Objective To explore the expression feature of long non-coding RNA (lncRNA) 1010001N08Rik in hyperoxia-induced bronchopulmonary dysplasia (BPD) and predict the mechanism that 1010001N08Rik might be involved in the occurrence and development of BPD by a series of bioinformatics analysis.Method The sequence,genomic position and structure characteristics of 1010001N08Rik were acquired from UCSC genome browser,and its target gene was predicted by Ensemble database.We successfully established the animal model of BPD by making newborn C57BL/6J mice exposed to 95％ concentrations of ambient oxygen for seven days.The expression of 1010001N08Rik and Gata 6 were detected using real-time quantitative polymerase chain reaction (PCR).Student's t test was used to compare their expression levels during the BPD process.Result The relative expression of 1010001N08Rik in BPD process at d1,d3,d5,d7 was 1.21 ± 0.33,2.02 ± 0.41,2.95 ± 0.45,4.20-± 0.48 respectively,and there were significant difference between adjacent time points (P ＜ 0.05).The relative expression of Gata 6 mRNA was 0.92 ±0.30,1.10 ± 0.31,0.86 ± 0.24,0.45-± 0.08 respectively,and there was significant difference between d5 and d7 (P ＜0.05).1010001N08Rik had highly conserved property among different species.The chromosomal regions of 1010001N08Rik existed transcriptional factors binding locations and epigenetic regulation clues,and its possible candidate target gene was Gata 6.Conclusion The expression of 1010001N08Rik increased during the formation process of BPD.Bioinformatics analysis and preliminary experiment results suggested that 1010001N08Rik might participate in the process of BPD by down-regulating Gata 6 expression.

To assess the performance of radiomics model based on cardiac magnetic resonance imaging (CMR) cine sequence for assessing the severity of mitral regurgitation.
 Methods: A total of 80 patients who underwent CMR and echocardiography examination were retrospectively enrolled, including 67 patients with no or slight mitral regurgitation and 13 patients with moderate or severe mitral regurgitation. The relative difference in average filtered gradient (RDAFG) of CMR cine sequence were generated, which were combined with minimum output sum of squared error tracker (MOSSE) to extract 25 radiomics features. After reducing feature dimensionality by principal component analysis (PCA) and oversampling the minority samples, the radiomics model was established using support vector machine (SVM). The performance of the model was assessed by receiver operating characteristic (ROC) curve.
 Results: There were significant differences (both P<0.01) of the 2-dimension radiomics features between the two groups. The best performance (area under the ROC curve) of the established radiomics model was 0.971, with sensitivity and specificity at 85.7% and 94.1%, respectively.
 Conclusion: The performance of the machine learning-based radiomics model derived from CMR cine sequence for assessing the severity of mitral regurgitation was excellent, which can facilitate the computer-aided diagnosis and treatment in the era of artificial intelligence.
Heart ; Humans ; Magnetic Resonance Imaging ; Mitral Valve Insufficiency ; diagnostic imaging ; Reproducibility of Results ; Retrospective Studies