Objective To analyze the levels of serum total bilirubin in patients and healthy con-trols, eatablish a method of using the mean of total bilirubin in serum of patients to control internal quality, and validate the reference range. Methods Frequencies mode of SPSS13.0 statistic software package was applied to performing analysis of all data, and then the analytic interval was determined based on the frequencies of the data. The daily data mean in the analytic interval was calculated. With the daily data mean as the testing data, the quality control was carried out by the same way of quality control for quality control sample. At the end of each month, the means of various analyzers were compared. Based on the results of healthy controls, the formula mean±1.96s was used to validate the current reference range. Results With the daily data mean as the testing data for quality control, its coefficient variation was within the accepatable limit, and the 95% distribition range was the same as the current reference interval. Conclusion It is necessary to establish suitable interval in which the da-ta mean was used for internal quality control. The current reference range in our hospital is proper.

OBJECTIVE To improve the level of HBV-DNA quantity dection.METHODS The necessity and feasibility of quality control improvement on the basis of external quality assessment results were analyzed.The source of error were songht through analyzing the whole experiment process,to improv the experiment protocol.RESULTS Four main sources of error were improved,and two of them existed in reality.Through improvements in handling protocol,coefficient of variability(CV) of internal quality control has decreased to 3.3% from 9.3% before improvement in protocol.External quality control results were also increased in large-scale.CONCLUSIONS Through the improvement in experiment process,quality assurance of HBV-DNA quantity analysis has been upgraded in essence.

In recent years, artificial intelligence (AI) has been widely applied in the field of drug discovery and development.In particular, natural language processing technology has been significantly improved after the emergence of the pre-training model.On this basis, the introduction of graph neural network has also made drug development more accurate and efficient.In order to help drug developers more systematically and comprehensively understand the application of artificial intelligence in drug discovery, this article introduces cutting-edge algorithms in AI, and elaborates on the various applications of AI in drug development, including drug small molecule design, virtual screening, drug repurposing, and drug property prediction, finally discusses the opportunities and challenges of AI in future drug development.