ObjectiveTo investigate the distribution of bacteria detected from blood culture of pediatric patients and to observe the blood culture contamination rate. MethodsA total of 6 530 blood samples, collected from January 2011 to December 2012 were detected by BacT/Alert 3D automated blood culture system. We found out the contamination bacteria according to clinical data, laboratory data and microbiology knowledge. ResultsA total of 314 bacteria strains were isolated from 6 530 blood samples, and the positive rate was 4.8%, 228 of which were gram-positive bacteria. The isolates were mainly coagulase-negative staphylococci (43.9%), followed by Staphylococcus aureus (2.9%). In addition, 86 cases were gram-negative bacteria, the majority of which were Escherichia coli (9.6%), followed by Klebsiella pneumonia (8.3%). The overall blood culture contamination rate was 49.7% (156 bacteria were identified). The top two were coagulase-negative staphylococci (31.2%), followed by Bacillus sp. (6.4%). ConclusionThe contamination rate is high in children's blood culture, and coagulase-negative staphylococci are the main bacteria. It's necessary to use clinical data and laboratory data to determine its clinical significance, and avoid unnecessary use of antibiotics.
ObjectiveTo realize automatic risk bias assessment for the randomized controlled trial (RCT) literature using BERT (Bidirectional Encoder Representations from Transformers) as an approach for feature representation and text classification.MethodsWe first searched The Cochrane Library to obtain risk bias assessment data and detailed information on RCTs, and constructed data sets for text classification. We assigned 80% of the data set as the training set, 10% as the test set, and 10% as the validation set. Then, we used BERT to extract features, construct text classification model, and evaluate the seven types of risk bias values (high and low). The results were compared with those from traditional machine learning methods using a combination of n-gram and TF-IDF as well as the Linear SVM classifier. The accuracy rate (P value), recall rate (R value) and F1 value were used to evaluate the performance of the models.ResultsOur BERT-based model achieved F1 values of 78.5% to 95.2% for the seven types of risk bias assessment tasks, which was 14.7% higher than the traditional machine learning method. F1 values of 85.7% to 92.8% were obtained in the extraction task of the other six types of biased descriptors except "other sources of bias", which was 18.2% higher than the traditional machine learning method.ConclusionsThe BERT-based automatic risk bias assessment model can realize higher accuracy in risk of bias assessment for RCT literature, and improve the efficiency of assessment.
Diabetes and its complications pose a serious threat to human life and health. It has become a public health problem of wide concern worldwide. Currently, diabetes is mainly treated with insulin injection in clinic. However, manual insulin injection still has many shortcomings. In recent years, with the deepening of research, it has been found that an automated insulin delivery system (AID), which combines a continuous glucose monitoring device with an insulin pump, can significantly improve the effectiveness of diabetes treatment and reduce the incidence of complications in patients. This paper firstly introduces the composition of the AID system and its working principle, and then details the development history and current status of the related technologies from the aspects of continuous glucose monitoring technology, insulin pumps and the development of closed-loop control algorithms, etc. Finally, this paper looks forward to the application prospect and future development of AID system in the field of diabetes treatment, providing theoretical reference for further research.
Traditional manual testing of ventilator performance is labor-intensive, time-consuming, and prone to errors in data recording, making it difficult to meet the current demands for testing efficiency in the development and manufacturing of ventilators. Therefore, in this study we designed an automated testing system for essential performance parameters of ventilators. The system mainly comprises a ventilator airflow analyzer, an automated switch module for simulated lungs, and a test control platform. Under the control of testing software, this system can perform automated tests of critical performance parameters of ventilators and generate a final test report. To validate the effectiveness of the designed system, tests were conducted on two different brands of ventilators under four different operating conditions, comparing tidal volume, oxygen concentration, and positive end expiratory pressure accuracy using both the automated testing system and traditional manual methods. Bland-Altman statistical analysis indicated good consistency between the accuracy of automated tests and manual tests for all respiratory parameters. In terms of testing efficiency, the automated testing system required approximately one-third of the time needed for manual testing. These results demonstrate that the designed automated testing system provides a novel approach and means for quality inspection and measurement calibration of ventilators, showing broad application prospects.