Identification of hub genes and construction of diagnostic nomogram model in schizophrenia.

Schizophrenia (SCZ), which is characterized by debilitating neuropsychiatric disorders with significant cognitive impairment, remains an etiological and therapeutic challenge. Using transcriptomic profile analysis, disease-related biomarkers linked with SCZ have been identified, and clinical outcomes can also be predicted. This study aimed to discover diagnostic hub genes and investigate their possible involvement in SCZ immunopathology. The Gene Expression Omnibus (GEO) database was utilized to get SCZ Gene expression data. Differentially expressed genes (DEGs) were identified and enriched by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and disease ontology (DO) analysis. The related gene modules were then examined using integrated weighted gene co-expression network analysis. Single-sample gene set enrichment (GSEA) was exploited to detect immune infiltration. SVM-REF, random forest, and least absolute shrinkage and selection operator (LASSO) algorithms were used to identify hub genes. A diagnostic model of nomogram was constructed for SCZ prediction based on the hub genes. The clinical utility of nomogram prediction was evaluated, and the diagnostic utility of hub genes was validated. mRNA levels of the candidate genes in SCZ rat model were determined. Finally, 24 DEGs were discovered, the majority of which were enriched in biological pathways and activities. Four hub genes (NEUROD6, NMU, PVALB, and NECAB1) were identified. A difference in immune infiltration was identified between SCZ and normal groups, and immune cells were shown to potentially interact with hub genes. The hub gene model for the two datasets was verified, showing good discrimination of the nomogram. Calibration curves demonstrated valid concordance between predicted and practical probabilities, and the nomogram was verified to be clinically useful. According to our research, NEUROD6, NMU, PVALB, and NECAB1 are prospective biomarkers in SCZ and that a reliable nomogram based on hub genes could be helpful for SCZ risk prediction.

General Transcription Factor IIF Polypeptide 2: A Novel Therapeutic Target for Depression Identified Using an Integrated Bioinformatic Analysis.

Depression currently affects 4% of the world's population; it is associated with disability in 11% of the global population. Moreover, there are limited resources to treat depression effectively. Therefore, we aimed to identify a promising novel therapeutic target for depression using bioinformatic analysis. The GSE54568, GSE54570, GSE87610, and GSE92538 gene expression data profiles were retrieved from the Gene Expression Omnibus (GEO) database. We prepared the four GEO profiles for differential analysis, protein-protein interaction (PPI) network construction, and weighted gene co-expression network analysis (WGCNA). Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes metabolic pathway analyses were conducted to determine the key functions of the corresponding genes. Additionally, we performed correlation analyses of the hub genes with transcription factors, immune genes, and N6-methyladenosine (m6A) genes to reveal the functional landscape of the core genes associated with depression. Compared with the control samples, the depression samples contained 110 differentially expressed genes (DEGs), which comprised 56 downregulated and 54 upregulated DEGs. Moreover, using the WGCNA and PPI clustering analysis, the blue module and cluster 1 were found to be significantly correlated with depression. GTF2F2 was the only common gene identified using the differential analysis and WGCNA; thus, it was used as the hub gene. According to the enrichment analyses, GTF2F2 was predominantly involved in the cell cycle and JAK-STAT, PI3K-Akt, and p53 signaling pathways. Furthermore, differential and correlation analyses revealed that 9 transcription factors, 12 immune genes, and 2 m6A genes were associated with GTF2F2 in depression samples. GTF2F2 may serve as a promising diagnostic biomarker and treatment target of depression, and this study provides a novel perspective and valuable information to explore the molecular mechanism of depression.

A Patient with COVID-19 Treated with ECMO for 26 Days.

In December 2019, an outbreak of a novel coronavirus disease 2019 (COVID-19) was reported in Wuhan, China, which was subsequently reported in other countries worldwide. COVID-19 is typically an acute self-limited disease that can rarely be fatal with a 5.6% case mortality rate (May, 2020), mainly due to substantial damage to pulmonary alveolar structures, and subsequent respiratory failure. Given the previous experience, extracorporeal membrane oxygenation (ECMO) has been proven to be an effective therapy in the treatment of respiratory failure or acute respiratory distress syndrome (ARDS). On the basis of similar principle, ECMO may also be an effective therapy in the treatment of severe COVID-19. However, due to huge cost, it is not common to apply ECMO continuously for a long time. We, herein, describe a COVID-19 pneumonia patient, a 77-year female，who was treated with ECMO for 26 days. The hospitalisation costs of the patient were highest in Jilin Province. Key Words: COVID-19, ECMO, Pneumonia.

A meta-analysis of naldemedine for the treatment of opioid-induced constipation.

INTRODUCTION: Opioid-induced constipation (OIC) is a common adverse effect in patients under long-term opioid therapy. Naldemedine is a novel peripherally acting µ-opioid receptor antagonists being developed for the treatment of OIC without affecting central analgesia. This meta-analysis is to assess the current evidence for efficacy and safety of naldemedine for the treatment of OIC. Areas covered: We searched through MEDLINE, EMBASE, Web of Science and Cochrane Library, 'ISRCTN Register' and'ClinicalTrials.gov' (up to Aug 2018). Our final review included five randomized clinical trials (1751 participants in total), three trials observed naldemedine for the treatment of OIC in non-cancer patients and two trials in cancer patients. A Random Effects model was used for all comparisons. Subgroup analyses for the following subgroups were carried out: naldemedine 0.1 mg; 0.2 mg; 0.4 mg; cancer patients; non-cancer patients. Expert opinion: Naldemedine improved the proportion of responders and spontaneous bowel movements frequency. The incidence of serious adverse effects (AEs) in naldemedine group was higher than placebo, especially in cancer patient subgroup. The AEs occurred in participants with naldemedine were mild to moderate and well tolerated during treatment. The results of this network meta-analysis will guide the future researchers in evaluating naldemedine for the treatment of OIC.