Objective To estimate the effect of desensitizing toothpaste containing arginine on dentine hypersensitivity. Methods Such databases as CNKI, PubMed, Web of Science and Cochrane Trials Register (Issue 4, 2008) were retrieved, and Google was used as a supplementary tool to search the information up to March 2010. Randomized controlled trials (RCTs) of treating dentine hypersensitivity with arginine-containing toothpaste were included, and the relevant information was extracted and the quality evaluation was undertaken. Meta-analyses were performed with RevMan 5.0 software. Results Five RCTs with 397 patients were included. The results of meta-analyses showed that at 8 weeks, arginine-containing toothpaste was significantly different from potassium-containing toothpaste in terms of tactile sensitivity test (SMD=1.32, 95%CI 0.68 to 1.96) and air blast test (SMD= –0.77, 95%CI –1.22 to 0.32) with a better therapeutic effect. Conclusion Current literature evidence shows that the arginine-containing toothpaste is effective for the dentine hypersensitivity. However, this study is based on a small number of RCTs and samples, so further studies with high-quality and large-sample RCTs are needed.
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.