Metabolomic Analysis in Saliva and Different Brain Regions of Older Mice with Postoperative Delirium Behaviors.

Objective: Postoperative delirium (POD) has become a critical challenge with severe consequences and increased incidences as the global population ages. However, the underlying mechanism is yet unknown. Our study aimed to explore the changes in metabolites in three specific brain regions and saliva of older mice with postoperative delirium behavior and to identify potential non-invasive biomarkers. Methods: Eighteen-month-old male C57/BL6 mice were randomly assigned to the anesthesia/surgery or control group. Behavioral tests were conducted 24 h before surgery and 6, 9, and 24 h after surgery. Complement C3 (C3) and S100 calcium-binding protein B protein (S100beta) levels were measured in the hippocampus, and a metabolomics analysis was performed on saliva, hippocampus, cortex, and amygdala samples. Results: In total, 43, 33, 38, and 14 differential metabolites were detected in the saliva, hippocampus, cortex, and amygdala, respectively. "Pyruvate" "alpha-linolenic acid" and "2-oleoyl-1-palmitoy-sn-glycero-3-phosphocholine" are enriched in one common pathway and may be potential non-invasive biomarkers for POD. Common changes were observed in the three brain regions, with the upregulation of 1-methylhistidine and downregulation of D-glutamine. Conclusion: Dysfunctions in energy metabolism, oxidative stress, and neurotransmitter dysregulation are implicated in the development of POD. The identification of changes in the level of salivary metabolite biomarkers could aid in the development of noninvasive diagnostic methods for POD.

Diagnostic model constructed by nine inflammation-related genes for diagnosing ischemic stroke and reflecting the condition of immune-related cells.

Background: Ischemic cerebral infarction is the most common type of stroke with high rates of mortality, disability, and recurrence. However, the known diagnostic biomarkers and therapeutic targets for ischemic stroke (IS) are limited. In the current study, we aimed to identify novel inflammation-related biomarkers for IS using machine learning analysis and to explore their relationship with the levels of immune-related cells in whole blood samples. Methods: Gene expression profiles of healthy controls and patients with IS were download from the Gene Expression Omnibus. Analysis of differentially expressed genes (DEGs) was performed in healthy controls and patients with IS. Single-sample gene set enrichment analysis was performed to calculate inflammation scores, and weighted gene co-expression network analysis was used to analyze genes in significant modules associated with inflammation scores. Key DEGs in significant modules were then analyzed using LASSO regression analysis for constructing a diagnostic model. The effectiveness and specificity of the diagnostic model was verified in healthy controls and patients with IS and with cerebral hemorrhage (CH) using qRT-PCR. The relationship between diagnostic score and the levels of immune-related cells in whole blood were analyzed using Pearson correlations. Results: A total of 831 DEGs were identified. Both chronic and acute inflammation scores were higher in patients with IS, while 54 DEGs were also clustered in the gene modules associated with chronic and acute inflammation scores. Among them, a total of 9 genes were selected to construct a diagnostic model. Interestingly, RT-qPCR showed that the diagnostic model had better diagnostic value for IS but not for CH. The levels of lymphocytes were lower in blood of patients with IS, while the levels of monocytes and neutrophils were increased. The diagnostic score of the model was negatively associated with the levels of lymphocytes and positively associated with levels of monocytes and neutrophils. Conclusions: Taken together, the diagnostic model constructed using the inflammation-related genes TNFSF10, ID1, PAQR8, OSR2, PDK4, PEX11B, TNIP1, FFAR2, and JUN exhibited high and specific diagnostic value for IS and reflected the condition of lymphocytes, monocytes, and neutrophils in the blood. The diagnostic model may contribute to the diagnosis of IS.