BACKGROUND:Rapid development of spermatogonial stem cells is the new hope for assisting reproductive technologies,and the stability of the number and structure of the chromosome cultured in vitro is one of the important factors in usage.OBJECTIVE: To study the influential factors on G-band exhibition in mouse spermatogonial stem cells, so as to offer a technology to identify karyotype of stem cells in culture.DESIGN: Observational experiment.SETTING: Luzhou Medical College.MATERIALS: The experiment was conducted in the Laboratory of Medical Molecular Biology, Luzhou Medical College from March 2004 to April 2005. 10 days old male and female Kunming mice were provided by Department of Animal of Luzhou Medical College (number of license No. 17 of experimental animal quality administration in Sichuan province).There were low-sugar DMEM medium, 10 mg/L Mitomycin-C, IMDM medium, 1 ×10-5 mol/L colchicines (PBS allocation),7.5 mmol/L KCL, fixative (mixture glacial acetic acid with methanol in 1:3), Giemsa staining solution and 0.25% zymine.METHODS: Bone marrow was aspirated from the thigh bone of mouse for feeder layer cells preparation. Cells from the male mice testis of 7-8 days after birth were prepared and made into cell suspension. After adjusting the cell density to 3×105 L-1, they were inoculated into the feeder layer of bone marrow stromal cells. Cells were cultured at 37 ℃ in CO2 incubator containing CO2 of 0.05 volume fraction and 70% humidity. The proliferation groups of stem cells cultured 15-20 days were selected, stirred and spread, and then treated with colchicine for 4-6 hours. Cell suspension was collected,and then stained after hypotonic treatment. The cells whose chromosomes were dispersed well and in metaphase were selected, and number of chromosomes was counted, and then the morphology of chromosomes was observed.MAIN OUTCOME MEASURES: Culture of bone marrow stromal cells and spermatogonial stem cells and coloration and count of chromosomes.RESULTS: The karyotype of spermatogonial stem cells was the same as the body cells of normal mouse, which showed granule or rod-shape. Karyotype was 20 pairs, 40 bars. Three kinds of chromosome morphology could be observed under oil immersion lens. The first type was condensed, which could be counted in total, but the band could not be seen.The second type was chromatosome that spread completely in the center of equatorial plate and were in metaphase. In this phase, total numbers and band were seen clearly. Last type-chromosomes had already folded and moved towards two poles and concentrated gradually, the total number of chromosomes could be counted, but bending could not be seen clearly.CONCLUSION:Many factors can affect the karyotype of spermatogonial stem cells, including the phase of cell division,effect of hypotonic solution, diffusion of the cell when dropping slides, concentration of trypsin and digestion time, etc.

Objective:To explore the personnel arrangement in the outpatient pharmacy by calculating working hour to provide ref-erence for the rational staffing in hospital. Methods:The daily work content and working hour of 18 pharmacists in the outpatient phar-macy of a large general hospital from January to March in 2013 were following-up observed and recorded using the working hour meas-urement. The data were input the EXcellsoftware to establish the database, and the workload in various positions was collected and sorted. The obtained relative parameters were used to calculate the needed worker number on the basis of manpower planning model. Results:The research confirmed the mean operation time for 9 work programs in the outpatient pharmacy, and the time for drug dispen-sing and distributing was detailed. The needed number of pharmacists was 13. 29 according to the calculation, plus the officer-in-charge and sanitation workers, the total number was 15. 29(approx. 16). Conclusion:The working hour measurement can scientifically de-termine the time for each job, and the workload should be used as the foundation for configuring personnel qualification and the number in outpatient pharmacy.

Objective:To investigate the value of preoperative prediction of Ki-67 expression status in breast cancer based on multi-phase enhanced MRI combined with clinical imaging characteristics prediction model.Methods:This study was retrospective. A total of 213 breast cancer patients who underwent surgical treatment at Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College between June 2016 and May 2017 were enrolled. All patients were female, aged 24-78 (51±10) years, and underwent routine breast MRI within 2 weeks prior to surgery. According to the different Ki-67 expression of postoperative pathological results, patients were divided into high expression group (Ki-67≥20%, 153 cases) and low expression group (Ki-67<20%, 60 cases). The radiomic features of breast cancer lesions were extracted from phase 2 (CE-2) and phase 7 (CE-7) images of dynamic contrast enhanced (DCE)-MRI, and all cases were divided into training and test sets according to the ratio of 7∶3. The radiomic features were first selected using ANOVA and Wilcoxon signed-rank test, followed by the least absolute shrinkage and selection operator method regression model. The same method of parameters selection was applied to clinical information and conventional imaging features [including gland classification, degree of background parenchymal enhancement, multifocal/multicentric, lesion location, lesion morphology, lesion long diameter, lesion short diameter, T 2WI signal characteristics, diffusion-weighted imaging (DWI) signal characteristics, apparent diffusion coefficient (ADC) values, time-signal intensity curve type, and axillary lymph nodes larger than 1 cm in short axis]. Support vector machine (SVM) was then used to construct prediction models for Ki-67 high and low expression states. The predictive performance of the models were evaluated using receiver operating characteristic (ROC) curves and area under cueve(AUC). Results:Totally 1 029 radiomic features were extracted from CE-2 and CE-7 images, respectively, and 9 and 7 best features were obtained after selection, respectively. And combining the two sets of features for a total of 16 features constituted the CE-2+CE-7 image best features. Five valuable parameters including lesion location, lesion short diameter, DWI signal characteristics, ADC values, and axillary lymph nodes larger than 1 cm in short axis, were selected from all clinical image features. The SVM prediction models obtained from the radiomic features of CE-2 and CE-7 images had a high AUC in predicting Ki-67 expression status (>0.70) in both the training set and the test set. The models were constructed by combining the CE-2, CE-7, and CE-2+CE-7 radiomic features with clinical imaging features, respectively, and the corresponding model performance in predicting Ki-67 expression status was improved compared with the models obtained by using the CE-2, CE-7, and CE-2+CE-7 radiomic features alone. The SVM prediction model obtained from CE-2+CE-7 radiomic features combined with clinical imaging features had the best prediction performance, with AUC of 0.895, accuracy of 84.6%, sensitivity of 87.9%, and specificity of 76.2% for predicting Ki-67 expression status in the training set and AUC of 0.822, accuracy of 70.3%, sensitivity of 76.1%, and specificity of 55.6% in test sets.Conclusion:The SVM prediction model based on DCE-MRI radiomic features can effectively predict Ki-67 expression status, and the combination of radiomic features and clinical imaging features can further improve the model prediction performance.