DCDA: CircRNA-Disease Association Prediction with Feed-Forward Neural Network and Deep Autoencoder.

Circular RNA is a single-stranded RNA with a closed-loop structure. In recent years, academic research has revealed that circular RNAs play critical roles in biological processes and are related to human diseases. The discovery of potential circRNAs as disease biomarkers and drug targets is crucial since it can help diagnose diseases in the early stages and be used to treat people. However, in conventional experimental methods, conducting experiments to detect associations between circular RNAs and diseases is time-consuming and costly. To overcome this problem, various computational methodologies are proposed to extract essential features for both circular RNAs and diseases and predict the associations. Studies showed that computational methods successfully predicted performance and made it possible to detect possible highly related circular RNAs for diseases. This study proposes a deep learning-based circRNA-disease association predictor methodology called DCDA, which uses multiple data sources to create circRNA and disease features and reveal hidden feature codings of a circular RNA-disease pair with a deep autoencoder, then predict the relation score of the pair by a deep neural network. Fivefold cross-validation results on the benchmark dataset showed that our model outperforms state-of-the-art prediction methods in the literature with the AUC score of 0.9794.

The association of serum gamma-glutamyl transpeptidase level and other laboratory parameters with blood pressure in hypertensive patients under ambulatory blood pressure monitoring.

BACKGROUND: Hypertension is a very important cause of morbidity and mortality. Serum gamma-glutamyl transpeptidase (GGT) is a biomarker of oxidative stress and associated with increased risk of hypertension and diabetes. The aim of this study was to evaluate the association of serum GGT level, which is an early marker of inflammation and endothelial dysfunction, with the deterioration of the diurnal rhythm of the blood pressure. METHODS: A total of 171 patients with hypertension were included in this study. Patients whose nighttime mean blood pressure, measured via ambulatory blood pressure monitoring, decreased between 10% and 20% compared with the daytime mean blood pressure were defined as "dippers", whereas patients with a nighttime blood pressure decrease lower than 10% were defined as "non-dippers". RESULTS: A total of 99 hypertensive patients (65 females/34 males) were classified as dippers and 72 patients (48 females/24 males) as non-dippers. The mean age of the non-dipper group was significantly greater than the dipper group. Serum GGT, C-reactive protein and uric acid levels were significantly higher among patients in the non-dipper group. Negative correlations were detected between GGT levels and diurnal systolic and diastolic blood pressure decreases. CONCLUSION: Our findings revealed that GGT level was higher in the non-dipper group, and was negatively correlated with the nighttime decrease of diurnal blood pressure. C-reactive protein and uric acid levels were also higher in the non-dipper group. However, future randomized controlled prospective studies with larger patient populations are necessary to confirm our findings.