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1.
Chinese Journal of Medical Instrumentation ; (6): 695-697, 2023.
Article in Chinese | WPRIM | ID: wpr-1010264

ABSTRACT

This study introduced a time-delay exposure system independent of the mobile digital radiography equipment. The system consisted of lithium battery, delay control circuit, micro electric motor and related auxiliary facilities. When the starting time was reached through the delay circuit, the motor pushed out the rod to squeeze the exposure button and completed the exposure. The accessories used in this system were easy to purchase and cheap. At the same time, the technology was mature and had good compatibility. The exposure success rate was high and the exposure effect was satisfactory. This time-delay exposure system had good practicability and popularization value.


Subject(s)
Radiographic Image Enhancement , Technology , Electric Power Supplies
2.
Acta Pharmaceutica Sinica B ; (6): 3925-3934, 2021.
Article in English | WPRIM | ID: wpr-922450

ABSTRACT

T cell immunoglobulin and ITIM domain (TIGIT) is a novel immune checkpoint that has been considered as a target in cancer immunotherapy. Current available bioassays for measuring the biological activity of therapeutic antibodies targeting TIGIT are restricted to mechanistic investigations because donor primary T cells are highly variable. Here, we designed a reporter gene assay comprising two cell lines, namely, CHO-CD112-CD3 scFv, which stably expresses CD112 (PVRL2, nectin-2) and a membrane-bound anti-CD3 single-chain fragment variable (scFv) as the target cell, and Jurkat-NFAT-TIGIT, which stably expresses TIGIT as well as the nuclear factor of activated T-cells (NFAT) response element-controlled luciferase gene, as the effector cell. The anti-CD3 scFv situated on the target cells activates Jurkat-NFAT-TIGIT cells through binding and crosslinking CD3 molecules of the effector cell, whereas interactions between CD112 and TIGIT prevent activation. The presence of anti-TIGIT mAbs disrupts their interaction, which in turn reverses the inactivation and luciferase expression. Optimization and validation studies have demonstrated that this assay is superior in terms of specificity, accuracy, linearity, and precision. In summary, this reliable and effective reporter gene assay may potentially be utilized in lot release control, stability assays, screening, and development of novel TIGIT-targeted therapeutic antibodies.

3.
Chinese Journal of Digestive Surgery ; (12): 555-563, 2021.
Article in Chinese | WPRIM | ID: wpr-883282

ABSTRACT

Objective:To investigate the clinical value of radiomics based on computed tomography (CT) examination in preoperative differential diagnosis of pancreatic serous cystadenoma (SCA) and mucinous cystadenoma (MCA).Methods:The retrospective case-control study was conducted. The clinicopathological and imaging data of 154 patients with pancreatic cystic neoplasms who were admitted to the First Affiliated Hospital, Zhejiang University School of Medicine from January 2012 to December 2019 were collected. There were 24 males and 130 females, aged (50±13)years. Of the 154 patients, 99 cases were diagnosed as SCA and 55 cases were diagnosed as MCA. All the 154 patients underwent plain and enhanced CT scan of pancreas before operation. The clinical characteristics, radiology features and radiomics features of all patients were collected to construct the clinical characteristics model, radiology model, radiomics model and fused model. The receiver operating characteristic (ROC) curve of each model was drawn, and those constructed models were evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Based on the optimal model, the nomogram was constructed. Observation indicators: (1) establishment and validation of clinical characteristics model; (2) establishment and validation of radiology model; (3) establishment and validation of radiomics model; (4) establishment and validation of fused model; (5) nomogram of fused model. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the Mann-Whitney U test. Count data were described as absolute numbers or percentages, and comparison between groups was analyzed using the chi-square test or Fisher exact probability. Results:(1) Establishment and validation of clinical characteristics model: 3 clinical characteristics, including age, symptoms and preoperative serum CA19-9, were selected using multinomial logistic linear regression analysis to construct the clinical characteristics model. Result of the multinomial logistic linear regression analysis was expressed by formula ①: clinical characteristics model score=0.635-0.007×age+0.054×clinical symptoms+0.108×preoperative serum CA19-9. The ROC curve for the test dataset of clinical characteristics model was drawn. The AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of clinical characteristics model were 0.611(95% confidence interval as 0.488?0.734, P<0.05), 56.6%, 66.7%, 56.3%, 41.5%, 78.4% for the training dataset and 0.771(95% confidence interval as 0.624?0.919, P<0.05), 77.8%, 63.1%, 88.5%, 80.1%, 76.7% for the test dataset, respectively. (2) Establishment and validation of radiology model: 5 radiology characteristics, including tumor location, the number of tumors, tumor diameter of cross section, lobulated tumor and polycystic tumor (more than 6), were selected using multinomial logistic linear regression analysis to construct the radiology model. Result of the multinomial logistic linear regression analysis was expressed by formula ②: radiology model score=?0.034+0.300×tumor location+0.202×the number of tumors+0.014×tumor diameter of cross section?0.251×lobulated tumor?0.170×polycystic tumor (more than 6). The ROC curve for the test dataset of radiology model was drawn. The AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of radiology model were 0.862(95% confidence interval as 0.791?0.932, P<0.05), 78.8%, 81.8%, 77.5%, 62.8%, 90.2% for the training dataset and 0.853(95% confidence interval as 0.713?0.994), P<0.05), 88.9%, 89.4%, 88.5%, 85.0%, 92.0% for the test dataset, respectively. (3) Establishment and validation of radiomics model: 4 categories of a total 1 067 radiomics features were extracted from 154 patients with pancreatic cystic neoplasms, including 7 first-order histogram features, 53 texture features, 848 wavelet features and 159 local binary pattern features. A total of 896 stable radiomics features were retained to construct the model, based on the condition of intraclass correlation coefficient >0.9. After selected by variance threshold and correlation coefficient threshold, 350 radiomics features were retained. Fifty synthetic radiomics features were constructed based on the original features in order to obtain potential radiomics features, and the total number of radiomics features was 400. After analyzed by the five-fold recursive feature elimination, 22 radiomics features were screened out, including 13 wavelet features, 7 synthetic radiomics features and 2 local binary pattern features. The support vector machine algorithm was used to construct the radiomics model. The penalty coefficient 'C' and parameter 'γ' of the radiomics model were 35.938 and 0.077, respectively. The kernel function of the radiomics model was 'radial basis function kernel'. The ROC curve of radiomics model using 5-fold cross validation was drawn. The average AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the radiomics model were 0.870 ( P<0.05), 83.1%, 81.8%, 83.8%, 73.8% and 89.2%, respectively. (4) Establishment and validation of fused model: the fused model was constructed after selecting the tumor location and lobulated tumor of radiology characteristics and radiomics score. Result of the multinomial logistic linear regression analysis was expressed by formula ③: fused model socre=?0.154+0.218×tumor location?0.223×lobulated tumor+0.621×radiomics score. The ROC curve for the test dataset of fused model was drawn. The AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of fused model were 0.893(95% confidence interval as 0.828?0.958, P<0.05), 83.7%, 81.8%, 84.5%, 71.1%, 90.9% for the training dataset and 0.966(95% confidence interval as 0.921?0.999, P<0.05), 91.1%, 84.2%, 96.2%, 94.1%, 89.3% for the test dataset, respectively. (5) Nomogram of fused model: the nomogram of fused model was illustrated with the Youden index of 0.416. Conclusion:The prediction model based on the radiomics signature and radiological features extracted from preoperative CT examination can make the differential diagnosis of pancreatic SCA from MCA.

