【摘要】目的介绍华西医院支援西部地区卫生工程项目的实践和成效。方法过去5年间，华西医院响应国家号召，通过各种帮扶形式，开展了一系列对口支援活动。结果华西医院利用自身的资源优势，通过各种帮扶形式，提高基层医院的医疗救治水平和综合服务能力，为建立城市支援农村卫生工作的长效机制进行了积极的实践和探索，取得显著成效。结论基层卫生事业与人民健康需求和现代医学进步存在着相当的差距，医疗体制改革对部属部管医院的对口支援提出了更高的要求，对口支援的许多细节还需要我们去进一步完善。【Abstract】Objective To introduce the practice and progress of the supportive rural hygiene program of West China Hospital. Methods In the past five years, West China Hospital had made a lot of supportive rural hygiene practice. Results West China Hospital made good use of its own advantages in resources to develop the treatment level and the comprehensive service capability of primary hospital. West China Hospital did a lot of practice to establish the effective system of assistance of city medical care to rural areas, and had already achieved remarkable effects. Conclusionre is a lot of disparity between the basic public health or the requirement of people and the modern medicine progress. Many details for support should be further consummated.
ObjectiveTo explore the correlation between quality of life and social support and anxiety level in children with epilepsy. MethodsA total of 207 children with epilepsy and their parents from March 2023 to December 2023 from Shanghai Children's Hospitalwere selected as the investigation objects, and the children's quality of life scale, Children's perceptive Social support Scale and PROMIS parental Report version anxiety brief form were used to investigate. The correlation between the quality of life of children with epilepsy and the level of social support and anxiety was analyzed. ResultsThe results of univariate analysis showed that the quality of life of children with epilepsy was affected by whether they had siblings and the frequency of onset in the past month (P<0.05). Pearson correlation analysis showed that social support was positively correlated with quality of life (P<0.05). The scores of anxiety and quality of life were negatively correlated (P<0.05). Social support was negatively correlated with anxiety scores (P<0.05). The results of multiple linear regression analysis showed that siblings, social support and anxiety were independent factors affecting the quality of life of children with epilepsy (P<0.05). ConclusionSocial support has a positive effect on the quality of life of children with epilepsy, anxiety level has a negative effect on the quality of life, and social support has a negative effect on anxiety. Therefore, clinical psychological support should be strengthened for children with epilepsy, fully mobilize their positive psychological factors, reduce their anxiety and other negative emotions, play a full range of social support, to achieve the goal of improving the quality of life.
Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.
Open reduction and internal fixation with plate and screw is one of the most widely used surgical methods in the treatment of proximal humeral fractures in the elderly. In recent years, more and more studies have shown that it is very important to strengthen the medial column support of the proximal humerus during the surgery. At present, orthopedists often use bone graft, bone cement, medial support screw and medial support plate to strengthen the support of the medial column of the proximal humerus when applying open reduction and internal fixation with plate and screw to treat proximal humeral fractures. Therefore, the methods of strengthening medial column support for proximal humerus fractures and their effects on maintaining fracture reduction, reducing postoperative complications and improving functional activities of shoulder joints after operation are reviewed in this paper. It aims to provide a certain reference for the individualized selection of medial support methods according to the fracture situation in the treatment of proximal humeral fractures.
Objective To determine whether lymph node-targeted chemotherapy with carbon nanoparticles absorbing 5-FU affects expressions of bcl-2, bax and caspase-3 in gastric cancer tissues, metastatic lymph nodes and normal gastric mucosa. Methods Twenty-eight patients with gastric cancer in our department were divided into lymph node-targeted chemotherapy (LNTC) group and control group from October 2005 to August 2006. The patients were treated with carbon nanoparticles absorbing 5-FU before operation in LNTC group and those were operated directly in control group. The gastric cancer tissues, metastatic lymph nodes and normal gastric mucosa were collected after operation. The expressions of bcl-2, bax and caspase-3 in those tissues were determined by immunohistochemical technique. Results In LNTC group, the positive expression rate of bcl-2 in gastric cancer tissues and metastatic lymph nodes was significantly lower than those in control group (28.6% vs . 78.6% , 25.0% vs . 70.0% , P ＜ 0.05), the positive expression rate of bax (85.7% vs . 28.6% , 80.0% vs . 30.0% ) and caspase-3 (57.1% vs . 14.3% , 55.0% vs . 15.0% ) in gastric cancer tissues and metastatic lymph nodes was significantly higher than those in control group ( P ＜ 0.05). The positive expression rate of bcl-2, bax and caspase-3 in normal gastric mucosa was not significantly different between two groups ( P ＞ 0.05). Conclusion The lymph node-targeted chemotherapy with carbon nanoparticles absorbing 5-FU can down-regulate the expression of bcl-2 and up-regulate the expression of bax and caspase-3 in gastric cancer tissues and metastatic lymph nodes, and therefore by affecting the expression levels of these apoptosis molecules may be one of the ways to induce tumor cell apoptosis.
