Multimodal transformer graph convolution attention isomorphism network (MTCGAIN): a novel deep network for detection of insomnia disorder.

Background: In clinic, the subjectivity of diagnosing insomnia disorder (ID) often leads to misdiagnosis or missed diagnosis, as ID may have the same symptoms as those of other health problems. Methods: A novel deep network, the multimodal transformer graph convolution attention isomorphism network (MTGCAIN) is proposed in this study. In this network, graph convolution attention (GCA) is first employed to extract the graph features of brain connectivity and achieve good spatial interpretability. Second, the MTGCAIN comprehensively utilizes multiple brain network atlases and a multimodal transformer (MT) to facilitate coded information exchange between the atlases. In this way, MTGCAIN can be used to more effectively identify biomarkers and arrive at accurate diagnoses. Results: The experimental results demonstrated that more accurate and objective diagnosis of ID can be achieved using the MTGCAIN. According to fivefold cross-validation, the accuracy reached 81.29% and the area under the receiver operating characteristic curve (AUC) reached 0.8760. A total of nine brain regions were detected as abnormal, namely right supplementary motor area (SMA.R), right temporal pole: superior temporal gyrus (TPOsup.R), left temporal pole: superior temporal gyrus (TPOsup.L), right superior frontal gyrus, dorsolateral (SFGdor.R), right middle temporal gyrus (MTG.R), left middle temporal gyrus (MTG.L), right inferior temporal gyrus (ITG.R), right median cingulate and paracingulate gyri (DCG.R), left median cingulate and paracingulate gyri (DCG.L). Conclusions: The brain regions in the default mode network (DMN) of patients with ID show significant impairment (occupies four-ninths). In addition, the functional connectivity (FC) between the right middle occipital gyrus and inferior temporal gyrus (ITG) has an obvious correlation with comorbid anxiety (P=0.008) and depression (P=0.005) among patients with ID.

Long Non-coding RNAs as Communicators and Mediators Between the Tumor Microenvironment and Cancer Cells.

Long non-coding RNAs (lncRNAs) are a class of more than 200 nucleotides RNA transcripts which have limited protein coding capacity. They regulate numerous biological processes in cancers through diverse molecular mechanisms. Aberrant expression of lncRNAs has been frequently associated with human cancer. Furthermore, the tumor microenvironment (TME) is composed of different cells such as cancer-associated fibroblasts (CAFs), endothelial cells and infiltrated immune cells, and all of which participate in communication with tumor cells affecting the progression of tumor. LncRNAs are directly and indirectly involved in the crosstalk between stromal cells and tumor cells and dysregulated lncRNAs expression in these cells could drive tumorigenesis. In this review, we explore the influence of aberrantly expressed lncRNAs in tumor progression, clarify the critical roles of lncRNAs in the TME, summarize findings on crosstalk between infiltrated immune cells, CAFs, endothelial cells, and tumor cells via lncRNAs, and discuss the promise of lncRNAs as tumor diagnostic markers and therapeutic targets.

Identification of a transcription factor-microRNA network in esophageal adenocarcinoma through bioinformatics analysis and validation through qRT-PCR.

Purpose: The rapidly rising incidence of esophageal adenocarcinoma (EAC), which is usually diagnosed late with a poor prognosis, has become a growing problem. This study investigated the potential transcription factor (TF)-related molecular mechanisms of EAC by using bioinformatics analysis and qRT-PCR validation. Methods: Expression profile datasets for mRNAs (GSE92396, GSE13898, GSE26886 and GSE1420) and miRNAs (GSE16456) were downloaded from the GEO database. Overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified through integrative analysis. Then, a TF-miRNA-mRNA network was constructed based on bioinformatics data from the TRRUST, TRED and miRTarBase database. Furthermore, overall survival analysis for the mRNAs and miRNAs in the TF-miRNA-mRNA network was performed with data from TCGA, and qRT-PCR was used to validate the results. Results: A total of 294 overlapping DEGs were identified in EAC tissues compared to normal tissues, including 181 downregulated and 113 upregulated genes. Then, 16 TFs that could target the DEGs and were related to cancer were predicted based on public databases, and 41 DEGs that could be targeted were identified as key genes. Additionally, 12 DEMs were predicted through miRTarBase to be associated with the key genes, and TP53-(miR-125b)-ID2 and JUN-(miR-30a)-IL1A from the TF-miRNA-mRNA network were identified to potentially play significant roles in EAC. Furthermore, CCL20, IL1A, ABCC3, hsa-miR-23b, and hsa-miR-191, which are involved in the TF-miRNA-mRNA network, were found to be significantly associated with patient survival in EAC. Finally, the expression of a miRNA-mRNA pair (hsa-miR-30a-5p and IL1A) was revealed to be correlated with prognosis. Conclusion: In this study, a TF-miRNA-mRNA network was constructed to analyze the potential molecular mechanisms of EAC. Key genes and miRNAs associated with patient survival were identified, which may reveal promising approaches for EAC diagnosis and therapy.

Expression of gastrointestinal nesfatin-1 and gastric emptying in ventromedial hypothalamic nucleus- and ventrolateral hypothalamic nucleus-lesioned rats.

AIM: To determine the expression levels of gastrointestinal nesfatin-1 in ventromedial hypothalamic nucleus (VMH)-lesioned (obese) and ventrolateral hypothalamic nucleus (VLH)-lesioned (lean) rats that exhibit an imbalance in their energy metabolism and gastric mobility. METHODS: Male Wistar rats were randomly divided into a VMH-lesioned group, a VLH-lesioned group, and their respective sham-operated groups. The animals had free access to food and water, and their diets and weights were monitored after surgery. Reverse transcription-polymerase chain reaction and immunostaining were used to analyse the levels of NUCB2 mRNA and nesfatin-1 immunoreactive (IR) cells in the stomach, duodenum, small intestine, and colon, respectively. Gastric emptying was also assessed using a modified phenol red-methylcellulose recovery method. RESULTS: The VMH-lesioned rats fed normal chow exhibited markedly greater food intake and body weight gain, whereas the VLH-lesioned rats exhibited markedly lower food intake and body weight gain. NUCB2/nesfatin-1 IR cells were localised in the lower third and middle portion of the gastric mucosal gland and in the submucous layer of the enteric tract. Compared with their respective controls, gastric emptying was enhanced in the VMH-lesioned rats (85.94% ± 2.27%), whereas the VLH lesions exhibited inhibitory effects on gastric emptying (29.12% ± 1.62%). In the VMH-lesioned rats, the levels of NUCB2 mRNA and nesfatin-1 protein were significantly increased in the stomach and duodenum and reduced in the small intestine. In addition, the levels of NUCB2 mRNA and nesfatin-1 protein in the VLH-lesioned rats were decreased in the stomach, duodenum, and small intestine. CONCLUSION: Our study demonstrated that nesfatin-1 level in the stomach and duodenum is positively correlated with body mass. Additionally, there is a positive relationship between gastric emptying and body mass. The results of this study indicate that gastrointestinal nesfatin-1 may play a significant role in gastric mobility and energy homeostasis.