To investigate the feasibility of in vitro inducing differentiation of bone marrow stromal cells (BMSCs) into neural stem cells and mature neural cells, and to offer reference for the application of BMSCs in the field of neuroscience, BMSCs were acquired from the marrow of dogs. Basic fibroblast growth factor (bFGF), all trans retinoic acid (RA) and glial cell line derived neurotrophic factor (GDNF) etc were used as proliferation or differentiation factors. Immunocytochemistry was employed to identify the cells at every culture stage. When BMSCs were proliferatively cultured for 24～72h, cleavage phase and cluster like clones appeared. On the 3rd day, some of the BMSCs derived cells started to express neuron specific enolase (NSE) or glial fibrillary acidic protein (GFAP). The same stage cells could be cloned, that is one of the characters of stem cells, when they were cultured in the proliferation medium. The neural cell modality like cells appeared on the 10th day after adding inducing factors into culture medium, which were proved by testing the express of NSE and GFAP. BMSCs can differentiate into neural stem cells and mature neural cells in vitro, and can be used as "seed cells" in the field of neuroscience.

To observe the growth, expansion and differentiation of the cultured bone marrow stroma cell (BMSC) from Macaca Lrus, BMSC isolated from adult Macaca Lrus were cultured with the culture medium confected by ourselves and were induced with some cytokines such as LIF and bFGF. The results showed that the BMSC could proliferate and generate Nestin positive clones when they were cultured in vitro. After subculture, these cells could grow rapidly and differentiated into neuron like cells and astrocyte like cells further, which expressed GFAP or NSE antigen respectively. Therefore, these BMSC possess renewal and differentiation abilities. On the other hand, the culture method we used in this experiment is suitable for culture of BMSC in vitro. The BMSC might be used as the seed cells of the neural stem cells.

Objective To analyze the characteristics of iatrogenic urerteral injury and summarize the experiences in prevention,diagnosis and treatment of iatrogenic urerteral injury. MethodsA review was made on the injurycauses,the injury locations,the treatment time,the methods of surgical procedures and the results of treatment in 17 patients with iatrogenic ureteral injury treated surgically from 1997 to 2003. Results Of 17 cases of iatrogenic ureteral injuries,gynecological,general surgical and urological procedures resulted in ureteral injuries in 12 cases (71%),four (24%) and one (6%),respectively. Of all the injuries,65% (11/17) appeared in the lower part of the ureter,18% (3/17) in the middle part of the ureter and 18% (3/17) in the upper part of the ureter. The main injury causes were ligation,partial ligation,complete transection and perforation,accounting for 29% (5/17),41% (7/17),24% (4/17) and 6% (1/17),respectively. Four cases were found during operation,nine at days 2-11 after operation and four were treated 3-6 months after injury. Treatment methods included end-to-end ureteral anastomosis in seven cases,ureteroneocystostomy in three,ureteral lithotomy in one,pure ureteral lysis in three and post-lysis double-J tube insertion in three. All patients were cured. The follow-up ranging from six months to three years showed no patients suffering from urinary tract infection,hydronephrosis or atrophy. Conclusions The location and type of injury determine the type of surgical repair. A thorough knowledge of pelvic anatomy and mastering the basic steps of diagnosis and treatment are critical for prevention and management of the iatrogenic urerteral injury.

