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All Studies   Meta Analysis       

Biomarkers Prediction and Immune Landscape in Covid-19 and “Brain Fog”

Wu et al., Elsevier BV, doi:10.2139/ssrn.4897774
Jul 2024  
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Quercetin for COVID-19
24th treatment shown to reduce risk in July 2021, now with p = 0.002 from 12 studies.
No treatment is 100% effective. Protocols combine treatments.
5,100+ studies for 112 treatments. c19early.org
In Silico study identifying key genes and potential therapeutic agents related to brain fog in COVID-19 patients. Authors analyzed frontal cortex transcriptome data and found upregulated genes involved in immune-related pathways and downregulated genes involved in neuron and synapse pathways. Five key genes (SST, S100A9, SOCS3, FKBP5, HBB) were identified. Authors also found abnormalities in innate and adaptive immune cells in the brain. Seven drugs (fulvestrant, bucladesine, S-adenosylmethionine, valproic acid, folic acid, kaempferol, and quercetin) were identified as potentially targeting the key genes.
Study covers quercetin and vitamin B9.
Wu et al., 18 Jul 2024, China, preprint, 7 authors. Contact: wuxueling76@126.com, yfzh71@126.com.
In Silico studies are an important part of preclinical research, however results may be very different in vivo.
This PaperQuercetinAll
Biomarkers prediction and immune landscape in COVID-19 and "brain fog"
Ben Wang, Cuiying Xie #, Qiong Wu #, Qiong Xu, Liyan Zhang, Yunfeng Zhao, Xueling Wu, Cuiying Xie, Qiong Wu
Background Recent studies have shown that there is a decline in cognitive and memory abilities following SARS-CoV-2 infection, but it is unclear how SARS-CoV-2 causes neurological dysfunction and which molecular processes in the brain(which is called brain fog)are affected. The study aimed to identify key "brain fog"-related genes in COVID-19 patients. Methods To investigate weather COVID-19 affects cognitive decline, differential expression analysis was employed to identify differentially "brain fog"-related expressed genes (DEGs) of frontal cortex from the GEO database (GSE188847). Functional enrichment analysis was conducted to identify relevant signaling pathways. LASSO regression was utilized to screen pivotal genes. Weighted gene co-expression network anlaysis (WGCNA) was applied to identify different modules. Additionally, we use the Comparative Toxicogenomics Database (CTD) database to search key genes related to drugs or molecular compounds. Results 81 up-regulated and 441 down-regulated DEGs, obtained from the frontal cortex samples between COVID-19 and normal samples, were identified. Furthermore, we identified upregulated DEGs is almost in lung fibrosis and immune-related pathways, yet down-regulated DEGs is in neuron and synapse pathways. We then identified five key genes associated with aging in COVID-19, including SST, S100A9, SOSC3, FKBP5 and HBB. Compared to normal tissue, the expression levels off SST significantly decreased, the other four genes were significantly increased. Finally, we identified seven drugs associated with key genes: Fulvestrant, Bucladesine, S-Adenosylmethionine, Valproic Acid, Folic Acid, kaempferol and Quercetin. Conclusions Our findings revealed several key "brain fog"-related genes related to COVID-19 and several relevant drugs, providing a novel insight into the mechanism of SARS-CoV-2 infection in brain and potential implications for future cognitive decline treatment. This study contributes to a better understanding of critical genes in COVID-19 and its implications for future therapeutic interventions.
Consent for publication All authors have read and agreed to the published version of the manuscript. Availability of data and material The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Authors' contributions BW, YZ, and XW conceived the study. BW,CX and QW collected data. BW, LZ and QX analyzed and interpreted data. BW and QW drafted the manuscript. YZ, and XW revised the manuscript. All authors contributed to the article and approved the submitted version. Authors' information (optional) P r e p r i n t n o t p e e r r e v i e w e d
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