Circulating microRNA profile in patients with membranous obstruction of the inferior vena cava.

Membranous obstruction of the inferior vena cava (MOVC) is a common type of Budd-Chiari syndrome. However, the pathogenesis of MOVC has not been fully elucidated. Recent studies demonstrated that microRNAs (miRNAs or miRs) are involved in multiple diseases. To the best of our knowledge, specific changes in the expression of miRNAs in MOVC patients have not been previously assessed. The present study used a microarray analysis, followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) validation, with the aim to access the miRNA expression levels in the plasma of 34 MOVC patients, compared with those in healthy controls. The results revealed a total of 16 differentially expressed miRNAs in MOVC patients. Subsequently, RT-qPCR analysis verified the statistically consistent expression of 5 selected miRNAs (miR-125a-5p, miR-133b, miR-423-5p, miR-1228-5p and miR-1266), in line with the results of the microarray analysis. These 5 miRNAs, which were described as crucial regulators in numerous biological processes and vascular diseases, may play an important role in the pathogenesis of MOVC. Bioinformatics analysis of target genes of the differentially expressed miRNAs revealed that these predicted targets were significantly enriched and involved in several key signaling pathways important for MOVC, including the ErbB, Wnt, MAPK and VEGF signaling pathway. In conclusion, miRNAs may involve in multiple signaling pathways contributing to the pathological processes of MOVC. The present study offers an intriguing new perspective on the involvement of miRNAs in MOVC; however, the precise underlying mechanisms require further validation.

[The application of artificial neural network in studying landless farmer's mental health problems].

To introduce a method of classification with high precision--the artificial neural network (ANN), and to compare the results using logistic regression method. Using data from 1070 landless peasants' mental health survey, the artificial neural network models and logistic regression model were built and compared on their advantages and disadvantages of the two models. The prediction accuracy for artificial neural network was 94.229% and for logistic regression it was 51.028%. ANN appeared to have had good ability on generalization. ANN displayed advantages when conditions of classical statistical techniques could not be met or the predictive effect appeared to be unsatisfactory. Hence, ANN would make a better fracture of its application in medical research.