A prediction model for short-term neurodevelopmental impairment in preterm infants with gestational age less than 32 weeks

IntroductionEarly identification and intervention of neurodevelopmental impairment in preterm infants may significantly improve their outcomes. This study aimed to build a prediction model for short-term neurodevelopmental impairment in preterm infants using machine learning method.MethodsPreterm infants with gestational age  < 32 weeks who were hospitalized in The Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, and were followed-up to 18 months corrected age were included to build the prediction model. The training set and test set are divided according to 8:2 randomly by Microsoft Excel. We firstly established a logistic regression model to screen out the indicators that have a significant effect on predicting neurodevelopmental impairment. The normalized weights of each indicator were obtained by building a Support Vector Machine, in order to measure the importance of each predictor, then the dimension of the indicators was further reduced by principal component analysis methods. Both discrimination and calibration were assessed with a bootstrap of 505 resamples.ResultsIn total, 387 eligible cases were collected, 78 were randomly selected for external validation. Multivariate logistic regression demonstrated that gestational age(p = 0.0004), extrauterine growth restriction (p = 0.0367), vaginal delivery (p = 0.0009), and hyperbilirubinemia (0.0015) were more important to predict the occurrence of neurodevelopmental impairment in preterm infants. The Support Vector Machine had an area under the curve of 0.9800 on the training set. The results of the model were exported based on 10-fold cross-validation. In addition, the area under the curve on the test set is 0.70. The external validation proves the reliability of the prediction model.ConclusionA support vector machine based on perinatal factors was developed to predict the occurrence of neurodevelopmental impairment in preterm infants with gestational age  < 32 weeks. The prediction model provides clinicians with an accurate and effective tool for the prevention and early intervention of neurodevelopmental impairment in this population

The complete chloroplast genome of Petunia exserta (Solanaceae: Petunioideae), an endangered ornamental species

Petunia exserta is an ornamental species on the brink of extinction in the wild. We report here the complete chloroplast genome of P. exserta, which is 156,598 bp in size consisting of a large single-copy region (87,095 bp), a small single-copy region (18,643 bp), and a pair of inverted repeats (25,430 bp for each). The chloroplast (used ‘cp’ hereafter) genome contains 132 genes, including 8 rRNA genes, 37 tRNA genes, and 87 protein-coding genes. Phylogenetic analysis demonstrated that P. exserta was most closely related to P. hybrida, and they together were closer to Calibrachoa hybrida than other taxa in the Solanaceae family