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Purpose: The study aimed to assess the value of the resting-state electroencephalogram (EEG)-based convolutional neural network (CNN) method for the diagnosis of depression and its severity in order to better serve depressed patients and at-risk populations. Methods: In this study, we used the resting state EEG-based CNN to identify depression and evaluated its severity. The EEG data were collected from depressed patients and healthy people using the Nihon Kohden EEG-1200 system. Analytical processing of resting-state EEG data was performed using Python andMATLAB software applications. The questionnaire included the Self- Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Symptom Check-List-90 (SCL-90), and the Eysenck Personality Questionnaire (EPQ). Results: A total of 82 subjects were included in this study, with 41 in the depression group and 41 in the healthy control group. The area under the curve (AUC) of the resting-state EEG-based CNN in depression diagnosis was 0.74 (95%CI: 0.70--0.77) with an accuracy of 66.40%. In the depression group, the SDS, SAS, SCL-90 subscales, and N scores were significantly higher in themajor depression group than those in the non-major depression group (p < 0.05). The AUC of the model in depression severity was 0.70 (95%CI: 0.65--0.75) with an accuracy of 66.93%. Correlation analysis revealed that major depression AI scores were significantly correlated with SAS scores (r = 0.508, p = 0.003) and SDS scores (r = 0.765, p < 0.001). Conclusion: Our model can accurately identify the depression-specific EEG signal in terms of depression diagnosis and severity identification. It would eventually provide new strategies for early diagnosis of depression and its severity. [ABSTRACT FROM AUTHOR]

Macrophage migration inhibitory factor (MIF) has multiple intrinsic enzymatic activities of the dopachrome/phenylpyruvate tautomerase and thiol protein oxidoreductase, and plays an important role in the development of obesity as a pro-inflammatory cytokine. However, which enzymatic activity of MIF is responsible for regulating in obesity are still unknown. In the present study, we investigated the roles of the tautomerase of MIF in high fat diet (HFD)-induced obesity using MIF tautomerase activity-lacking (MIFP1G/P1G) mice. Our results showed that the serum MIF and the expression of MIF in adipose tissue were increased in HFD-treated mice compared with normal diet fed mice. The bodyweights were significantly reduced in MIFP1G/P1G mice compared with WT mice fed with HFD. The sizes of adipocytes were smaller in MIFP1G/P1G mice compared with WT mice fed with HFD using haematoxylin and eosin (H&E) staining. In addition, the MIFP1G/P1G mice reduced the macrophage infiltration, seen as the decreases of the expression of inflammatory factors such as F4/80, IL-1β, TNFα, MCP1, and IL-6. The glucose tolerance tests (GTT) and insulin tolerance tests (ITT) assays showed that the glucose tolerance and insulin resistance were markedly improved, and the expressions of IRS and PPARγ were upregulated in adipose tissue from MIFP1G/P1G mice fed with HFD. Furthermore, we observed that the expressions of Bax, a pro-apoptotic protein, and the cleaved caspase 3-positive cells in white tissues were decreased and the ratio of Bcl2/Bax was increased in MIFP1G/P1G mice compared with WT mice. Taken together, our results demonstrated that the tautomerase activity-lacking of MIF significantly alleviated the HFD-induced obesity and adipose tissue inflammation, and improved insulin resistance in MIFP1G/P1G mice. [ABSTRACT FROM AUTHOR]

Background: Autism spectrum disorders (ASD) are hereditary, heterogeneous and biologically complex neurodevelopmental disorders. Individual studies on gene expression in ASD cannot provide clear consensus conclusions. Therefore, a systematic review to synthesize the current findings from brain tissues and a search tool to share the meta-analysis results are urgently needed. Methods: Here, we conducted a meta-analysis of brain gene expression profiles in the current reported human ASD expression datasets (with 84 frozen male cortex samples, 17 female cortex samples, 32 cerebellum samples and 4 formalin fixed samples) and knock-out mouse ASD model expression datasets (with 80 collective brain samples). Then, we applied R language software and developed an interactive shared and updated database (dbMDEGA) displaying the results of meta-analysis of data from ASD studies regarding differentially expressed genes (DEGs) in the brain. Results: This database, dbMDEGA (https://dbmdega.shinyapps.io/dbMDEGA/), is a publicly available web-portal for manual annotation and visualization of DEGs in the brain from data from ASD studies. This database uniquely presents meta-analysis values and homologous forest plots of DEGs in brain tissues. Gene entries are annotated with meta-values, statistical values and forest plots of DEGs in brain samples. This database aims to provide searchable meta-analysis results based on the current reported brain gene expression datasets of ASD to help detect candidate genes underlying this disorder. Conclusion: This new analytical tool may provide valuable assistance in the discovery of DEGs and the elucidation of the molecular pathogenicity of ASD. This database model may be replicated to study other disorders. [ABSTRACT FROM AUTHOR]

Background: Hepatitis B virus (HBV), because of its error-prone viral polymerase, has a high mutation rate leading to widespread substitutions, deletions, and insertions in the HBV genome. Deletions may significantly change viral biological features complicating the progression of liver diseases. However, the clinical conditions correlating to the accumulation of deleted mutants remain unclear. In this study, we explored HBV deletion patterns and their association with disease status and antiviral treatment by performing whole genome sequencing on samples from 51 hepatitis B patients and by monitoring changes in deletion variants during treatment. Clone sequencing was used to analyze preS regions in another cohort of 52 patients. Results: Among the core, preS, and basic core promoter (BCP) deletion hotspots, we identified preS to have the highest frequency and the most complex deletion pattern using whole genome sequencing. Further clone sequencing analysis on preS identified 70 deletions which were classified into 4 types, the most common being preS2. Also, in contrast to the core and BCP regions, most preS deletions were in-frame. Most deletions interrupted viral surface epitopes, and are possibly involved in evading immuno-surveillance. Among various clinical factors examined, logistic regression showed that antiviral medication affected the accumulation of deletion mutants (OR = 6.81, 95% CI = 1.296 ~ 35.817, P = 0.023). In chronic carriers of the virus, and individuals with chronic hepatitis, the deletion rate was significantly higher in the antiviral treatment group (Fisher exact test, P = 0.007). Particularly, preS2 deletions were associated with the usage of nucleos(t)ide analog therapy (Fisher exact test, P = 0.023). Dynamic increases in preS1 or preS2 deletions were also observed in quasispecies from samples taken from patients before and after three months of ADV therapy. In vitro experiments demonstrated that preS2 deletions alone were not responsible for antiviral resistance, implying the coordination between wild type and mutant strains during viral survival and disease development. Conclusions: We present the HBV deletion distribution patterns and preS deletion substructures in viral genomes that are prevalent in northern China. The accumulation of preS deletion mutants during nucleos(t)ide analog therapy may be due to viral escape from host immuno-surveillance. [ABSTRACT FROM AUTHOR]