Objective To explore the application value of preoperative methylene blue staining in locating for the operation of intraspi-nal tumors. Methods The clinical data of patients with intraspinal tumors from September 2010 to September 2012 in our hospital were ret-rospectively analyzed. The patients were divided into tag group and control group according to whether stained by methylene blue or not. The operation time( min) ,intraoperative hemorrhage,the rate of total resection of tumor,spinal instability rate,tumor recurrence rate,and reopera-tion rate of two groups were compared. Results The operation time of tag group was significantly shorter than that of the control group. The amount of intraoperative bleeding was significantly less than that of in control group, the differences were statistically significant(P<0. 05). The total resection rate of tumor was significantly higher than that in control group,the differences were statistically significant(P<0. 05). The spinal instability rate,tumor recurrence rate and operation rate of patients within 1 year in two groups were not significant. Conclusion The methylene blue method is simple and convenient,and provides favorable conditions for the operation,which reduces the operation time and intraoperative hemorrhage,increases the rate of complete tumor resection. There was no difference in recurrence rate,operation rate and the stability of the spine within 1 year compared to traditional method.

Objective To investigate the evaluation index of melasma staging by clinical manifestations and non-invasive skin detection technology.Methods A total of 195 patients with a clinical diagnosis of melasma were enrolled from the First Affiliated Hospital of Kunming Medical University.The skin with lesion enlarged,color darker,erythema,red occured after scratching or lesion faded after compressing with glass belonged to the active stage;on the contrary,it was in the stable stage.Reflectance confocal microscopy (RCM),dermoscopy,Mexameter 18 and LAB were used to observe skin lesions of different stage of melasma.Results There were 115 patients (59.0 ％) in the active stage of melasma and 80 patients (41.0 ％) in the stable stage.DMA score in active stage 35.08± 10.59 were significantly higher than that of the stable stage 15.06-4-9.20 (P＜0.05).There were statistically significant difference in the quantity of inflammatory cell and blood vessels between two stages of melasma (P＜0.05).Erythema index (EI) in active stage of melasma 376.35±61.39 were high-er than that of the stable stage 320.364± 62.40 (P＜0.05).A-value in active stage of melasma 13.28± 1.75 were higher than that of the stable stage 12.34± 1.78 (P＜0.05).However,there were no siginificant differences in the quantity of melenin,melanin index (MI),L-value and B-value.Conclusions Melasma could be divided into active stage or stable stage,respectively,according to its clinical manifestations.DMA score,quantity of inflammatory cells and blood vessels,EI and A-value could be used as the reference index of melasma staging.

Objective To observe the value of deep learning combine with radiomics based on MRI for evaluating H3 K27 status of midline gliomas.Methods Totally 127 patients with diffuse midline glioma H3 K27-altered(H3-DMG)and 127 patients with midline glioblastoma(GBM)without H3 K27 mutation were retrospectively enrolled.The patients were randomly divided into training set(n=204)and test set(n=50)at the ratio of 8:2.U-Net neural network visual and radiomics features of tumors were extracted based on MRI,and a deep learning radiomics model was established,its value for evaluating H3 K27 status was observed.Results Based on training set,0.500 was obtained as the security score partition value for the model classification task.In test set,the median safety score of the obtained deep learning radiomics model for evaluating H3 K27 status of H3-DMG and GBM was 0(0,0)and 0.999(0.616,1.000),respectively,for the former was lower than for the latter(Z=-5.114,P＜0.001).The sensitivity,specificity,accuracy and area under the curve of deep learning radiomics model for evaluating H3 K27 status in training set was 93.14％,81.37％,87.25％and 0.953(95％CI[0.923,0.976]),respectively,while was 88.00％,80.00％,84.00％and 0.922(95％CI[0.829,0.986])in test set,respectively.Conclusion Deep learning radiomics based on MRI could accurately evaluate H3 K27 status of midline gliomas.