4.
Chongqing Medicine ; (36): 1025-1028, 2015.
Article in Chinese | WPRIM | ID: wpr-460577

ABSTRACT

Objective To study the effects of apoptosis of the tumor cells in rabbit liver VX2 carcinoma after treatment by radiofrequency ablation(RFA) combined with trans arterial chemoembolization(TACE) and high‐frequency hyperthermia(HFH) . Methods Rabbit liver VX2 carcinoma model was established .Rabbit liver VX2 tumor models were divided into the following group:group A ,RFA+ TACE;group B ,RFA + TACE + HFH ;group C ,RFA + HFH ;group D ,TACE+ HFH .The changes of serum ALT was detected to realize the safety of the treatment .Cell apoptosis were detected by DNA agarose gel electrophoresis and terminal deoxynucleotidyltransferase‐mediated Dutp nick end‐labeling(TUNEL) assay ;SP immunohistochemistry ,Western blot and Real‐time quantitative PCR(RT‐PCR) were used to detect Caspase‐3 protein and mRNA expression levels .Results The changes of serum ALT in group B was significantly higher .Compared with other groups ,the apoptosis index in group B was increased marked‐ly(P<0 .05) .Western blot and RT‐PCR Caspase‐3 protein and mRNA levels in group B were higher than the other groups(P<0 .05) .Conclusion RFA+ TACE+ HFH can effectively kill tumor cells and promote apoptosis of tumor cells ,but ,at the same time ,can damage liver function .

5.
Journal of Southern Medical University ; (12): 463-467, 2014.
Article in Chinese | WPRIM | ID: wpr-356898

ABSTRACT

<p><b>OBJECTIVE</b>To study the changes in hypoxia-inducible factor (HIF1α, HIF2α) in the residual tumor cells in nude mice bearing hepatocellular carcinoma (HCC) following treatment with high-intensity focused ultrasound (HIFU).</p><p><b>METHODS</b>Thirty nude mice bearing human HCC received treatment with HIFU. At 1, 3, and 5 days and 1 and 2 weeks after the treatment, the mice were examined for pathological changes of the residual tumor with HE staining; SP immunohistochemistry, Western blotting and real-time quantitative PCR were used to detect the protein and mRNA expressions of HIF1α and HIF2α in the tumor.</p><p><b>RESULTS</b>HE staining revealed the presence of residual tumor cells and large necrotic areas after the treatment. Immunohistochemistry showed a gradual increment of HIF1α protein and mRNA expressions after the treatment, reaching the peak level at 3 days (P<0.05) followed by progressive reduction at 5 days and 1 and 2 weeks. HIF2α expressions at either the protein or mRNA levels exhibited no significant changes within 3 days after the treatment (P>0.05) but increased significantly at 5 days and 1 and 2 weeks (P<0.05).</p><p><b>CONCLUSION</b>The changes of HIF1α and HIF2α in the residual tumor after HIFU treatment in nude mice bearing HCC can be associated with tumor cell apoptosis and angiogenesis after the treatment.</p>


Subject(s)
Animals , Humans , Mice , Carcinoma, Hepatocellular , Metabolism , Pathology , Therapeutics , Hep G2 Cells , Hypoxia-Inducible Factor 1, alpha Subunit , Metabolism , Liver Neoplasms , Metabolism , Pathology , Therapeutics , Neoplasm, Residual , Metabolism , Pathology , Ultrasonic Therapy , Methods
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