A Multi-Relational Graph Encoder Network for Fine-Grained Prediction of MiRNA-Disease Associations.

MicroRNAs (miRNAs) are critical in diagnosing and treating various diseases. Automatically demystifying the interdependent relationships between miRNAs and diseases has recently made remarkable progress, but their fine-grained interactive relationships still need to be explored. We propose a multi-relational graph encoder network for fine-grained prediction of miRNA-disease associations (MRFGMDA), which uses practical and current datasets to construct a multi-relational graph encoder network to predict disease-related miRNAs and their specific relationship types (upregulation, downregulation, or dysregulation). We evaluated MRFGMDA and found that it accurately predicted miRNA-disease associations, which could have far-reaching implications for clinical medical analysis, early diagnosis, prevention, and treatment. Case analyses, Kaplan-Meier survival analysis, expression difference analysis, and immune infiltration analysis further demonstrated the effectiveness and feasibility of MRFGMDA in uncovering potential disease-related miRNAs. Overall, our work represents a significant step toward improving the prediction of miRNA-disease associations using a fine-grained approach could lead to more accurate diagnosis and treatment of diseases.

Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals.

Automated detection of depression using Electroencephalogram (EEG) signals has become a promising application in advanced bioinformatics technology. Although current methods have achieved high detection performance, several challenges still need to be addressed: (1) Previous studies do not consider data redundancy when modeling multi-channel EEG signals, resulting in some unrecognized noise channels remaining. (2) Most works focus on the functional connection of EEG signals, ignoring their spatial proximity. The spatial topological structure of EEG signals has not been fully utilized to capture more fine-grained features. (3) Prior depression detection models fail to provide interpretability. To address these challenges, this paper proposes a new model, Multi-view Graph Contrastive Learning via Adaptive Channel Optimization (MGCL-ACO) for depression detection in EEG signals. Specifically, the proposed model first selects the critical channels by maximizing the mutual information between tracks and labels of EEG signals to eliminate data redundancy. Then, the MGCL-ACO model builds two similarity metric views based on functional connectivity and spatial proximity. MGCL-ACO constructs the feature extraction module by graph convolutions and contrastive learning to capture more fine-grained features of different perspectives. Finally, our model provides interpretability by visualizing a brain map related to the significance scores of the selected channels. Extensive experiments have been performed on public datasets, and the results show that our proposed model outperforms the most advanced baselines. Our proposed model not only provides a promising approach for automated depression detection using optimal EEG signals but also has the potential to improve the accuracy and interpretability of depression diagnosis in clinical practice.

Dexamethasone combined metronidazole on mammary duct ectasia and its relationship with serum IL-10 and IL-17.

AIM: To explore the effect of dexamethasone combined with metronidazole in the treatment of mammary duct ectasia (MDE) and its relationship with changes in serum interleukin-10 (IL-10) and IL-17 expression. METHODS: One hundred and twenty patients with MDE were divided into two groups randomly, control and observation groups (each n = 60). Another 50 patients with normal physical examination were recruited in the normal group. The expressions of serum IL-10 and IL-17 in three groups before and after treatment were observed. The prediction value of IL-10 and IL-17 in clinical efficacy was evaluated. RESULTS: Among three groups, the expression of IL-10 in the normal group was the highest (P < 0.001), but the expression of IL-17 was the lowest (P < 0.001). After treatment, the expression of IL-17 in observation group was lower (P < 0.001), the expression of IL-10 was higher (P < 0.05) than that in the control group. The areas under the IL-10 and IL-17 curve were 0.874 and 0.806, respectively. CONCLUSIONS: Dexamethasone combined with metronidazole can effectively improve the clinical efficacy of MDE patient treatment and serum IL-10 and IL-17 can be used as potential predictors of treatment efficacy.