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Objective To study the influence situation of amoxicillin and clavulanate potassium for the postoperative infection and inflammatory indexes of patients with emergency intestinal obstruction operation by relaxation suture .Methods 80 patients with emergency intestinal obstruction operation by relaxation suture in department of general gurgery of our hospital from April 2014 to April 2016 were randomly divided into control group and observation group with 40 cases in each group, the control group were treated with routine treatment, the observation group were treated with amoxicillin and clavulanate potassium on the treatment of control group, then the postoperative infection rates, inflammatory related indexes before and after the treatment of two groups were compared.Results The postoperative infection rate of observation group was 5.00%,and it was obviously lower than the 17.50% of control group, the difference was significant(P<0.05); the serum inflammatory related indexes of two groups before the treatment were compared,the differences were not significant ;the serum anti-inflammatory indexes interleukin-4(IL-4), IL-10, IL-13 and pro-inflammatory indexes tumor necrosis factor-α(TNF-α), IL-6 at 1st, 2nd and 5th day after the treatment were all lower than those of control group,the pro-inflammatory index IL-2 were higher than those of control group, the differences were all significant(P<0.05).Conclusion The amoxicillin and clavulanate potassium can reduce the postoperative infection of patients with emergency intestinal obstruction operation by relaxation suture , and its control role for the related inflammatory indexes is better, so its application value is higher and is worthy of clinical promotion.

0.05). Conclusions There is no difference in immune responses to rectal cancer patients undergoing laparoscopic total mesorectal excision with anal sphincter preservation and those having open surgery.

Melanocytic lesions occur on the surface of the skin, in which the malignant type is melanoma with a high fatality rate, seriously endangering human health. The histopathological analysis is the gold standard for diagnosis of melanocytic lesions. In this study, a fully automated intelligent diagnosis method based on deep learning was proposed to classify the pathological whole slide images (WSI) of melanocytic lesions. Firstly, the color normalization based on CycleGAN neural network was performed on multi-center pathological WSI; Secondly, ResNet-152 neural network-based deep convolutional network prediction model was built using 745 WSI; Then, a decision fusion model was cascaded, which calculates the average prediction probability of each WSI; Finally, the diagnostic performance of the proposed method was verified by internal and external test sets containing 182 and 54 WSI, respectively. Experimental results showed that the overall diagnostic accuracy of the proposed method reached 94.12% in the internal test set and exceeded 90% in the external test set. Furthermore, the color normalization method adopted was superior to the traditional color statistics-based and staining separation-based methods in terms of structure preservation and artifact suppression. The results demonstrate that the proposed method can achieve high precision and strong robustness in pathological WSI classification of melanocytic lesions, which has the potential in promoting the clinical application of computer-aided pathological diagnosis.
Humans ; Deep Learning ; Melanoma/pathology* ; Diagnosis, Computer-Assisted ; Neural Networks, Computer ; Skin/pathology*