Smoking may increase the usage of antidepressant: evidence from genomic perspective analysis.

This study uses the two-sample Mendelian randomization (TSMR) method to explore the causal relationships between smoking initiation (SMKI), never smoking (NSMK), past tobacco smoking (PTSMK), and the usage of antidepressants (ATD). Single-nucleotide polymorphisms (SNPs) with genome-wide significance (P < 5E-08) related to SMKI, NSMK, and PTSMK were selected from the genome-wide association study (GWAS) database as instrumental variables (IVs). The main method, inverse variance weighted (IVW), was utilized to investigate the causal relationship. The results demonstrated a positive causal relationship between SMKI and ATD use, where SMKI leads to an increase in ATD use. Conversely, NSMK and PTSMK showed a negative causal relationship with ATD use, meaning that NSMK and PTSMK lead to a reduction in ATD use. Additionally, sensitivity analysis showed that the results of this study were robust and reliable. Using the TSMR method and from a genetic perspective, this study found that SMKI leads to an increase in ATD use, while NSMK and PTSMK reduce ATD use.

Exploring adverse events of Vilazodone: evidence from the FAERS database.

OBJECTIVE: This study aims to conduct an exhaustive evaluation of Vilazodone's safety in clinical application and to unearth the potential adverse event (AE) risks associated with its utilization based on FDA Adverse Event Reporting System (FAERS) database. METHODS: This research employed data spanning from the first quarter of 2011 to the third quarter of 2023 from the FAERS database. Various signal detection methodologies, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM), were utilized to ascertain the correlation between Vilazodone and specific AEs. RESULTS: The study compiled a total of 17,439,268 reports of drug AEs, out of which 5,375 were related to Vilazodone. Through signal mining, 125 Preferred Terms (PTs) encompassing 27 System Organ Classes (SOCs) were identified. The findings indicated a higher prevalence among females and patients within the 45 to 65 age bracket. The principal categories of AEs included Psychiatric disorders, Nervous system disorders, and Gastrointestinal disorders, with prevalent incidents of Diarrhoea, Nausea, and Insomnia. Moreover, the study identified robust signals of novel potential AEs, notably in areas such as sleep disturbances (Sleep paralysis, Hypnagogic hallucination, Rapid eye movements sleep abnormal, Sleep terror, Terminal insomnia, Tachyphrenia), sexual dysfunctions (Female orgasmic disorder, Orgasm abnormal, Disturbance in sexual arousal, Spontaneous penile erection, Anorgasmia, Sexual dysfunction, Ejaculation delayed), and other symptoms and injuries (Electric shock sensation, Violence-related symptom, Gun shot wound). CONCLUSION: Although Vilazodone presents a positive prospect in the management of MDD, the discovery of AEs linked to its use, particularly the newly identified potential risks such as sleep and sexual dysfunctions, necessitates heightened vigilance among clinicians.

Mining and analysis of adverse event signals of Cariprazine based on the real-world data of FAERS database.

OBJECTIVE: This study aims to analyze the adverse events (AEs) of Cariprazine based on the FAERS database, providing evidence for its safety surveillance. METHODS: For signal quantification of Cariprazine-related AEs, we used disproportionality analysis including the Ratio of Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS) algorithms. RESULTS: We selected Cariprazine-related AE reports from the FAERS database from the fourth quarter of 2015 to the first quarter of 2023, and performed a detailed data analysis. Out of a total of 12,278,580 case reports, 3659 were found to be directly related to Cariprazine. We identified 140 Preferred Terms (PT) to describe these AEs, finding that they involved 27 organ systems. Specifically, AEs related to eye disorders such as Cataract cortical, Cataract nuclear, Accommodation disorder, Lenticular opacities, Oculogyric crisis, Dyschromatopsia were not explicitly mentioned in the drug's leaflet, indicating the presence of new ADR signals. CONCLUSION: Analysis of the FAERS database identified AEs associated with Cariprazine, notably in eye disorders not previously documented in the drug's official leaflet. These findings emphasize the need for continuous post-market surveillance and awareness among healthcare professionals regarding potential new ADR signals.

The expression of immune related genes and potential regulatory mechanisms in schizophrenia.

OBJECTIVE: This study aimed to investigate the role of immune dysfunction in the pathogenesis of schizophrenia through single-cell transcriptome and bulk RNA data analyses. METHODS: The single-cell RNA sequencing (scRNA-seq) was selected to assess the cellular composition and gene expression profiles of the brain tissue. Further, bulk RNA sequencing data was utilized to corroborate findings from the single-cell analyses and provide additional insights into the molecular changes associated with the disease. Gene-drug interaction data was also included to identify potential therapeutic drugs targeting these dysregulated immune-related genes in schizophrenia. RESULTS: We discovered significant differences in cellular composition within schizophrenia tissue, including increased infiltration of fibroblasts, horizontal basal cells, monocytes, mesenchymal cells, and smooth muscle cells. The investigation of immune-related genes revealed significantly different expression of genes such as S100A2, CCL14, IGHA1, BPIFA1, GDF15, IL32, BPIFB2, HLA-DRA, S100A8, PTX3, TPM2, TNFRSF12A, GREM1 and others. These genes possibly contribute to the progression of schizophrenia through various pathways such as humoral immune response, IL-17 signaling pathway, adaptive immune response, antigen processing and presentation, and gut IgA production. Our findings also suggest possible transcriptional regulation in schizophrenia's immune dysfunction by transcription factors in monocytes, neutrophils, endothelial cells, and epithelial cells. Lastly, potential therapeutic drugs were identified through gene-drug interaction data, such as those targeting HLA-A and HLAB. CONCLUSION: The cellular heterogeneity and immune-related gene dysregulation play important roles in schizophrenia, which provides a foundation for understanding the pathogenesis and developing new treatment methods.