Exploring Adaptive Graph Topologies and Temporal Graph Networks for EEG-Based Depression Detection.

In recent years, Graph Neural Networks (GNNs) based on deep learning techniques have achieved promising results in EEG-based depression detection tasks but still have some limitations. Firstly, most existing GNN-based methods use pre-computed graph adjacency matrices, which ignore the differences in brain networks between individuals. Additionally, methods based on graph-structured data do not consider the temporal dependency information of brain networks. To address these issues, we propose a deep learning algorithm that explores adaptive graph topologies and temporal graph networks for EEG-based depression detection. Specifically, we designed an Adaptive Graph Topology Generation (AGTG) module that can adaptively model the real-time connectivity of the brain networks, revealing differences between individuals. In addition, we designed a Graph Convolutional Gated Recurrent Unit (GCGRU) module to capture the temporal dynamical changes of brain networks. To further explore the differential features between depressed and healthy individuals, we adopt Graph Topology-based Max-Pooling (GTMP) module to extract graph representation vectors accurately. We conduct a comparative analysis with several advanced algorithms on both public and our own datasets. The results reveal that our final model achieves the highest Area Under the Receiver Operating Characteristic Curve (AUROC) on both datasets, with values of 83% and 99%, respectively. Furthermore, we perform extensive validation experiments demonstrating our proposed method's effectiveness and advantages. Finally, we present a comprehensive discussion on the differences in brain networks between healthy and depressed individuals based on the outputs of our final model's AGTG and GTMP modules.

The role of long noncoding RNA in major human disease.

BACKGROUND: Long noncoding RNAs (lncRNAs) are RNAs whose transcripts are longer than 200nt in length and lack the ability to encode proteins due to lack of specific open reading frames. lncRNAs were once thought to represent transcriptome noise or garbage sequences and a byproduct of RNA polymerase II (Pol II), and thereby ignored by researchers. In fact, lncRNA was involved in a wide variety of physiological and pathological processes in organisms. Comprehensive study of lncRNA does not only provide explanations to the physiological and pathological processes of living organisms, but also gives us new perspectives to the diagnosis, prevention and treatment of some clinical diseases. Therefore, the study of lncRNA is a very broad field of great research value and significance. RESULTS: This article reviews the function of lncRNAs and their role in major human diseases. CONCLUSIONS: Numerous studies show that lncRNA might serve as a biomarker for diagnosis and prognosis of various diseases. Compared to conventional biomarkers, lncRNA seems to have a higher diagnostic and prognostic values, not only because of their tissue and disease specific expression patterns, but also due to their highly stable physical and chemical properties.

MicroRNAs Regulate Thymic Epithelium in Age-Related Thymic Involution via Down- or Upregulation of Transcription Factors.

Age-related thymic involution is primarily induced by defects in nonhematopoietic thymic epithelial cells (TECs). It is characterized by dysfunction of multiple transcription factors (TFs), such as p63 and FoxN1, and also involves other TEC-associated regulators, such as Aire. These TFs and regulators are controlled by complicated regulatory networks, in which microRNAs (miRNAs) act as a key player. miRNAs can either directly target the 3'-UTRs (untranslated regions) of the TFs to suppress TF expression or target TF inhibitors to reduce or increase TF inhibitor expression and thereby indirectly enhance or inhibit TF expression. Here, we review the current understanding and recent studies about how miRNAs are involved in age-related thymic involution via regulation of TEC-autonomous TFs. We also discuss potential strategies for targeting miRNAs to rejuvenate age-related declined thymic function.

CysLT1 receptor antagonist alleviates pathogenesis of collagen-induced arthritis mouse model.

Cysteinyl leukotrienes (CysLTs) play a key role in inflammatory diseases such as asthma and their receptors' antagonists are currently used as anti-asthmatic drugs. CysLTs have also been found to participate in other inflammatory reactions. Here, we reported that in rheumatoid arthritis (RA) animals model, collagen-induced arthritis, (CIA), CysLT1, a receptor for CysLTs, was up-regulated in hind paw and lymph node, while CysLTs levels in the blood were also higher than normal mice. Montelukast, a drug targeting CysLT1, has been shown to effectively reduce the CIA incidence, peak severity, and cumulative disease scores. Further study indicated that CysLT1 signaling did not affect the differentiation of pathogenic T helper cells. We conclude that montelukast may play important roles in the pathogenesis of CIA, mainly by inducing infiltration of pathogenic T cells, increasing IL-17A secretion and expression of IL-17A, while these effects can be blocked by CysLT1 antagonists. Our findings indicate that antagonist of CysLT1 receptor may be used to treat rheumatoid arthritis.