【摘要】 目的 总结开展“优质护理服务示范工程活动”以来，护士满意度提高的原因与经验。 方法 分别于2010年1月和11月采用一般情况调查表及明尼苏达工作满意度问卷短式量表调查干部/老年科的护士在开展“优质护理服务示范工程活动”前后的工作满意度。 结果 开展“优质护理服务示范工程活动”1年以来，护士的内在满意度上升了35.27%，外在满意度上升了29.25%，一般满意度上升了27%。 结论 干部/老年科通过提高护士对各岗位的价值与责任的认可，科学规划护士的职业生涯，完善科室文化建设、薪酬与激励机制使护士的职业成就感、自身价值满意度均有不同程度提高。【Abstract】 Objective To summarize the reasons and experience of enhancing nurse′s satisfaction after improving the “high-quality nursing services”. Methods A general questionnaire and Minnesota Satisfaction Questionnaire (MSQ) were used to investigate satisfactions of nurses working at the senior leader/ person′s wards before and after improving the activity on “high-quality nursing service”. Results One year later, the inner satisfactions of participates increased 35.27%, the outer satisfactions increased 29.25%, and the general satisfactions increased 27%. Conclusion Nurse′s professional achievability and the satisfaction on self-value increase after enhancing professional value and responsibility of nurse, planning reasonably professional career of nurse, and perfecting culture construction, and the salary and encourage mechanism.
Heart sound is one of the common medical signals for diagnosing cardiovascular diseases. This paper studies the binary classification between normal or abnormal heart sounds, and proposes a heart sound classification algorithm based on the joint decision of extreme gradient boosting (XGBoost) and deep neural network, achieving a further improvement in feature extraction and model accuracy. First, the preprocessed heart sound recordings are segmented into four status, and five categories of features are extracted from the signals based on segmentation. The first four categories of features are sieved through recursive feature elimination, which is used as the input of the XGBoost classifier. The last category is the Mel-frequency cepstral coefficient (MFCC), which is used as the input of long short-term memory network (LSTM). Considering the imbalance of the data set, these two classifiers are both improved with weights. Finally, the heterogeneous integrated decision method is adopted to obtain the prediction. The algorithm was applied to the open heart sound database of the PhysioNet Computing in Cardiology(CINC) Challenge in 2016 on the PhysioNet website, to test the sensitivity, specificity, modified accuracy and F score. The results were 93%, 89.4%, 91.2% and 91.3% respectively. Compared with the results of machine learning, convolutional neural networks (CNN) and other methods used by other researchers, the accuracy and sensibility have been obviously improved, which proves that the method in this paper could effectively improve the accuracy of heart sound signal classification, and has great potential in the clinical auxiliary diagnosis application of some cardiovascular diseases.
The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn’t during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.
Objective To prepare Pluronic F-127 composite gel loaded with transforming growth factor β3 (TGF-β3) and bone marrow mesenchymal stem cells (BMSCs) and observe its osteogenesis and angiogenesis effects in vivo and in vitro. Methods BMSCs were isolated from the tibial and femoral bone marrow of New Zealand white rabbits and passaged, and the 3rd generation cells were used for subsequent experiments after identification of osteogenic and adipogenic induction. Pluronic F-127 powder and TGF-β3 were dissolved in L-DMEM medium to prepare Pluronic F-127 gel, TGF-β3+Pluronic F-127 gel, BMSCs+Pluronic F-127 gel, and TGF-β3+BMSCs+Pluronic F-127 gel. The 3rd generation of BMSCs were cultured with L-DMEM medium (group A), osteogenic induction medium (group B), osteogenic induction medium containing Pluronic F-127 gel (group C), and osteogenic induction medium containing TGF-β3+Pluronic F-127 gel (group D), respectively. After 14 days of culturing, alkaline phosphatase (ALP) staining and Alizarin red staining were used to observe the osteogenesis. In addition, the BMSCs were cultured with L-DMEM medium containing Pluronic F-127 gel (experimental group) and L-DMEM medium (control group) for 1, 2, 3, and 4 days, respectively. And the cell proliferation was detected by MTT assay. Ten New Zealand white rabbits were taken to prepare the maxillary sinus lift models, and Pluronic F-127 gel (group A), TGF-β3+Pluronic F-127 gel (group B), BMSCs+Pluronic F-127 gel (group C), and TGF-β3+BMSCs+Pluronic F-127 gel (group D) were injected into the bone defects, respectively. On the 8th week, imaging examination and HE staining were used to observe the formation of new bone, immunohistochemical staining was used to observe the expression of vascular endothelial growth factor (VEGF) and bone morphogenetic protein 2 (BMP-2) in bone tissue, and Western blot was used to detect the relative expressions of VEGF, oncostatin M (OSM), and BMP-4 proteins in bone tissue. Results Osteogenic and adipogenic induction identified the isolated and cultured cells as BMSCs. In vitro staining showed that ALP activity and Alizarin red concentration in group D were significantly higher than those in other groups (P<0.05). MTT assay showed that the absorbency (A) value of the two groups increased gradually, and there was no significant difference between the groups at each time point (P>0.05). In vivo experimental imaging examination showed that the bone mineral density and osteogenic continuity of group D were the best, and the proportion of new bone volume was superior to other groups (P<0.05). HE staining showed that compared with other groups, bone trabeculae in group D were dense and arranged regularly, on which a large number of osteoblasts and osteoclasts were distributed, and a large number of new bone formation could be seen. Immunohistochemical staining showed the strong positive expressions of BMP-2 and VEGF in group D (P<0.05); Western blot detection showed that the relative expressions of VEGF, OSM, and BMP-4 proteins in group D were significantly higher than those in other groups (P<0.05). Conclusion The BMSCs in Pluronic F-127 composite gel loaded with TGF-β3 and BMSCs can be induced to differentiate into osteoblasts, and the composite gel has no toxic effect on cells, and has obvious osteogenesis and angiogenesis in the maxillary sinus of rabbits.
Objective To develop a Matlab toolbox to improve the efficiency of musculoskeletal kinematics analysis while ensuring the consistency of musculoskeletal kinematics analysis process and results. Methods Adopted the design concept of “Batch processing tedious operation”, based on the Matlab connection OpenSim interface function ensures the consistency of musculoskeletal kinematics analysis process and results, the functional programming was applied to package the five steps for scale, inverse kinematics analysis, residual reduction algorithm, static optimization analysis, and joint reaction analysis of musculoskeletal kinematics analysis as functional functions, and command programming was applied to analyze musculoskeletal movements in large numbers of patients. A toolbox called LLMKA (Lower Limbs Musculoskeletal Kinematics Analysis) was developed. Taking 120 patients with medial knee osteoarthritis as the research object, a clinical researcher was selected using the LLMKA toolbox and OpenSim to test whether the analysis process and results were consistent between the two methods. The researcher used the LLMKA toolbox again to conduct musculoskeletal kinematics analysis in 120 patients to verify whether the use of this toolbox could improve the efficiency of musculoskeletal kinematics analysis compared with using OpenSim. Results Using the LLMKA toolbox could analyze musculoskeletal kinematics analysis in a large number of patients, and the analysis process and results were consistent with the use of OpenSim. Compared to using OpenSim, musculoskeletal kinematics analysis was completed in 120 patients using the LLMKA toolbox with only 2 operations were needed to enter the patient body mass data, operating steps decreased by 99.19%, total analysis time by 66.84%, and manual participation time by 99.72%, just need 0.079 1 hour (4 minutes and 45 seconds). Conclusion The LLMKA toolbox can complete a large number of musculoskeletal kinematics analysis in patients with one click in a way that is consistent in process and results with using OpenSim, reducing the total time of musculoskeletal kinematics analysis, and liberating clinical researchers from cumbersome steps, making more energy into the clinical significance of musculoskeletal kinematics analysis results.
Aiming at the problems of obscure clinical auscultation features of pulmonary hypertension associated with congenital heart disease and the complexity of existing machine-aided diagnostic algorithms, an algorithm based on the statistical characteristics of the high-frequency components of the second heart sound signal is proposed. Firstly, an endpoint detection adaptive segmentation method is employed to extract the second heart sounds. Subsequently, the high-frequency component of the heart sound is decomposed using the discrete wavelet transform. Statistical features including the Hurst exponent, Lempel-Ziv information and sample entropy are extracted from this component. Finally, the extracted features are utilized to train an extreme gradient boosting algorithm (XGBoost) classifier, which achieves an accuracy of 80.45% in triple classification. Notably, this method eliminates the need for a noise reduction algorithm, allows for swift feature extraction, and achieves effective multi-classification using only three features. It is promising for early screening of pulmonary hypertension associated with congenital heart disease.