In total, 607 bedridden patients aged 60 years and over at Tianlin community hospital and four other hospitals or at home-care in Shanghai were investigated with questionnaire, physical examinations and related laboratory tests. Contents of investigation include general situation, medical conditions, 24-hour recall of diet, intake of nutritional supplement preparation, and so on. As compare to the reference for nutrient intake of the elderly set in the Guide to Diet for Chinese Residents (2007), total nutritional intake was obviously insufficient and three nutrients in diet were imbalanced for most bedridden elderly patients. Intake of most nutrients (including proteins, dietary fiber, calcium, iron, zinc, selenium,copper, magnesium, phosphor, retinol equivalent, vitamin B1 , vitamin B2, vitamin C) was obviously low in bedridden elderly patients, with a larger gap from the requirements of the Dietary Guide.

Objective:To investigate the effect of siRNA interfering survivin gene on proliferation of human tongue cancer Tca8113 cells,and clarify the effect of survivin Tca8113 cells biomechanism.Methods: The Tca8113 cells at logarithmic growth phase were selected and divided into interference group,negative control group,and blank control group.The relative levels of survivin mRNA and survivin protein expression of 3 groups were detected by RT-PCR and Western blot assay,the inhibitory rate of proliferation of Tca8113 cells was checked by MTT method,the apoptotic rate was assessed by flow cytometry(FCM),the the migration ability of Tca8113 cells were detected by Wound healing assay.Results: Compared with control group,the survivin mRNA and protein expression was markedly down-regulated in Tca8113 cells following RNA interference treatment(P<0.05),the cell proliferation were down-regulated in interference group(P<0.05),the cell apoptotic rate were up-regulated in interference group(P<0.05),the migration ability was significantly decreased in interference group(P<0.05).Conclusion: The expression of siRNA-survivin can significantly inhibit the invasion in tongue cancer Tca8113 cells,indicating it might be a potential biological therapeutic target for tongue cancer.

Objective:To evaluate the effects of parathyroid autotransplantation on total central lymph node dissection and postoperative parathyroid functional recovery in endoscopic radical operation for thyroid carcinoma.Method:The data of 152 patients undergoing endoscopic radical operation for thyroid carcinoma are retrospectively analyzed. The incidence of postoperative hypoparathyroidism, serum PTH and Ca 2+ concentrations at different time points, and the number of lymph nodes cleared were counted. Results:The rate of temporary hypoparathyroidism in the experimental group was higher than that in the control group, and the incidence of permanent hypoparathyroidism was lower than that in the control group, with statistically significant differences ( χ2=6.243, P=0.029). Patient's PTH in the experimental group is significantly higher than that in the control group at 1 week, 1 , 3 , 6 and 12 months after operation, and the difference is statistically significant ( F=25.193, P<0.05); Ca 2+ concentration in experimental group is higher than that in control group at 1, 3, 6 and 12 months after operation, and the difference is statistically significant ( F=3.268, P=0.005); The average number of central zone lymph node dissection and positive lymph nodes per case in the experimental group was higher than that in the control group, and the difference was statistically significant ( t=2.000, P=0.047; t=2.014, P=0.046). Conclusion:In radical lumpectomy for thyroid cancer, parathyroid autotransplantation can effectively prevent permanent postoperative hypoparathyroidism while achieving a more complete lymph node dissection in the central region.

Objective:To develope a deep-learning-based auto-segmentation model to segment organs at risk (OARs) in head and neck (H&N) region and compare with atlas-based auto-segmentation software (Smart segmentation).Methods:The auto-segmentation model consisted of classification model and segmentation model based on deep learning neural network. The classification model was utilized to classify CT slices into six categories in the cranio-caudal direction, and then the CT slices corresponding to the categories for different OARs were pushed to the segmentation model respectively. The CT image data of 150 patients were used for auto-segmentation model training and building atlas library in Smart segmentation software. Another 20 patients were used as testing dataset for both auto-segmentation model and Smart segmentation software. Dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to evaluate the accuracy of two method, and auto-segmentation time cost was recorded. Paired Student′s t-test or non-parametric Wilcoxon signed-rank test was performed depending on result of normality test. Results:The DSC and HD of auto-segmentation model for brainstem, left eye, right eye, left optic nerve, right optic nerve, left temporal lobe, right temporal lobe, mandible, left parotid and right parotid were 0.88 and 4.41 mm, 0.89 and 2.00 mm, 0.89 and 2.12 mm, 0.70 and 3.00 mm, 0.80 and 2.24 mm, 0.81 and 7.98 mm, 0.84 and 8.82 mm, 0.89 and 5.57 mm, 0.70 and 11.92 mm, 0.77 and 11.27 mm respectively. The results of auto-segmentation model were better than those of Smart segmentation ( t=3.115-7.915, Z=-1.352 to -3.921, P<0.05) except left and right parotids. In addition, the speed of auto-segmentation model was 51.28% faster than that of Smart segmentation. Conclusions:In this study, the deep-learning-based auto-segmentation model demonstrated superior performance in accuracy and efficiency on segmenting OARs in H&N CT images, which was better than Smart segmentation software.