Objective:To develop early gastric cancer (EGC) detection system of magnifying blue laser imaging (ME-BLI) model and magnifying narrow-band imaging (ME-NBI) model based on deep convolutional neural network, to compare the performance differences of the two models and to explore the effects of training methods on the accuracy.Methods:The images of benign gastric lesions and EGC under ME-BLI and ME-NBI were respectively collected. A total of five data sets and three test sets were collected. Data set 1 included 2 024 noncancerous lesions and 452 EGC images under ME-BLI. Data set 2 included 2 024 noncancerous lesions and 452 EGC images under ME-NBI. Data set 3 was the combination of data set 1 and 2 (a total of 4 048 noncancerous lesions and 904 EGC images under ME-BLI and ME-NBI). Data set 4: on the basis of data set 2, another 62 noncancerous lesions and 2 305 EGC images under ME-NBI were added (2 086 noncancerous lesions and 2 757 EGC images under ME-NBI). Data set 5: on the basis of data set 3, another 62 noncancerous lesions and 2 305 EGC images under ME-NBI were added(4 110 noncancerous lesions and 3 209 EGC images under ME-NBI and ME-BLI). Test set A included 422 noncancerous lesions and 197 EGC images under ME-BLI. Test set B included 422 noncancerous lesions and 197 EGC images under ME-NBI. Test set C was the combination of test set A and B (844 noncancerous and 394 EGC images under ME-BLI and ME-NBI). Five models were constructed according to these five data sets respectively and their performance was evaluated in the three test sets. Per-lesion video was collected and used to compare the performance of deep convolutional neural network models under ME-BLI and ME-NBI for the detection of EGC in clinical environment, and compared with four senior endoscopy doctors. The primary endpoint was the diagnostic accuracy of EGG, sensitivity and specificity. Chi-square test was used for statistical analysis.Results:The performance of model 1 was the best in test set A with the accuracy, sensitivity and specificity of 76.90% (476/619), 63.96% (126/197) and 82.94% (350/422), respectively. The performance of model 2 was the best in test set B with the accuracy, sensitivity and specificity of 86.75% (537/619), 92.89% (183/197) and 83.89% (354/422), respectively. The performance of model 3 was the best in test set B with the accuracy, sensitivity and specificity of 86.91% (538/619), 84.26% (166/197) and 88.15% (372/422), respectively. The performance of model 4 was the best in test set B with the accuracy, sensitivity and specificity of 85.46% (529/619), 95.43% (188/197) and 80.81% (341/422), respectively. The performance of model 5 was the best in test set B, with the accuracy, sensitivity and specificity of 83.52% (517/619), 96.95% (191/197) and 77.25% (326/422), respectively. In terms of image recognition of EGC, the accuracy of models 2 to 5 was higher than that of model 1, and the differences were statistically significant ( χ2=147.90, 149.67, 134.20 and 115.30, all P<0.01). The sensitivity and specificity of models 2 and 3 were higher than those of model 1, the specificity of model 2 was lower than that of model 3, and the differences were statistically significant ( χ2=131.65, 64.15, 207.60, 262.03 and 96.73, all P < 0.01). The sensitivity of models 4 and 5 was higher than those of models 1 to 3, and the specificity of models 4 and 5 was lower than those of models 1 to 3, and the differences were statistically significant ( χ2=151.16, 165.49, 71.35, 112.47, 132.62, 153.14, 176.93, 74.62, 14.09, 15.47, 6.02 and 5.80, all P<0.05). The results of video test based on lesion showed that the average accuracy of doctors 1 to 4 was 68.16%. And the accuracy of models 1 to 5 was 69.47% (66/95), 69.47% (66/95), 70.53% (67/95), 76.84% (73/95) and 80.00% (76/95), respectively. There were no significant differences in the accuracy among models 1 to 5 and between models 1 to 5 and doctors 1 to 4 (all P>0.05). Conclusions:ME-BLI EGC recognition model based on deep learning has good accuracy, but the diagnostic effecacy is sligntly worse than that of ME-NBI model. The effects of EGC recognition model of ME-NBI combined with ME-BLI is better than that of a single model. A more sensitive ME-NBI model can be obtained by increasing the number of ME-NBI images, especially the images of EGG, but the specificity is worse.

