Objective To investigate the clinical efficacy of polysaccharide iron combined with folic acid in treatment of anemia in gestation women and its effect on neonatal outcomes.Methods 82 cases of pregnant women with iron deficiency anemia were selected and randomly divided into two groups, each had 41 cases.All patients received discontinuation of iron supplementation two weeks pre-treatment, control group received compound ferrous sulfate and folic Acid Tablets (4 tablets, tid), the treatment group received with Iron Polysaccharide Complex Capsules (0.15 g~0.30 g, qd) and Folic Acid Tablets (0.4 m, qd) .Levels of serum Hb, SF and SI between two groups were compared, and the pregnancy outcomes, neonatal outcomes, and effect and safety were observed.Results Compared with pre-treatment, levels of RBC, HCT and Ret, levels of Hb, SF and SI in two groups were all increased (P<0.05),and those indexes in treatment group were higher than control group (P<0.05).Compared with control group, birth outcomes in cesarean section, the rate of postpartum hemorrhage and neonatal distress rate in treatment group were lower (P<0.05), scores of neonatal muscle tension, pulse, respiration, skin frowning were higher ( P <0.05 ), the total efficiency and safety were higher ( P <0.05 ). Conclusion Polysaccharide iron combined with folic acid in treatment of anemia in gestation women was accurate , it can significantly improve the neonatal outcomes.

Objective To study the clinicopathological features and biological behavior of inflammatory pseudotumor-like follicular dendritic cell tumor.Methods We studied the clinical data,HE sections,immunohistochemical staining,Epstein-Barr virus encoded nuclear RNA(EBER)in situ hybridization and outcome of one patient with inflammatory pseudotumor-like follicular dendritic cell tumor of liver,and thirteen patients with inflammatory pseudotumor of liver and spleen treated at the Shengjing Hospital of China Medical University from 2001 to 2010.Results Among the thirteen inflammatory pseudotumors,we diagnosed 1 patient with inflammatory pseudotumor-like follicular dendritic cell tumor of spleen and 1 patient with inflammatory pseudotumor-like follicular dendritic cell tumor of liver using immuno-histochemical staining and EBER in situ by hybridization.The liver case had pathological morphology consistent with those described in the literatures,but the splenic case had specific histologic features.They were both female,and were alive 2.5 and 6 years after operation.Conclusions Inflammatory pseudotumor-like follicular dendritic cell tumor should be distinguished from inflammatory pseudotumor.It is a rare tumor seen mainly in liver and spleen.The diagnosis depends on histopathological and immunohistochemical findings.Inflammatory pseudotumor-like follicular dendritic cell tumor is a low-grade malignant tumor.Surgical excision is the treatment of choice.The two cases provided evidence for its indolent behavior.

BACKGROUND:Based on different algorithms of machine learning,how to carry out clinical research on lumbar disc herniation with the help of various algorithmic models has become a trend and hot spot in the development of intelligent medicine at present. OBJECTIVE:To review the characteristics of different algorithmic models of machine learning in the diagnosis and treatment of lumbar disc herniation,and summarize the respective advantages and application strategies of algorithmic models for the same purpose. METHODS:The computer searched PubMed,Web of Science,EMBASE,CNKI,WanFang,VIP and China Biomedical(CBM)databases to extract the relevant articles on machine learning in the diagnosis and treatment of lumbar disc herniation.Finally,96 articles were included for analysis. RESULTS AND CONCLUSION:(1)Different algorithm models of machine learning provide intelligent and accurate application strategies for clinical diagnosis and treatment of lumbar disc herniation.(2)Traditional statistical methods and decision trees in supervised learning are simple and efficient in exploring risk factors and establishing diagnostic and prognostic models.Support vector machine is suitable for small data sets with high-dimensional features.As a nonlinear classifier,it can be applied to the recognition,segmentation and classification of normal or degenerative intervertebral discs,and to establish diagnostic and prognostic models.Ensemble learning can make up for the shortcomings of a single model.It has the ability to deal with high-dimensional data and improve the precision and accuracy of clinical prediction models.Artificial neural network improves the learning ability of the model,and can be applied to intervertebral disc recognition,classification and making clinical prediction models.On the basis of the above uses,deep learning can also optimize images and assist surgical operations.It is the most widely used model with the best performance in the diagnosis and treatment of lumbar disc herniation.The clustering algorithm in unsupervised learning is mainly used for disc segmentation and classification of different herniated segments.However,the clinical application of semi-supervised learning is relatively less.(3)At present,machine learning has certain clinical advantages in the identification and segmentation of lumbar intervertebral discs,classification and grading of the degenerative intervertebral discs,automatic clinical diagnosis and classification,construction of the clinical predictive model and auxiliary operation.(4)In recent years,the research strategy of machine learning has changed to the neural network and deep learning,and the deep learning algorithm with stronger learning ability will be the key to realizing intelligent medical treatment in the future.