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Background and Objectives Suicide rates typically increase during recessions. However, few studies have explored how recessions affect risk among older adults nearing retirement. This study used a large suicide mortality registry to characterize and quantify suicide related to retirement during the Great Recession (GR). Research Design and Methods Data come from the National Violent Death Reporting System (NVDRS, 2004–2017; N  = 53,298 suicide deaths age ≥50). We analyzed the text narratives (i.e. descriptions of the most salient circumstances to each suicide) of these decedents using natural language processing (NLP) to identify cases that were "retirement-related" (RR, e.g. anticipating, being unable to, or recently retiring). We used time-series analysis to quantify variation in RR over the GR, and compared these trends to retirees (i.e. decedents whose occupation was "retired") and all decedents aged ≥50. We used content and network analysis to characterize themes represented in the narratives. Results There were 878 RR cases (1.6% of suicides aged ≥50) identified by the NLP model; only 52% of these cases were among retirees. RR cases were younger (62 vs 75 years) and more educated (41.5% vs 24.5% college degree) than retirees. The rate of RR suicide was positively associated with indicators of the GR (e.g. short-term unemployment R 2 = 0.70, p  = .024), but economic indicators were not correlated with the suicide rate among retirees or older adults in general. Economic issues were more central to the narratives of RR cases during the GR compared to other periods. Discussion and Implications Recessions shape suicide risk related to retirement transitions. [ABSTRACT FROM AUTHOR]

The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery. We initiated our approach by conducting differential gene expression analysis on addiction-related transcriptomic data to identify key genes. We propose a novel topological differentiation to identify key genes from a protein–protein interaction network derived from DEGs. This method utilizes persistent Laplacians to accurately single out pivotal nodes within the network, conducting this analysis in a multiscale manner to ensure high reliability. Through rigorous literature validation, pathway analysis and data-availability scrutiny, we identified three pivotal molecular targets, mTOR, mGluR5 and NMDAR, for drug repurposing from DrugBank. We crafted machine learning models employing two natural language processing (NLP)-based embeddings and a traditional 2D fingerprint, which demonstrated robust predictive ability in gauging binding affinities of DrugBank compounds to selected targets. Furthermore, we elucidated the interactions of promising drugs with the targets and evaluated their drug-likeness. This study delineates a multi-faceted and comprehensive analytical framework, amalgamating bioinformatics, topological data analysis and machine learning, for drug repurposing in addiction treatment, setting the stage for subsequent experimental validation. The versatility of the methods we developed allows for applications across a range of diseases and transcriptomic datasets. [ABSTRACT FROM AUTHOR]

Background Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns. Methods A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States. Results The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates. Conclusions Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates. [ABSTRACT FROM AUTHOR]