ArticleTranslational neuroscience2024
Identifying key biomarkers and therapeutic candidates for post-COVID-19 depression through integrated omics and bioinformatics approaches.
Article in Translational neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Biomarkers of post-acute infection syndrome: a systematic literature review.Frontiers in immunology · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Depression, the leading cause of disability worldwide, is known to be exacerbated by severe acute respiratory syndrome coronavirus 2 infection, worsening coronavirus disease 2019 (COVID-19) outcomes. However, the mechanisms and treatments for this comorbidity are not well understood. Methods: This study utilized Gene Expression Omnibus datasets for COVID-19 and depression, combined with protein-protein interaction networks, to identify key genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to understand gene functions. The CIBERSORT algorithm and NetworkAnalyst were used to examine the relationship of immune cell infiltration with gene expression and to predict transcription factors (TFs) and microRNAs (miRNAs) interactions. The Connectivity Map database was used to predict drug interactions with these genes. Results: Conclusions: This research provides new insights into the molecular mechanisms of post-COVID-19 depression and suggests potential therapeutic strategies, marking a step forward in understanding and treating this complex comorbidity.
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