Objective To observe Hedgehog signaling pathways of liver cancer cell growth and the influence of the metastatic potential targeted inhibit Hedgehog.Methods Construction of Smo shRNA plasmid,The stable and low-expressed Smo-expressing HCC QGY-7701 cell line was screened after lipofection.The stable and low-expressed Smo-expressing HCC QGY-7701 cell line was screened,The cell cycle,apoptosis,invasion and metastasis of QGY-7701 cells were detected by Western blot,flow cytometry,CCK8 and transwell assay.Subcutaneous implantation of hepatocarcinoma cells in nude mice.Study on the growth and metastasis of hepatocarcinoma cells with low expression of Smo in.The ultrastructural changes of hepatoma cells with low expression of Smo were observed under electron microscope.Results RT-PCR and Western blot showed stable shR-Smo cell line was successfully constructed.Cell cycle test showed that compared with the control group,G0/G1 cells increased in shR-Smo,cells in S phase decreased;apoptosis,CCK8 and Transwell tests showed that Smo-gene silencing could significantly increase the apoptosis percentage of the hepatic cancer cells to (5.46％ ± 1.46％),proliferation activity decreasedand and the migration rates reduced to (7.82％ ±2.14)％;nude mice model showed that Smo-gene silencing could inhibit the growth of hepatocellular carcinoma cells in vivo,electron microscopy revealed that lysosomes increased significantly in Smo-gene silence cells.Conclusions Blocking Hh signaling pathways,liver cancer cells in vitro malignant degree of decline.Hedgehog in treating liver cancer have hidden meaning.

Objective To analyze the abnormal results of individual dose monitoring for radiation workers in Tongzhou District, Beijing, China, and to propose corresponding improvement measures. Methods Questionnaires were used to investigate the radiation workers who had abnormal dose monitoring results. Results In 2015—2019, among the 12 595 individual dose monitoring results for radiation workers in Tongzhou District, 23 were abnormal. The main type of work with abnormal monitoring results was medical diagnostic radiographer (69.60%). A total of 17 (73.90%) radiation workers had an exposure dose ranging from 1.25 mSv to 5 mSv. Conclusion The abnormal dose results were all from non-occupational exposure, mainly due to the dosimeter left in the workplace. The key to solving the problem is to further strengthen the education and training for radiation workers, to improve the institutional radiation protection management, and to supervise and inspect the relevant work strictly.

Objective:To establish an ultrasound radiomics model by integrating clinical, pathological, and conventional ultrasound features with radiomics characteristics, and to explore its clinical value in predicting endocrine resistance in hormone receptor(HR)-positive breast cancer.Methods:A retrospective analysis was performed on 478 patients with HR-positive breast cancer from January 2017 to December 2021 in the Second Affiliated Hospital of Harbin Medical University, of which 430 were resistant and 48 were sensitive. The clinical, pathological and immunohistochemical data and ultrasound images were collected.Firstly, the propensity score was used to process and match the data. Secondly, Logistic regression was used to screen clinical, pathological, and conventional ultrasound features associated with endocrine resistance. Then, PyRadiomics was used to extract the radiomic features of grayscale ultrasound images, and a series of methods such as Lasso regression were used to screen the radiomic features related to endocrine resistance. Seven machine learning methods such as random forest were used to build a radiomics model. Finally, clinical, pathological and ultrasound features were added to establish a clinical pathological model, a clinical pathological ultrasound model, a clinical pathological radiomics model and a combined model of the four features, and the model effectiveness was evaluated.Results:①Propensity score matching: 96 patients were matched, including 48 patients in the drug-resistant group and 48 patients in the sensitive group. ②Screening clinical pathological conventional ultrasound features related to endocrine resistance: lymph node metastasis, tumor diameter, posterior echo attenuation, and growth orientation were independent predictors of endocrine resistance (all P<0.05). ③Screening radiomics features related to endocrine resistance: 18 features such as Dependence Entropy. ④Establishing radiomics model: the machine learning model of random forest method (AUC=0.80) performed best. ⑤Radiomics model integrating clinical, pathological and conventional ultrasound features: the AUC of the clinical pathological model was 0.70, the AUC of the clinical pathological ultrasound model was 0.78, the AUC of the clinical pathological radiomics model was 0.82, and the AUC of the combined model was 0.86. Conclusions:The radiomics model established by the random forest method performs best in predicting endocrine resistance in HR-positive breast cancer. The model that integrates multiple features performs best in assessing endocrine resistance.which is expected to provide an objective basis for clinicians to predict endocrine resistance in HR-positive breast cancer.