Objective To observe the value of apparent diffusion coefficient(ADC)for quantitatively evaluating progressive muscle injury of rabbit limbs in early stage of high-voltage electrical burn.Methods Twenty healthy adult rabbits were selected to establish limb high-voltage electrical burn models,which were randomly divided into 0.5,24,48 and 72 h groups(each n=5).MR diffusion weighted imaging(DWI)was collected for each group at 0.5,24,48 and 72 h after modeling,and the injured core muscles of the right hind limb and the normal muscles of the contralateral limb were taken for HE staining.The muscle's ADC,muscle fiber density(MFD)and muscle fiber diameter(D)values at the injured core of current entry and exit were compared,and those of normal muscle were also analyzed.The correlations of ADC values in injured core muscle and MFD or D values were investigated.Results There were significant differences of ADC values of injured core muscle at both the entry and exit and normal muscle,also of ADC values of injured core muscle at the entry and exit within each group(all P＜0.05).ADC values of injured core muscle at the entry and exit decreased with time going(all P＜0.05),but ADC values of normal muscle were not significantly different among different time points(P＞0.05).MFD values of injured core muscle at the entry and exit decreased with time going(all P＜0.05),while MFD values of the normal muscle,D values of the injured core muscle at the entry and exit and normal muscle were not significantly different among time points(all P＞0.05).ADC value of the injured core muscle was positively correlated with MFD value and negatively correlated with D value(rs=0.846,r=-0.507,both P＜0.05).Conclusion ADC could quantitatively evaluate the progressive muscle injury of rabbit limbs in early stage of high-voltage electrical burn.

Objective:To explore the overall modulatory effect of the intermittent theta burst stimulation(iTBS),a novel non-invasive brain stimulation technique,on the topology of the brain functional network in stroke patients. Method:Sixteen patients with stroke were recruited.Based on their brain resting-state functional magnetic reso-nance images,the changes of the brain network topological properties,including the clustering coefficient,characteristic path length,local and global efficiency,"small-world",assortativity and hierarchy,were ana-lyzed before and after one session of left M1 iTBS. Result:The clustering coefficient,local efficiency and normalized hierarchy coefficient decreased significantly after one session of the iTBS,the global efficiency decreased near significantly,and the σ value of"small-world"increased close to significantly;while the other topological properties showed no significant change fol-lowing the iTBS intervention. Conclusion:The findings of the present study indicated that the left M1 iTBS may modulate the global net-work topological characteristics of the brain functional network in patients with stroke.

Objective:To evaluate the effect of deep learning based on DWI and fluid attenuated inversion recovery (FLAIR) to construct a prediction model of the onset time in acute stroke.Methods:A total of 324 cases of acute stroke with clear onset time, from January 2017 to May 2020 in Nanjing First Hospital, were retrospectively enrolled and analyzed. The patients were divided into a training set of 226 patients and a test set of 98 patients according to the complete randomization method using a 7∶3 ratio, and the patients were divided into ≤ 4.5 h and >4.5 h according to symptom onset time in each group. The acute infarction areas on DWI and the corresponding high signal area on FLAIR were manually outlined by physician. Using the InceptionV3 model as the basic model for image features extraction, the deep learning prediction model based on single sequence (DWI, FLAIR) and multi sequences (DWI+FLAIR) were established and verified. Then the area under curve (AUC), accuracy of human readings, single sequence model and multi sequence model in predicting the acute stroke onset time from imaging were compared.Results:DWI-FLAIR mismatch was found in 94 cases (94/207) of patients with symptom onset time from imaging ≤ 4.5 h, while in 28 cases (28/117) of patients with symptom onset time from imaging >4.5 h. ROC analysis showed that the AUC of DWI-FLAIR mismatch in predicting acute stroke onset time from imaging was 0.607, and the accuracy was 60.2%. The prediction model of deep learning based on single sequence showed that the AUC of FLAIR was 0.761 and the accuracy was 71.4%; the AUC of DWI was 0.836 and the accuracy was 81.6%. The AUC of predicting stroke onset time based on the multi-sequence (DWI+FLAIR) deep learning model was 0.852, which was significantly better than that of manual identification ( Z = 0.617, P = 0.002), FLAIR sequence deep learning model ( Z = 2.133, P = 0.006) and DWI sequence deep learning model ( Z = 1.846, P = 0.012). Conclusion:The deep learning model based on DWI and FLAIR is superior to human readings in predicting acute stroke onset time from imaging, which could provide guidance for intravenous thrombolytic therapy for acute stroke patients with unknown onset time.

Objective:To evaluate the efficacy of chemotherapy for advanced biliary tract carcinoma and the factors that influence sur-vival. Methods:A total of 91 cases of advanced biliary tract carcinoma from January 2010 to April 2015 were enrolled in our study. The patients' characteristics, chemotherapy regimens, and effects were analyzed. Results:We enrolled 56 males and 35 females with a me-dian age of 57 years. A total of 90 patients were assessable for their responses to first-line chemotherapy. A total of 69 patients re-ceived the GP regimen, whereas 21 patients received some other regimens. The disease control rate (DCR), median progression free survival (mPFS), and median overall survival (mOS) were 68.1%versus 52.4%, 5.10 months versus 2.50 months (P=0.025), and 13.00 months versus 7.20 months, respectively. Only 31 patients received S-1 based regimens, and 12 patients received some other regi-mens as second-line chemotherapy. The DCR, median PFS, and median OS showed no statistical differences. Only four patients re-ceived S-1 based regimen plus bevacizumab as second-line chemotherapy (median PFS 5.3 months;median OS 7 months). Hematologi-call toxicity was the most common side effect in the first-line GP regimen. The side effects of the S-1 based chemotherapy regimen was relatively less. Conclusion:The GP regimen is an effective first-line chemotherapy for advanced biliary tract carcinoma, whereas S-1 ap-pears as an effective second-line chemotherapy drug. Bevacizumab-based regimens may be effective and require further validation.