ObjectiveTo develop a prognostic prediction model of endovascular treatment (EVT) for acute ischemic stroke (AIS) induced by large-vessel occlusion (LVO), this study applied machine learning classification model light gradient boosting machine (LightGBM) to construct a unique prediction model.MethodsA total of 973 patients were enrolled, primary outcome was assessed with modified Rankin scale (mRS) at 90 days, and favorable outcome was defined using mRS 0–2 scores. Besides, LightGBM algorithm and logistic regression (LR) were used to construct a prediction model. Then, a prediction scale was further established and verified by both internal data and other external data.ResultsA total of 20 presurgical variables were analyzed using LR and LightGBM. The results of LightGBM algorithm indicated that the accuracy and precision of the prediction model were 73.77 and 73.16%, respectively. The area under the curve (AUC) was 0.824. Furthermore, the top 5 variables suggesting unfavorable outcomes were namely admitting blood glucose levels, age, onset to EVT time, onset to hospital time, and National Institutes of Health Stroke Scale (NIHSS) scores (importance = 130.9, 102.6, 96.5, 89.5 and 84.4, respectively). According to AUC, we established the key cutoff points and constructed prediction scale based on their respective weightings. Then, the established prediction scale was verified in raw and external data and the sensitivity was 80.4 and 83.5%, respectively. Finally, scores >3 demonstrated better accuracy in predicting unfavorable outcomes.ConclusionPresurgical prediction scale is feasible and accurate in identifying unfavorable outcomes of AIS after EVT.
Because teachers in colleges and universities generally do not know the number of students in their classes, they spend considerable time monitoring student attendance, reducing the teaching time. In this study, we investigated a new method for effectively monitoring the attendance of students in classes of colleges and universities. When students enter a classroom at our campus, they must store their cellphones in a pouch containing multiple pockets that is hung from a wall of the classroom. These cellphone storage hanging pockets (abbreviated to cellphone pockets) have become a necessary tool for students to store cellphones and not only improve the efficiency of studying in the classroom, but are also convenient for teachers to check student attendance at the start of classes. We investigated a template matching method for efficiently finding the attendance of students using cellphone pockets. We used images of cellphone pockets as the input images for template matching to find the grids of the cellphone pockets with no cellphones. This enabled teachers to use the serial numbers of the cellphone pocket grids with no cellphones to identify the absent students.
The underground coal gasification (UCG) technology is basically mature, but the influence of its own process and tools slows down its industrialization progress. This paper introduced the development and field test of two new UCG coiled-tubing gasification agent injection tools. The test results show that the two kinds of gasification agent injection tools ensure the injection point under control by conducting downhole temperature measurement and ground monitoring jointly. The problem that the tool is burnt by the backfire is solved by designing a backfire prevention device. To realize low pressure drop, the gasification agent flow channel inside the tool is designed optimally to keep the tool pressure drop not more than 0.5 MPa and the system pressure drop not more than 3 MPa. The tool overall has the characteristics of low pressure drop, high temperature resistance, backfire prevention and anti burning to satisfy the demand of the field test. This technology is a new achievement in the development of UCG technology and equipment in China. The research conclusions can provide technical reference for developing a new generation of UCG technology.
The recent global pandemic of coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although respiratory symptoms are the primary manifestation of the majority of COVID-19 patients, an increasing number of neurological symptoms and manifestations of COVID-19 have been observed. In this review, we detail the neurological complications of COVID-19, such as gustatory and olfactory dysfunctions, stroke, memory decline, muscle injury, and seizures. Furthermore, we introduce neural invasion mechanism underlying SARS-CoV-2 infection and, further, explain the occurrence of these complications. This review offers insights into the neurological signs and symptoms of COVID-19, which may help improve the prognosis of the infected patients.
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