Background and purpose: Futile recanalization occurs when the endovascular thrombectomy (EVT) is a technical success but fails to achieve a favorable outcome. This study aimed to use machine learning (ML) algorithms to develop a pre-EVT model and a post-EVT model to predict the risk of futile recanalization and to provide meaningful insights to assess the prognostic factors associated with futile recanalization.Methods: Consecutive acute ischemic stroke patients with large vessel occlusion (LVO) undergoing EVT at the National Advanced Stroke Center of Nanjing First Hospital (China) between April 2017 and May 2021 were analyzed. The baseline characteristics and peri-interventional characteristics were assessed using four ML algorithms. The predictive performance was evaluated by the area under curve (AUC) of receiver operating characteristic and calibration curve. In addition, the SHapley Additive exPlanations (SHAP) approach and partial dependence plot were introduced to understand the relative importance and the influence of a single feature.Results: A total of 312 patients were included in this study. Of the four ML models that include baseline characteristics, the “Early” XGBoost had a better performance {AUC, 0.790 [95% confidence intervals (CI), 0.677–0.903]; Brier, 0.191}. Subsequent inclusion of peri-interventional characteristics into the “Early” XGBoost showed that the “Late” XGBoost performed better [AUC, 0.910 (95% CI, 0.837–0.984); Brier, 0.123]. NIHSS after 24 h, age, groin to recanalization, and the number of passages were the critical prognostic factors associated with futile recanalization, and the SHAP approach shows that NIHSS after 24 h ranks first in relative importance.Conclusions: The “Early” XGBoost and the “Late” XGBoost allowed us to predict futile recanalization before and after EVT accurately. Our study suggests that including peri-interventional characteristics may lead to superior predictive performance compared to a model based on baseline characteristics only. In addition, NIHSS after 24 h was the most important prognostic factor for futile recanalization.

Background. Cholesterol efflux and lipid raft redistribution contribute to attenuating temozolomide resistance of glioblastoma. Ginsenosides are demonstrated to modify cholesterol metabolism and lipid raft distribution, and the brain distribution and central nervous effects of whose isoforms Rb1, Rg1, Rg3, and CK have been identified. This study aimed to reveal the role of Rb1, Rg1, Rg3, and CK in the drug resistance of glioblastoma. Methods. The effects of ginsenosides on cholesterol metabolism in temozolomide-resistant U251 glioblastoma cells were evaluated by cholesterol content and efflux assay, confocal laser, qRT-PCR, and Western blot. The roles of cholesterol and ginsenosides in temozolomide resistance were studied by CCK-8, flow cytometry, and Western blot, and the mechanism of ginsenosides attenuating resistance was confirmed by inhibitors. Results. Cholesterol protected the survival of resistant U251 cells from temozolomide stress and upregulated multidrug resistance protein (MDR)1, which localizes in lipid rafts. Resistant cells tended to store cholesterol intracellularly, with limited cholesterol efflux and LXRα expression to maintain the distribution of lipid rafts. Ginsenosides Rb1, Rg1, Rg3, and CK reduced intracellular cholesterol and promoted cholesterol efflux in resistant cells, causing lipid rafts to accumulate in specific regions of the membrane. Rg1 and CK also upregulated LXRα expression and increased the cytotoxicity of temozolomide in the presence of cholesterol. We further found that cholesterol efflux induction, lipid raft redistribution, and temozolomide sensitization by Rg1 and CK were induced by stimulating LXRα. Conclusions. Ginsenosides Rg1 and CK controlled temozolomide resistance in glioblastoma cells by regulating cholesterol metabolism, which are potential synergists for temozolomide therapy.

Background: CPUL1, a phenazine analog, has demonstrated potent antitumor properties against hepatocellular carcinoma (HCC) and indicates a promising prospect in pharmaceutical development. However, the underlying mechanisms remain largely obscure. Methods: Multiple HCC cell lines were used to investigate the in vitro effects of CPUL1. The antineoplastic properties of CPUL1 were assessed in vivo by establishing a xenograft nude mice model. After that, metabolomics, transcriptomics, and bioinformatics were integrated to elucidate the mechanisms underlying the therapeutic efficacy of CPUL1, highlighting an unanticipated involvement of autophagy dysregulation. Results: CPUL1 suppressed HCC cell proliferation in vitro and in vivo, thereby endorsing the potential as a leading agent for HCC therapy. Integrative omics characterized a deteriorating scenario of metabolic debilitation with CPUL1, presenting an issue in the autophagy contribution of autophagy. Subsequent observations indicated that CPUL1 treatment could impede autophagic flow by suppressing autophagosome degradation rather than its formation, which supposedly exacerbated cellular damage triggered by metabolic impairment. Moreover, the observed late autophagosome degradation may be attributed to lysosome dysfunction, which is essential for the final stage of autophagy and cargo disposal. Conclusions: Our study comprehensively profiled the anti-hepatoma characteristics and molecular mechanisms of CPUL1, highlighting the implications of progressive metabolic failure. This could partially be ascribed to autophagy blockage, which supposedly conveyed nutritional deprivation and intensified cellular vulnerability to stress.