Objective To develop an active monitoring model for adverse drug events(ADEs)related to antimicrobial use in children based on the China hospital pharmacovigilance system(CHPS).Methods Trigger items for the active monitoring model were initially drafted through a review of relevant literature,adverse reaction databases,and drug label warnings,and subsequently refined using the Delphi method.A retrospective analysis was performed on pediatric inpatients who received antibiotics at Anning First People's Hospital between January 1 and December 31,2024.The detection rate and positive predictive value(PPV)of the active monitoring model were calculated and compared with spontaneous ADE reports from the same period.Risk factors for ADEs were further analyzed using logistic regression.Results 25 trigger items were established for the active monitoring model.Among 1,784 cases,233 ADEs were identified,yielding a detection rate of 13.06%(233/1,784).The spontaneous reporting rate of adverse events during the same period was 1.85%(33/1,784).The difference between the two was statistically significant(P<0.001).There were 727 positive trigger events,with 299 cases of ADE detected,resulting in an overall PPV of 41.13%(299/727).Logistic regression revealed that antibiotic use exceeding 3 days(OR=1.454,95%CI:1.012-2.088)was significantly associated with ADE occurrence.Conclusion Compared with conventional spontaneous reporting,the active monitoring model can significantly improve the detection rate of ADEs of pediatric antimicrobial drugs and achieve active and real-time monitoring of drug adverse events.

In order to realize brain-computer interface (BCI), optimal features of single trail motor imagery electroencephalogram (EEG) were extracted and classified. Mu rhythm of EEG was obtained by preprocessing, and the features were optimized by spatial filtering, which are estimated from a set of data by method of common spatial pattern. Classification decision can be made by Fisher criterion, and classification performance can be evaluated by cross validation and receiver operating characteristic (ROC) curve. Optimal feature dimension determination projected by spatial filter was discussed deeply in cross-validation way. The experimental results show that the high discriminate accuracy can be guaranteed, meanwhile the program running speed is improved. Motor imagery intention classification based on optimized EEG feature provides difference of states and simplifies the recognition processing, which offers a new method for the research of intention recognition.

Objective To evaluate the effect of the empowerment theory-based health education on anxiety, depression and self-efficacy in the major caregivers for patient with leukemia. Methods Eighty patients with leukemia and their caregivers were enrolled during November 2013 to November 2014 and were divided into the intervention group (n=40) and the control group (n=40). The caregivers of the intervention group received empowerment theory-based education , while those of the control group received general health education. The two groups were compared in terms of anxiety, depression and self-efficacy. Results Before intervention, there were no significant differences in the three items between the groups (P>0.05). After intervention, the level of self efficacy in the intervention group was better than that in the control group (P<0.05) and the scores in the intervention group were significantly lower than those of the control group. Conclusion The empowerment theory-based education can relieve anxiety and depression and improve the self efficacy of the caregivers of patients with leukemia.