especially in patients with tumor size over 1cm、suspected capsular invasion and lateral neck lymph node metastasis confirmed by preoperative imaging examination.