ObjectiveTo observe the effect of acupuncture and Chinese medication in improving the quality of life in post-menopausal women.MethodThe menopause Rating Scale (MRS) was adopted to evaluate the quality of life in post-menopausal women. Thirty post-menopausal women of MRS≥20 were randomized into an acupuncture group and a Chinese medication group to respectively receive 4-week treatment. They were scored again by using MRS at the end of intervention and during the follow-up study to compare with the pre-treatment scores.ResultThe decrease of MRS score was more significant in the acupuncture group than that in the Chinese medication group, and the intra-group comparisons between pre-treatment and post-treatment scores showed significant differences (P<0.001), and the intra-group comparisons also showed differences between pre-treatment and follow-up results (P<0.05).ConclusionThe effect of acupuncture in improving the quality of life in post-menopausal women is more significant than that of Chinese medication.

With the progress of information technology and intelligent technology,the intelligent development of urine testing instruments is facing new opportunities.Using the disease cybernetics theory model to analyze the business process and current urine testing instruments of clinical urine analyzer,a generational theoretical model of urine testing instruments has been constructed,which is conducive to guiding the intelligent development direction of urine testing instruments.The study divides urine testing instruments into one to four generations of products,with the first-generation of products being operated by doctors.The second-generation products are currently available for laboratory technicians to use various urine analyzers.The third-generation products further optimize the testing process and intelligence,without the need for inspectors to operate.The fourth-generation products are unmanned and do not require sampling.It can be seen that with the development of technology,urine analysis has indeed become more convenient,but after all,various instruments have their limitations.Therefore,the establishment of a theoretical model for the generation of urine testing instruments should be applied in clinical urine testing,which can not only improve the efficiency of urine analysis but also improve its quality.

Feature representation is the crucial factor for the magnetic resonance imaging (MRI) based computer-aided diagnosis (CAD) of Parkinson's disease (PD). Deep polynomial network (DPN) is a novel supervised deep learning algorithm, which has excellent feature representation for small dataset. In this work, a stacked DPN (SDPN) based ensemble learning framework is proposed for diagnosis of PD, which can improve diagnostic accuracy for small dataset. In the proposed framework, SDPN was performed on each subset of extracted features from MRI images to generate new feature representation. The support vector machine (SVM) was then adopted to perform classification task on each subset. The ensemble learning algorithm was then performed on all the SVM classifiers to generate the final diagnosis for PD. The experimental results on the Parkinson's Progression Markers Initiative dataset (PPMI) showed that the proposed algorithm achieved the classification accuracy, sensitivity and specificity of 90.15%, 85.48% and 93.27%, respectively, with the brain network features, and it also got the classification accuracy of 87.18%, sensitivity of 86.90% and specificity of 87.27% on the multi-view features extracted from different brain regions. Moreover, the proposed algorithm outperformed other algorithms on the MRI dataset from PPMI. It suggests that the proposed SDPN-based ensemble learning framework has the feasibility and effectiveness for the CAD of PD.