Schizophrenia and the COVID-19 pandemic: A narrative review from the biomedical perspective

Clinical Outcome in Persons with Severe Mental Disorders Attending a Mental Health Day Center during the COVID-19 Pandemic

BACKGROUND

Studies assessing the psychological impact of the COVID-19 pandemic on populations highlight the emergence of mental health difficulties, especially if a mental health disorder is already present. Patients with severe mental illnesses (SMIs) may be even more vulnerable to the psychosocial effects of the pandemic. However, little is known regarding the possible impact of the pandemic on SMI patients supported by community-based mental health day centers.

METHODS

A two-year prospective study comprising 29 individuals with SMI was conducted by the Skitali Mental Health Day Center in Ioannina, Northwest Greece. The described group of examined patients consisted mainly of psychotic patients (65.5%). Patients were assessed using the Health of Nations Outcome Scale and the Global Assessment of Functioning scale, and scores prior to and after the onset of the pandemic were compared.

RESULTS

The results indicated that participants did not present any significant decline in their overall clinical status during the COVID-19 pandemic and the national lockdown measures.

CONCLUSIONS

This finding is relevant because previous research has shown that the pandemic may negatively impact adherence to treatment and service attendance and that the symptomatology of patients with SMIs may further deteriorate. It is suggested that the operation of mental health day centers during collective stressful events should be preserved, but further research is needed to evaluate their role in maintaining continuity of care during such events.

Identification of shared pathogenetic mechanisms between COVID-19 and IC through bioinformatics and system biology

COVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein-protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.