Aim: We aimed to investigate the influence of admission fibrinogen-to-albumin ratio (FAR) on 3-month outcomes after acute lacunar stroke. Materials & methods: Consecutive patients with acute lacunar stroke were included and classified into two groups according to an optimized FAR cut-off value determined by receiver operating characteristic curve analysis. Results: Compared with those with low FAR (<0.077), patients from the high FAR group (≥0.077) had significantly higher risk for 3-month disability and the composite outcome of death/disability. After logistic regression adjustment, high FAR was still significantly associated with 3-month disability and death/disability. Conclusion: FAR ≥0.077 on admission might be an independent predictor of disability and death/disability at 3 months after lacunar stroke, which needs to be verified in future studies.
With the development of UAV technology, the task allocation problem of multiple UAVs is remarkable, but most of these existing heuristic methods are easy to fall into the problem of local optimization. In view of this limitation, deep transfer reinforcement learning is applied to the task allocation problem of multiple unmanned aerial vehicles, which provides a new idea about solving this kind of problem. The deep migration reinforcement learning algorithm based on QMIX is designed. The algorithm first compares the target task with the source task in the strategy base to find the task with the highest similarity, and then migrates the network parameters obtained from the source task after training, stored in the strategy base, so as to accelerate the convergence of the QMIX algorithm. Simulation results show that the proposed algorithm is significantly better than the traditional heuristic method of allocation in terms of efficiency and has the same running time.
Introduction: Patients with hemorrhagic transformation (HT) were reported to have hemorrhage expansion. However, identification these patients with high risk of hemorrhage expansion has not been well studied. Objectives: We aimed to develop a radiomic score to predict hemorrhage expansion after HT among patients treated with thrombolysis/thrombectomy during acute phase of ischemic stroke. Methods: A total of 104 patients with HT after reperfusion treatment from the West China hospital, Sichuan University, were retrospectively included in this study between 1 January 2012 and 31 December 2020. The preprocessed initial non-contrast-enhanced computed tomography (NECT) imaging brain images were used for radiomic feature extraction. A synthetic minority oversampling technique (SMOTE) was applied to the original data set. The after-SMOTE data set was randomly split into training and testing cohorts with an 8:2 ratio by a stratified random sampling method. The least absolute shrinkage and selection operator (LASSO) regression were applied to identify candidate radiomic features and construct the radiomic score. The performance of the score was evaluated by receiver operating characteristic (ROC) analysis and a calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical value of the model. Results: Among the 104 patients, 17 patients were identified with hemorrhage expansion after HT detection. A total of 154 candidate predictors were extracted from NECT images and five optimal features were ultimately included in the development of the radiomic score by using logistic regression machine-learning approach. The radiomic score showed good performance with high area under the curves in both the training data set (0.91, sensitivity: 0.83; specificity: 0.89), test data set (0.87, sensitivity: 0.60; specificity: 0.85), and original data set (0.82, sensitivity: 0.77; specificity: 0.78). The calibration curve and DCA also indicated that there was a high accuracy and clinical usefulness of the radiomic score for hemorrhage expansion prediction after HT. Conclusions: The currently established NECT-based radiomic score is valuable in predicting hemorrhage expansion after HT among patients treated with reperfusion treatment after ischemic stroke, which may aid clinicians in determining patients with HT who are most likely to benefit from anti-expansion treatment.
<b><i>Background:</i></b> Limited data exist on the significance of acute cerebral microinfarcts (A-CMIs) in the context of acute ischemic stroke (AIS). We aimed to determine the profile and prognostic significance of A-CMIs on magnetic resonance imaging (MRI) in patients presenting with AIS. <b><i>Methods:</i></b> A prospective single-center series of patients with AIS who had 3T MRIs between March 2013 and December 2019. The presence, number, and location of A-CMIs on diffusion-weighted imaging, and markers of cerebral small vessel disease (CSVD), macroinfarcts features, and etiology were classified as cardioembolism (CE) or large artery atherosclerosis (LAA) or none. <b><i>Results:</i></b> Among 273 patients, A-CMIs were detected in 130 patients (47.6%), of whom cortical A-CMIs were found in 95 (73.0%) patients. Patients with A-CMIs were significantly older, less likely to have diabetes mellitus, and more likely to have atrial fibrillation and an embolic source (CE or LAA) compared to other patients. Patients with A-CMI had a higher frequency of macroinfarcts (diameter >20 mm), more often multiple and distributed in single or multiple vessel territories than other patients. An embolic source (LAA or CE) was independently associated with cortical A-CMIs (LAA adjusted odds ratio [aOR] 4.0 95% confidence interval [CI] 1.6–9.5; CE aOR 2.5, 95% CI 1.1–5.6), whereas lacunes were independently related to subcortical A-CMIs (aOR 2.6, 95% CI 1.2–5.8). <b><i>Conclusions:</i></b> We have shown A-CMIs occur in cortical and subcortical regions in nearly half of AIS patients, where microembolism and CSVD are, respectively, the key presumed etiological mechanism.
BackgroundAtrial fibrillation (AF) is related to an increased risk of cognitive dysfunction. Besides clinically overt stroke, AF can damage the brain via several pathophysiological mechanisms. We aimed to assess the potential mediating role of cerebral small vessel disease (SVD) and cognitive performance in individuals with AF.MethodsStroke-free individuals with AF from the cardiological outpatient clinic at West China Hospital of Sichuan University were recruited. Extensive neuropsychological testing tools were assessed including global function, domains of attention, executive functions, learning, and memory. 3 T magnetic resonance imaging (MRI) was used for SVD markers assessment of white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMBs), and enlarged perivascular spaces (EPVS). The correlation between SVD markers and cognitive measures was analyzed by multivariate linear regression models.ResultsWe finally enrolled 158 participants, of whom 95 (60.1%) were males. In multivariate models, the presence of lacunes independently associated with Montreal Cognitive Assessment (Model 1: ß = 0.52, Model 2: ß = 0.55), Rey Auditory Verbal Learning Test-immediate and delayed recall (Model 1: ß = 0.49; ß = 0.69; Model 2: ß = 0.53; ß = 0.73) as well as Stroop-Acorrect (Model 1: ß = 0.12; Model 2: ß = 0.13), while total WMH severity independently associated with Strooptime-A (Model 1: ß = 0.24; Model 3: ß = 0.27), Strooptime-B (Model 1: ß = 0.17; Model 3: ß = 0.17), Strooptime-C (Model 1: ß = 0.22; Model 3: ß = 0.21) and Shape Trail Test-A (Model 1: ß = 0.17; Model 3: ß = 0.16).ConclusionIn our cohort of stroke-free individuals with AF, lacunes, and WMHs were independently associated with cognitive decline while EPVS and CMBs did not show significance. Assessment of SVD MRI markers might be valuable for cognition risk stratification and facilitate optimal management of patients with AF.
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