Reduced plasma levels of RGM-A predict stroke-associated pneumonia in patients with acute ischemic stroke: A prospective clinical study

Knockdown of RGMA improves ischemic stroke via Reprogramming of Neuronal Metabolism

Neuronal energy metabolism dysregulation is involved in various pathologies of Ischemia-reperfusion (I/R), yet the role of RGMA in neuronal metabolic reprogramming has not been reported. In this study, we found that RGMA expression significantly increased after I/R, and compared to control mice, mice with MCAO/R showed an increase in glycolytic metabolic products and the expression of glycolytic pathway proteins. Furthermore, RGMA levels are closely related to neuronal energy metabolism. We discovered that knockdown of RGMA can shift neuronal energy metabolism towards oxidative phosphorylation and the pentose phosphate pathway, thereby protecting mice from ischemic reperfusion injury. Mechanistically, knockdown of RGMA can downregulate PGK1 expression, reducing the increase in glycolytic flux following ischemia reperfusion. Moreover, we found that knockdown of RGMA can reduce the interaction between USP10 and PGK1, thus affecting the ubiquitination degradation of PGK1. In summary, our data suggest that RGMA may regulate neuronal energy metabolism by inhibiting the USP10-mediated deubiquitination of PGK1, thus protecting it from I/R injury. This study provides new ideas for clarifying the intrinsic mechanism of neuronal damage after I/R.

Construction and comparison of short-term prognosis prediction model based on machine learning in acute ischemic stroke

Objective

To construct and compared the short-term prognosis prediction models of acute ischemic stroke (AIS) by machine learning (ML).

Methods

Retrospectively study. The group W (mRS≤3) was clustered, and combined with group P (mRS>3) to form the post-clustering dataset for modeling. The "glmnet", "rpart", "xgboost", "randomForest", "neuralnet" packages were used to construct ML models. The accuracy, sensitivity, specificity, positive predict value (PPV), negative predict value (NPV) among the models were compared. Four external clinical datasets were used for external clinical validation. The optimal prediction model was determined by variable screening ability, model visualization, and external clinical validation performance.

Results

The post-clustering dataset contains 139 patients (group W) and 122 patients (group P). The neutrophil multiplied by D-dimer (NDM) has predictive value in all ML prediction models in this study. In the decision tree model, NDMQ occupies the first tree node, When NDM≤5.62 and the age<74.5, the probability of poor prognosis of AIS is less than 20 %. When NDM>5.62 and accompanied by pneumonia, the incidence of poor prognosis of AIS is about 90 %. In the Random Forest (RF) model, NDMQ had the highest Gini index. The variable combination screened by the RF model had the best performance in the neural network, and the accuracy, sensitivity, specificity, PPV, and NPV of the external validation were 0.800, 0.774, 0.833, 0.857, and 0.741, respectively. The RF model had the best performance in the external clinical validation datasets, with accuracies of 0.646, 0.697, 0.695, and 0.713, respectively.

Conclusions

NDM shows predictive value for AIS short-term prognosis in all ML models in this study. The optimal model in screening characteristic variables and the performance of in external clinical datasets was RF model. In the analysis of medical data with small sample size and outcome as categorical variables, RF could be used as the main algorithm to build a model.

Disulfidptosis and its Role in Peripheral Blood Immune Cells after a Stroke: A New Frontier in Stroke Pathogenesis

BACKGROUND

Stroke-Induced Immunodepression (SIID) is characterized by apoptosis in blood immune populations, such as T cells, B cells, NK cells, and monocytes, leading to the clinical presentation of lymphopenia. Disulfidptosis is a novel form of programmed cell death characterized by accumulating disulfide bonds in the cytoplasm, resulting in cellular dysfunction and eventual cell death.

OBJECTIVE

In this study, we investigated the association between disulfidptosis and stroke by analyzing gene sequencing data from peripheral blood samples of stroke patients.

METHODS

Differential gene expression analysis identified a set of disulfidptosis-related genes (DRGs) significantly associated with stroke. Initial exploration identified 32 DRGs and their interactions. Our study encompassed several analyses to understand the molecular mechanisms of DRGs in stroke. Weighted Gene Co-Expression Network Analysis (WGCNA) uncovered modules of co-expressed genes in stroke samples, and differentially expressed gene (DEG) analysis highlighted 1643 key genes.

RESULTS

These analyses converged on four hub genes of DRGs (SLC2A3, SLC2A14, SLC7A11, NCKAP1) associated with stroke. Immune cell composition analysis indicated positive correlations between hub genes and macrophages M1, M2, and neutrophils and negative associations with CD4+ and CD8+ T cells, B cells, and NK cells. Sub-cluster analysis revealed two distinct clusters with different immune cell expression profiles. Gene Set Enrichment Analysis (GSEA) demonstrated enrichment of apoptosis-related pathways, neurotrophin signaling, and actin cytoskeleton regulation. Associations between hub genes and apoptosis, necroptosis, ferroptosis, and cuproptosis, were also identified.

CONCLUSION

These results suggest that the DRG hub genes are interconnected with various cell death pathways and immune processes, potentially contributing to stroke pathological development.