Background
Peripheral nerve injury induces upregulation of the calcium channel alpha-2-delta-1 proteins in the dorsal root ganglia and dorsal spinal cord that correlates with neuropathic pain development. Similar behavioral hypersensitivity was also observed in injury-free transgenic mice (TG) over-expressing the alpha-2-delta-1 proteins in neuronal tissues. To investigate pathways regulating alpha-2-delta-1 protein-mediated behavioral hypersensitivity, we examined whether spinal serotonergic 5-HT3 receptors are involved similarly in the modulation of behavioral hypersensitivity induced by either peripheral nerve injury in a nerve injury model or neuronal alpha-2-delta-1 over-expression in the TG model.
Methods
The effects of blocking behavioral hypersensitivity in these two models by intrathecal or systemic injections of 5-HT3 receptor antagonist, ondansetron, were compared.
Results
Our data indicated that the TG mice displayed similar behavioral hypersensitivities to non-painful mechanical stimulation (tactile allodynia) and painful thermal stimulation (thermal hyperalgesia) as that observed in the nerve injury model. Interestingly, tactile allodynia and thermal hyperalgesia in both models can be blocked similarly by intrathecal, but not systemic, injection of ondansetron.
Conclusions
Our data suggest that spinal 5-HT3 receptors are likely play a role in alpha-2-delta-1-mediated behavioral hypersensitivities through a descending serotonergic facilitation.
The recent pandemic of Coronavirus Disease 2019 (COVID-19) has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aimed to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID -19 patients and influenza patients based on clinical variables alone. We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.
Genome instability is a hallmark of cancer that arises through a panoply of mechanisms driven by oncogene and tumour-suppressor gene mutations. Oncogenic mutations in the core splicing factor SF3B1 have been linked to genome instability. Since SF3B1 mutations alter the selection of thousands of 3' splice sites affecting genes across biological pathways, it is not entirely clear how they might drive genome instability. Here we confirm that while R-loop formation and associated replication stress may account for some of the SF3B1-mutant genome instability, a mechanism involving changes in gene expression also contributes. An SF3B1-H662Q mutant cell line mis-splices the 5'UTR of the DNA repair regulator DYNLL1, leading to higher DYNLL1 protein levels, mis-regulation of DNA repair pathway choice and PARP inhibitor sensitivity. Reduction of DYNLL1 protein in these cells restores genome stability. Together these data highlight how SF3B1 mutations can alter cancer hallmarks through subtle changes to the transcriptome.
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