ObjectiveTo observe and compare the efficacy and safety of intravenous thrombolysis with alteplase or urokinase in the first-ever acute ischemic stroke patients arriving at the hospital 3.5-4.5 h after onset.MethodsClinical data of patients with acute ischemic stroke treated in Shihezi People’s Hospital between January 2019 and October 2020 were prospectively collected. The National Insititutes of Health Stroke Scale (NIHSS) score on the 7th day and the 90th day, the modified Rankin Scale (mRS) score and the Blessed Behavior Scale (BBS) score on the 90th day, and symptomatic bleeding within 36 h after thrombolysis were analyzed and compared between the patients receiving alteplase threatment (the alteplase group) and the ones receiving urokinase treatment (the urokinase group).ResultsTotally 96 patients were treated with intravenous thrombolysis. Among them, 58 patients received alteplase threatment and 38 received urokinase treatment. The difference in NIHSS, mRS, or BBS scores between the two groups before treatment was not statistically significant (P>0.05). On the 90th day after treatment, the NIHSS, mRS, and BBS scores of the alteplase group were 3.59±3.73, 2.26±1.26, and 15.33±8.28, respectively, and those of the urokinase group were 5.95±4.88, 3.00±0.87, and 20.37±11.80, respectively; the differences between the two groups were all statistically significant (P<0.05). There was no significant difference in the rate of symptomatic intracerebral hemorrhage between the two groups within 36 h after treatment (P>0.05). Multiple linear regression analyses showed that the treatment method was related to the NIHSS score on the 7th day, the NIHSS score on the 90th day, the mRS score on the 90th day, and the BBS score on the 90th day (P<0.05), the history of heart disease was related to the mRS score on the 90th day (P<0.05), and the income was related to the BBS score on the 90th day (P<0.05).ConclusionFor the hyperactue ischemic stroke, the overall effect of alteplase treatment may be better than that of urokinase treatment.
Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.