Objective To assess the effectiveness of psychological intervention on post-stoke depression. Methods Such databases as the JBI Database of Systematic Review (1980 to June, 2010), The Cochrane Library (1980 to June, 2010), PubMed (1966 to 2010), CINAHL(1982 to May, 2000), CBM (1978 to 2010), and CNKI (1979 to 2010) were searched to collect randomized controlled trials (RCTs). In accordance with the predefined inclusion and exclusion criteria, the quality of included studies was evaluated, and then meta-analyses were performed by using RevMan 5.0 software. Results A total of 33 RCTs were included. The results of meta-analyses showed: (1) Compared with the control group, the short-term effect of psychological intervention was more effective in decreasing depression score. The subgroup analysis showed that the intervention effects at the time of four weeks, six weeks, eight weeks, and 12 weeks were better than those of the control group. (2) The long-term effect of psychological intervention was more effective in decreasing depression score. The subgroup analyses showed that the intervention effects at the interval of eight weeks, 24 weeks, and 48 weeks were better than those of the control group. (3) The combined or single application of either cognitive-behavioral psychotherapy or supportive psychotherapy was more effective in decreasing depression score than the control group. However, there was no significant difference between the general psychological treatment group and the control group. (4) The subgroup analyses showed that the different qualities of the included studies were more effective than those of the control group. Conclusion Various psychological intervention is effective in decreasing the patient’s depression score, and cognitive-behavioral therapy and supportive psychotherapy, especially, can significantly improve the depression state and promote recovery.
Heart sound signal is a kind of physiological signal with nonlinear and nonstationary features. In order to improve the accuracy and efficiency of the phonocardiogram (PCG) classification, a new method was proposed by means of support vector machine (SVM) in which the complete ensemble empirical modal decomposition with adaptive noise (CEEMDAN) permutation entropy was as the eigenvector of heart sound signal. Firstly, the PCG was decomposed by CEEMDAN into a number of intrinsic mode functions (IMFs) from high to low frequency. Secondly, the IMFs were sifted according to the correlation coefficient, energy factor and signal-to-noise ratio. Then the instantaneous frequency was extracted by Hilbert transform, and its permutation entropy was constituted into eigenvector. Finally, the accuracy of the method was verified by using a hundred PCG samples selected from the 2016 PhysioNet/CinC Challenge. The results showed that the accuracy rate of the proposed method could reach up to 87%. In comparison with the traditional EMD and EEMD permutation entropy methods, the accuracy rate was increased by 18%–24%, which demonstrates the efficiency of the proposed method.
The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive detection of radiation dose to casualties after nuclear and radiation emergencies. For in-vivo EPR spectrum analysis, manual labeling of peaks and calculation of signal intensity are often used, which have problems such as large workload and interference by subjective factors. In this study, a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine (SVM) technology, which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements. In this study, a spectrum analysis method based on genetic algorithm optimization neural network (GA-BPNN) was established, which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured. The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction, and could meet the needs of dose assessment in nuclear emergency. This study explored the application of machine learning methods in EPR spectrum processing, improved the intelligence level of EPR spectrum processing, and would help to enhance the efficiency of mass EPR spectra processing.
When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.
In order to improve the motion fluency and coordination of lower extremity exoskeleton robots and wearers, a pace recognition method of exoskeleton wearer is proposed base on inertial sensors. Firstly, the triaxial acceleration and triaxial angular velocity signals at the thigh and calf were collected by inertial sensors. Then the signal segment of 0.5 seconds before the current time was extracted by the time window method. And the Fourier transform coefficients in the frequency domain signal were used as eigenvalues. Then the support vector machine (SVM) and hidden Markov model (HMM) were combined as a classification model, which was trained and tested for pace recognition. Finally, the pace change rule and the human-machine interaction force were combined in this model and the current pace was predicted by the model. The experimental results showed that the pace intention of the lower extremity exoskeleton wearer could be effectively identified by the method proposed in this article. And the recognition rate of the seven pace patterns could reach 92.14%. It provides a new way for the smooth control of the exoskeleton.