Objectives To systematically review the efficacy and safety of carbetocinversusoxytocin on the prevention of postpartum hemorrhage (PPH) for women undergoing vaginal delivery. Methods PubMed, The Cochrane Library, Web of Science, CBM, WanFang Data, CNKI and VIP databases were electronically searched to collect randomized controlled trials (RCTs) on carbetocinversusoxytocin on the prevention of PPH for women undergoing vaginal delivery from inception to January 2018. Two reviewers independently screened literature, extracted data and assessed risk of bias of included studies, then, meta-analysis was performed by using RevMan 5.3 and Stata 12.0 software. Results A total of 16 RCTs including 2 537 patients were included. The results of meta-analysis showed that: compared to oxytocin, carbetocin could reduce the amount of blood loss within 24h (MD=–107.68, 95%CI–130.21 to –85.15, P<0.000 01) and 2h (MD=–85.98, 95%CI–93.37 to –78.59,P<0.000 01), hemoglobin (Hb) within 24h after delivery (MD=–5.63, 95%CI–6.82 to –4.43,P<0.000 01), the occurrence of PPH (RR=0.46, 95%CI 0.32 to 0.66,P<0.000 01) and the requirement for additional uterotonic agents (RR=0.63, 95%CI 0.48 to 0.84,P=0.002). There was no significant difference in the risk of adverse effects between two groups. Conclusions Current evidence shows that carbetocin is superior to oxytocin in the prevention of PPH for women undergoing vaginal delivery, without increasing the adverse effects. Due to limited quality and quantity of the included studies, more high quality studies are required to verify above the conclusion.
The diagnosis of pancreatic cancer is very important. The main method of diagnosis is based on pathological analysis of microscopic image of Pap smear slide. The accurate segmentation and classification of images are two important phases of the analysis. In this paper, we proposed a new automatic segmentation and classification method for microscopic images of pancreas. For the segmentation phase, firstly multi-features Mean-shift clustering algorithm (MFMS) was applied to localize regions of nuclei. Then, chain splitting model (CSM) containing flexible mathematical morphology and curvature scale space corner detection method was applied to split overlapped cells for better accuracy and robustness. For classification phase, 4 shape-based features and 138 textural features based on color spaces of cell nuclei were extracted. In order to achieve optimal feature set and classify different cells, chain-like agent genetic algorithm (CAGA) combined with support vector machine (SVM) was proposed. The proposed method was tested on 15 cytology images containing 461 cell nuclei. Experimental results showed that the proposed method could automatically segment and classify different types of microscopic images of pancreatic cell and had effective segmentation and classification results. The mean accuracy of segmentation is 93.46%±7.24%. The classification performance of normal and malignant cells can achieve 96.55%±0.99% for accuracy, 96.10%±3.08% for sensitivity and 96.80%±1.48% for specificity.