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.
【摘要】目的介紹華西醫院支援西部地區衛生工程項目的實踐和成效。方法過去5年間，華西醫院響應國家號召，通過各種幫扶形式，開展了一系列對口支援活動。結果華西醫院利用自身的資源優勢，通過各種幫扶形式，提高基層醫院的醫療救治水平和綜合服務能力，為建立城市支援農村衛生工作的長效機制進行了積極的實踐和探索，取得顯著成效。結論基層衛生事業與人民健康需求和現代醫學進步存在著相當的差距，醫療體制改革對部屬部管醫院的對口支援提出了更高的要求，對口支援的許多細節還需要我們去進一步完善。【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.
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.
Aiming at the status of muscle and joint damage caused on surgeons keeping surgical posture for a long time, this paper designs a medical multi-position auxiliary support exoskeleton with multi-joint mechanism by analyzing the surgical postures and conducting conformational studies on different joints respectively. Then by establishing a human-machine static model, this study obtains the joint torque and joint force before and after the human body wears the exoskeleton, and calibrates the strength of the exoskeleton with finite element analysis software. The results show that the maximum stress of the exoskeleton is less than the material strength requirements, the overall deformation is small, and the structural strength of the exoskeleton meets the use requirements. Finally, in this study, subjects were selected to participate in the plantar pressure test and biomechanical simulation with the man-machine static model, and the results were analyzed in terms of plantar pressure, joint torque and joint force, muscle force and overall muscle metabolism to assess the exoskeleton support performance. The results show that the exoskeleton has better support for the whole body and can reduce the musculoskeletal burden. The exoskeleton mechanism in this study better matches the actual working needs of surgeons and provides a new paradigm for the design of medical support exoskeleton mechanism.
Hypertension is the primary disease that endangers human health. A convenient and accurate blood pressure measurement method can help to prevent the hypertension. This paper proposed a continuous blood pressure measurement method based on facial video signal. Firstly, color distortion filtering and independent component analysis were used to extract the video pulse wave of the region of interest in the facial video signal, and the multi-dimensional feature extraction of the pulse wave was preformed based on the time-frequency domain and physiological principles; Secondly, an integrated feature selection method was designed to extract the universal optimal feature subset; After that, we compared the single person blood pressure measurement models established by Elman neural network based on particle swarm optimization, support vector machine (SVM) and deep belief network; Finally, we used SVM algorithm to build a general blood pressure prediction model, which was compared and evaluated with the real blood pressure value. The experimental results showed that the blood pressure measurement results based on facial video were in good agreement with the standard blood pressure values. Comparing the estimated blood pressure from the video with standard blood pressure value, the mean absolute error (MAE) of systolic blood pressure was 4.9 mm Hg with a standard deviation (STD) of 5.9 mm Hg, and the MAE of diastolic blood pressure was 4.6 mm Hg with a STD of 5.0 mm Hg, which met the AAMI standards. The non-contact blood pressure measurement method based on video stream proposed in this paper can be used for blood pressure measurement.
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.
ObjectiveTo explore the effect of full nutritional management pattern on perioperative nutritional status in patients with head and neck malignancies. MethodsSixty-four patients with head and neck cancer treated in our department between March 2012 and June 2013 were randomly divided into control group and study group with 32 in each. The control group received conventional dietary guidance, while patients in the study group were given full nutritional management. Nutritional Risk Screening Scale 2002 (NRS-2002) was used for nutrition screening and assessment before surgery (after admission) and after surgery (3 days after surgery). The study group received full nutritional support, along with nutrition-related physical examination and biochemical tests, and observation of postoperative complications, and hospital stay and costs were also observed. ResultsNutritional risk existed in 29.7%-48.4% of the head and neck cancer patients during various stages of the perioperative period. Through the full nutritional support, patients in the study group had a significantly lower risk than those in the control group (P<0.01). Body mass index, triceps skinfold thickness, mid-arm muscle circumference, prealbumin, and creatinine in the study group were significantly more improved compared with the control group (P<0.01). No significant difference was detected in blood urea and serum albumin between the two groups. Postoperative complications in the study group was significantly lower (P<0.05), and hospital stay and costs were significantly lower than the control group (P<0.001). ConclusionFull nutritional management pattern can significantly improve the perioperative nutritional status in head and neck cancer patients. Early detection of nutritional risk and malnutrition (foot) in the patients and carrying out normal and scientific nutrition intervention are helpful in the rehabilitation of these patients. We suggest that qualified hospitals should carry out the full nutritional management model managed by a Nutrition Support Team for patients with malignancies.
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.
ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.
Because of the diversity and complexity of clinical indicators, it is difficult to establish a comprehensive and reliable prediction model for induction of labor (IOL) outcomes with existing methods. This study aims to analyze the clinical indicators related to IOL and to develop and evaluate a prediction model based on a small-sample of data. The study population consisted of a total of 90 pregnant women who underwent IOL between February 2023 and January 2024 at the Shanghai First Maternity and Infant Healthcare Hospital, and a total of 52 clinical indicators were recorded. Maximal information coefficient (MIC) was used to select features for clinical indicators to reduce the risk of overfitting caused by high-dimensional features. Then, based on the features selected by MIC, the support vector machine (SVM) model based on small samples was compared and analyzed with the fully connected neural network (FCNN) model based on large samples in deep learning, and the receiver operating characteristic (ROC) curve was given. By calculating the MIC score, the final feature dimension was reduced from 55 to 15, and the area under curve (AUC) of the SVM model was improved from 0.872 before feature selection to 0.923. Model comparison results showed that SVM had better prediction performance than FCNN. This study demonstrates that SVM successfully predicted IOL outcomes, and the MIC feature selection effectively improves the model’s generalization ability, making the prediction results more stable. This study provides a reliable method for predicting the outcome of induced labor with potential clinical applications.