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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. [ABSTRACT FROM AUTHOR]

This research endeavored to elucidate the antioxidant attributes of lactic acid bacteria, specifically their impact on anti-aging and lifespan augmentation in Caenorhabditis elegans. The study focused on Lactiplantibacillus plantarum A72, identified through ARTP mutagenesis for its potent antioxidant properties. In vitro analysis affirmed its free radical neutralizing capacity. In C. elegans, the strain not only extended the lifespan by 25.13% and amplified motility 2.52-fold, but also maintained reproductive capabilities. Remarkably, Lpb. plantarum A72 diminished reactive oxygen species (ROS) and malondialdehyde (MDA) levels in C. elegans by 34.86% and 69.52%, respectively, while concurrently enhancing its antioxidant enzyme activities. The strain also bolstered C. elegans survival rates by 46.33% and 57.78% under high temperature and H2O2 conditions, respectively. Transcriptomic scrutiny revealed that Lpb. plantarum A72 could retard C. elegans aging and extend lifespan by upregulating the sod-5 and hsp-16.1 genes and downregulating the fat-6 and lips-17 genes. These findings propose Lpb. plantarum A72 as a potential antioxidant and anti-aging lactic acid bacteria. [ABSTRACT FROM AUTHOR]

Background and purpose: Futile recanalization occurs in a significant proportion of patients with basilar artery occlusion (BAO) after endovascular thrombectomy (EVT). Therefore, our goal was to develop a visualized nomogram model to early identify patients with BAO who would be at high risk of futile recanalization, more importantly, to aid neurologists in selecting the most appropriate candidates for EVT. Methods: Patients with BAO with EVT and the Thrombolysis in Cerebral Infarction score of =2b were included in the National Advanced Stroke Center of Nanjing First Hospital (China) from October 2016 to June 2021. The exclusion criteria were lacking the 3-month Modified Rankin Scale (mRS), age < 18 years, the premorbid mRS score >2, and unavailable baseline CT imaging. Potential predictors were selected for the construction of the nomogram model and the predictive and calibration capabilities of the model were assessed. Results: A total of 84 patients with BAO were finally enrolled in this study, and patients with futile recanalization accounted for 50.0% (42). The area under the curve (AUC) of the nomogram model was 0.866 (95% CI, 0.786-0.946). The mean squared error, an indicator of the calibration ability of our prediction model, was 0.025. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A generalised nested array (GNA) configuration with large minimum inter-element spacing is attractive to alleviate mutual coupling. In this study, by decomposing and rearranging the sub-arrays of GNA, the authors propose a new nested array configuration named as widened nested array (WNA). The WNA enables to provide the reduced mutual coupling, enhanced degrees of freedom (DOFs), extended array aperture and improved direction of arrival (DOA) estimation performance compared to GNA. The closed-form expressions for sensor positions of WNA are given and the optimal array configuration for largest DOFs is derived. Meanwhile, the authors propose a partial angular compressed sensing(CS) based DOA estimation algorithm for WNA. Specifically, discrete Fourier transform method is first derived for consecutive DOFs of WNA to obtain initial DOA estimates. Subsequently, the initial estimates are utilised to compress the angular range of overcomplete dictionary of the CS algorithm, which is exploited to obtain the fine DOA estimates. Extensive simulation results testify that the proposed algorithm has the same estimation accuracy as CS algorithm with lower complexity. [ABSTRACT FROM AUTHOR]