Effect of breastfeeding on the development of infection-related diseases during hospitalization in late preterm infants in 25 hospitals in Beijing, China / 中国当代儿科杂志

OBJECTIVE@#To investigate the incidence rate of infectious diseases during hospitalization in late preterm infants in Beijing, China, as well as the risk factors for infectious diseases and the effect of breastfeeding on the development of infectious diseases.@*METHODS@#Related data were collected from the late preterm infants who were hospitalized in the neonatal wards of 25 hospitals in Beijing, China, from October 23, 2015 to October 30, 2017. According to the feeding pattern, they were divided into a breastfeeding group and a formula feeding group. The two groups were compared in terms of general status and incidence rate of infectious diseases. A multivariate logistic regression analysis was used to investigate the risk factors for infectious diseases.@*RESULTS@#A total of 1 576 late preterm infants were enrolled, with 153 infants in the breastfeeding group and 1 423 in the formula feeding group. Of all infants, 484 (30.71%) experienced infectious diseases. The breastfeeding group had a significantly lower incidence rate of infectious diseases than the formula feeding group (22.88% vs 31.55%, @*CONCLUSIONS@#Breastfeeding can significantly reduce the incidence of infectious diseases and is a protective factor against infectious diseases in late preterm infants. Breastfeeding should therefore be actively promoted for late preterm infants during hospitalization.

C: Consensus Cancer Driver Gene Caller / 基因组蛋白质组与生物信息学报·英文版

Next-generation sequencing has allowed identification of millions of somatic mutations in human cancer cells. A key challenge in interpreting cancer genomes is to distinguish drivers of cancer development among available genetic mutations. To address this issue, we present the first web-based application, consensus cancer driver gene caller (C), to identify the consensus driver genes using six different complementary strategies, i.e., frequency-based, machine learning-based, functional bias-based, clustering-based, statistics model-based, and network-based strategies. This application allows users to specify customized operations when calling driver genes, and provides solid statistical evaluations and interpretable visualizations on the integration results. C is implemented in Python and is freely available for public use at http://drivergene.rwebox.com/c3.