Objective:To explore the precise layered and tension-reducing sutures for skin pigmented mole surgery to promote tissue healing and reduce scar hyperplasia.Methods:From January 2019 to December 2021, the First Department of Surgery of the Civil Aviation General Hospital and Tenth Department of the Plastic Surgery Hospital of the Chinese Academy of Medical Sciences treated 56 patients with skin pigmented moles aged 18-52 years, with an average age of 26 years, including 30 males and 26 females. All patients in this group underwent surgical resection of skin pigmented moles, which reached the subcutaneous fat layer. The dermis and subcutaneous tissue under the skin incision were precisely buried and guided suture by using the middle common hole equal-chord and equal-arc buried guide suture with scale marks on both ends of the needle tip.Results:The incision width of skin tissue defect in this group of patients was less than 30 mm. After the suturing was completed, the tension between the tissues on both sides of the incision and the close-fitting of each layer of tissue on both sides of the incision without dead space were realized immediately. 55 cases achieved primary incision healing. After two years of follow-up observation, there was no scar hyperplasia, and the effect was satisfactory. In only one case, local incision was red and swollen due to suture reaction, and a small amount of scar hyperplasia appeared later.Conclusions:This submerged guided suture method is an effective surgical technique for reducing skin incision scars, and it is more suitable for small incisions with a skin incision length of less than 10 mm, which is difficult to achieve layered suture of the deep tissue of the incision with ordinary suture needles.

Objective:To compare the ability of deep convolutional neural network-crop (DCNN-C) and deep convolutional neural network-whole (DCNN-W), 2 artificial intelligence systems based on different training methods to dignose early gastric cancer (EGC) diagnosis under magnifying image-enhanced endoscopy (M-IEE).Methods:The images and video clips of EGC and non-cancerous lesions under M-IEE under narrow band imaging or blue laser imaging mode were retrospectively collected in the Endoscopy Center of Renmin Hospital of Wuhan University, for the training set and test set for DCNN-C and DCNN-W. The ability of DCNN-C and DCNN-W in EGC identity in image test set were compared. The ability of DCNN-C, DCNN-W and 3 senior endoscopists (average performance) in EGC identity in video test set were also compared. Paired Chi-squared test and Chi-squared test were used for statistical analysis. Inter-observer agreement was expressed as Cohen′s Kappa statistical coefficient (Kappa value).Results:In the image test set, the accuracy, sensitivity, specificity and positive predictive value of DCNN-C in EGC diagnosis were 94.97%(1 133/1 193), 97.12% (202/208), 94.52% (931/985), and 78.91%(202/256), respectively, which were higher than those of DCNN-W(86.84%, 1 036/1 193; 92.79%, 193/208; 85.58%, 843/985 and 57.61%, 193/335), and the differences were statistically significant ( χ2=4.82, 4.63, 61.04 and 29.69, P=0.028, =0.035, <0.001 and <0.001). In the video test set, the accuracy, specificity and positive predictive value of senior endoscopists in EGC diagnosis were 67.67%, 60.42%, and 53.37%, respectively, which were lower than those of DCNN-C (93.00%, 92.19% and 87.18%), and the differences were statistically significant ( χ2=20.83, 16.41 and 11.61, P<0.001, <0.001 and =0.001). The accuracy, specificity and positive predictive value of DCNN-C in EGC diagnosis were higher than those of DCNN-W (79.00%, 70.31% and 64.15%, respectively), and the differences were statistically significant ( χ2=7.04, 8.45 and 6.18, P=0.007, 0.003 and 0.013). There were no significant differences in accuracy, specificity and positive predictive value between senior endoscopists and DCNN-W in EGC diagnosis (all P>0.05). The sensitivity of senior endoscopists, DCNN-W and DCNN-C in EGC diagnosis were 80.56%, 94.44%, and 94.44%, respectively, and the differences were not statistically significant (all P>0.05). The results of the agreement analysis showed that the agreement between senior endoscopists and the gold standard was fair to moderate (Kappa=0.259, 0.532, 0.329), the agreement between DCNN-W and the gold standard was moderate (Kappa=0.587), and the agreement between DCNN-C and the gold standard was very high (Kappa=0.851). Conclusion:When the training set is the same, the ability of DCNN-C in EGC diagnosis is better than that of DCNN-W and senior endoscopists, and the diagnostic level of DCNN-W is equivalent to that of senior endoscopists.