RESUMO
OBJECTIVES: Blood-brain barrier (BBB) disruption is an important pathological change after cerebral infarction that exacerbates brain injury. We aimed to investigate and compare the predictive utility of pre-treatment BBB permeability (BBBP) and BBBP within 1 h after endovascular treatment (EVT) for hemorrhagic transformation (HT) and 90-day prognosis. METHODS: Patients underwent preoperative computed tomography perfusion (CTP) and non-contrast CT (NCCT) within 1 h after EVT. Preoperative BBBP was determined by the relative permeability surface area product (rPS) in the hypoperfusion area. Postoperative BBBP was determined by the post-EVT Alberta Stroke Program Early CT Score (Post-ASPECTS), which is based on brain parenchymal hyperdensity on the postoperative NCCT. OUTCOMES: We included 100 patients. Univariate logistic regression analysis revealed correlations of preoperative rPS with HT, poor outcomes, and death. However, these correlations were not observed in multivariate logistic regression. A Post-ASPECTS ≤7 and could independently predict poor outcomes, while Post-ASPECTS ≤6 could independently predict death and HT. The baseline National Institutes of Health Stroke Scale (NIHSS) score could independently predict poor outcomes and death but not HT. A combined model using the baseline NIHSS and Post-ASPECTS scores had better predictive performance for poor outcomes and death than baseline NIHSS score alone; however, it was not superior to the predictive performance of the Post-ASPECTS score. CONCLUSION: The preoperative rPS cannot independently predict clinical outcomes in EVT-treated patients; contrastingly, the Post-ASPECTS score could independently predict poor outcomes, death, and HT. This parameter could inform prompt postoperative treatment decisions.
RESUMO
BACKGROUND Increasing studies have shown the important clinical role of immune and stromal cells in gastric cancer microenvironment. Based on information of immune and stromal cells in The Cancer Genome Atlas, this study aimed to construct a prognostic risk assessment model for gastric cancer. MATERIAL AND METHODS Based on the immune/structural scores, differentially expressed genes (DEGs) were filtered and analyzed. Afterwards, DEGs associated with prognosis were screened and the risk assessment model was constructed in the training set. Moreover, the validity of the model was verified both in the testing set and the overall sample. RESULTS In this study, patients were divided into high-score and low-score groups based on immune/stromal score, and 919 DEGs were identified. By applying least absolute shrinkage and selection operator (LASSO) and Cox analysis, 10 mRNAs were selected to form a prognostic risk assessment model, risk score=(0.294*SLC17A9) + (-0.477*FERMT3) + (0.866*NRP1) + (0.350*MMRN1) + (0.381*RNASE1) + (0.189*TRIB3) + (0.230*PGAP3) + (0.087*MAGEA3) + (0.182*TACR2) + (0.368*CYP51A1). In the training set, the low-risk group divided by the model was found to have better overall survival, and the prediction efficiency of the model was demonstrated to be good. Multivariate Cox analysis indicated that the model could work as a prognostic factor independently. Similar results were shown in the testing group and overall patients cohort group. Finally, the risk assessment model and other clinical variables were integrated to construct a nomogram. CONCLUSIONS In general, this study constructs a prognostic risk assessment model for gastric cancer, which could improve the prognosis stratification of patients combined with other clinical indicators.
