Deep latent variable joint cognitive modeling of neural signals and human behavior.

As the field of computational cognitive neuroscience continues to expand and generate new theories, there is a growing need for more advanced methods to test the hypothesis of brain-behavior relationships. Recent progress in Bayesian cognitive modeling has enabled the combination of neural and behavioral models into a single unifying framework. However, these approaches require manual feature extraction, and lack the capability to discover previously unknown neural features in more complex data. Consequently, this would hinder the expressiveness of the models. To address these challenges, we propose a Neurocognitive Variational Autoencoder (NCVA) to conjoin high-dimensional EEG with a cognitive model in both generative and predictive modeling analyses. Importantly, our NCVA enables both the prediction of EEG signals given behavioral data and the estimation of cognitive model parameters from EEG signals. This novel approach can allow for a more comprehensive understanding of the triplet relationship between behavior, brain activity, and cognitive processes.

Identification of critical genes and molecular pathways in COVID-19 myocarditis and constructing gene regulatory networks by bioinformatic analysis.

BACKGROUND: There is growing evidence of a strong relationship between COVID-19 and myocarditis. However, there are few bioinformatics-based analyses of critical genes and the mechanisms related to COVID-19 Myocarditis. This study aimed to identify critical genes related to COVID-19 Myocarditis by bioinformatic methods, explore the biological mechanisms and gene regulatory networks, and probe related drugs. METHODS: The gene expression data of GSE150392 and GSE167028 were obtained from the Gene Expression Omnibus (GEO), including cardiomyocytes derived from human induced pluripotent stem cells infected with SARS-CoV-2 in vitro and GSE150392 from patients with myocarditis infected with SARS-CoV-2 and the GSE167028 gene expression dataset. Differentially expressed genes (DEGs) (adjusted P-Value <0.01 and |Log2 Fold Change| ≥2) in GSE150392 were assessed by NetworkAnalyst 3.0. Meanwhile, significant modular genes in GSE167028 were identified by weighted gene correlation network analysis (WGCNA) and overlapped with DEGs to obtain common genes. Functional enrichment analyses were performed by using the "clusterProfiler" package in the R software, and protein-protein interaction (PPI) networks were constructed on the STRING website (https://cn.string-db.org/). Critical genes were identified by the CytoHubba plugin of Cytoscape by 5 algorithms. Transcription factor-gene (TF-gene) and Transcription factor-microRibonucleic acid (TF-miRNA) coregulatory networks construction were performed by NetworkAnalyst 3.0 and displayed in Cytoscape. Finally, Drug Signatures Database (DSigDB) was used to probe drugs associated with COVID-19 Myocarditis. RESULTS: Totally 850 DEGs (including 449 up-regulated and 401 down-regulated genes) and 159 significant genes in turquoise modules were identified from GSE150392 and GSE167028, respectively. Functional enrichment analysis indicated that common genes were mainly enriched in biological processes such as cell cycle and ubiquitin-protein hydrolysis. 6 genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) were identified as critical genes. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Etoposide, Methotrexate, Troglitazone, etc) were considered as target drugs for COVID-19 Myocarditis. CONCLUSIONS: Through bioinformatics method analysis, this study provides a new perspective to explore the pathogenesis, gene regulatory networks and provide drug compounds as a reference for COVID-19 Myocarditis. It is worth highlighting that critical genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) may be potential biomarkers and treatment targets of COVID-19 Myocarditis for future study.

Microsomal prostaglandin E2 synthase-1 and its inhibitors: Molecular mechanisms and therapeutic significance.

Inflammation is closely linked to the abnormal phospholipid metabolism chain of cyclooxygenase-2/microsomal prostaglandin E2 synthase-1/prostaglandin E2 (COX-2/mPGES-1/PGE2). In clinical practice, non-steroidal anti-inflammatory drugs (NSAIDs) as upstream COX-2 enzyme activity inhibitors are widely used to block COX-2 cascade to relieve inflammatory response. However, NSAIDs could also cause cardiovascular and gastrointestinal side effects due to its inhibition on other prostaglandins generation. To avoid this, targeting downstream mPGES-1 instead of upstream COX is preferable to selectively block overexpressed PGE2 in inflammatory diseases. Some mPGES-1 inhibitor candidates including synthetic compounds, natural products and existing anti-inflammatory drugs have been proved to be effective in in vitro experiments. After 20 years of in-depth research on mPGES-1 and its inhibitors, ISC 27864 have completed phase II clinical trial. In this review, we intend to summarize mPGES-1 inhibitors focused on their inhibitory specificity with perspectives for future drug development.

Warm Communication Style Strengthens Expectations and Increases Perceived Improvement.

Communication, both verbal and nonverbal, between healthcare providers and patients has been shown to affect treatment outcomes in clinical settings. Separately, accumulating research suggests a role for response expectations in altering treatment outcomes, especially in the context of inert or placebo treatment. However, few studies have examined which aspects of patient-provider communication strengthen expectations, leading to better treatment outcomes. The goal of this study is to test the effect of both verbal and nonverbal aspects of communication on response expectations and treatment outcomes in the context of a placebo exercise program. In a 2 × 2 between-subjects analogue study design, 89 healthy adults were randomly assigned to interact with a trainer with either a warm or a neutral communication style. Each group also received from their trainer either basic or enhanced information about the placebo training program. Participants performed coordination and balance tests before and after the placebo training program, and expectations were assessed after the training but prior to the second series of tests. Participants in the warm condition had significantly higher expectations of treatment effects and reported more improvement in their performance. No such differences were found between the enhanced and basic information conditions. There were no significant effects of communication style or information on actual balance and coordination performance. These results shed light on the importance of nonverbal communication, especially facial expression and tone of voice, in strengthening expectations and increasing subjective improvement.