【摘要】 目的 观察70岁以上老年心肌梗死急性期血清胱抑素C（cystatin C，CysC）水平，探讨急性心肌梗死后CysC水平变化的意义。 方法 顺序入选2010年7月-2011年7月期间70岁以上急性心肌梗死患者58例及正常对照58例。入选对象均经冠状动脉造影检查确诊或排除诊断，记录急性心肌梗死患者梗死部位和梗死相关血管，并计算Gensini积分。所有入选对象采血，使用乳胶增强免疫透射比浊法测定急性期血清CysC水平。 结果 心肌梗死急性期，血清CysC水平低于正常对照组（Plt;0.05）；不同冠状动脉病变评分与血清CysC水平呈负相关，Gensini积分越高，血清CysC水平越低。 结论 血清CysC与冠心病关系密切。检测CysC，为冠心病的风险预测、老年患者危险分层和治疗提供一条新的线索和途径。
Objective To evaluate the efficacy of normalization management on prognosis in elderly patients with coronary artery disease, in aspects of drug compliance, readmission rate and quality of life. Methods A total of 110 patients above 65 years old with coronary artery disease visiting West China Hospital from August 2010 to February 2011 were investigated. The patients were divided into two groups: the intensive management group (n=55) and the general management group (n=55). The measures such as regular follow-up, regular examination and medical education were conducted in the intensive management group, and the two groups were observed in aspects of drug compliance, readmission rate and quality of life. Results After 1-year follow-up, the percentages of patients taking aspirin/clopidogrel (98.18% vs. 67.27%, Plt;0.05), nitrate (85.45% vs. 40.00%, Plt;0.05), ACEI/ARB (56.36% vs. 18.18%, Plt;0.05), β receptor blocker (58.18% vs. 29.09%, Plt;0.05) and statin (94.55% vs. 32.73%, Plt;0.05) were higher in the intensive management group than those in the general management group. Also, the readmission rate was lower (12.73% vs. 41.42%, Plt;0.05) and the score of quality of life was higher in the intensive management group than that in the general management group. Conclusion The normalization management guided by evidence-based medicine for the elderly patients with coronary artery disease is helpful to improve the drug compliance, reduce the readmission rate, and improve the quality of life.
In order to solve the predicament brought by the aging society, China has promoted the combination of medical care. Hence, rehabilitation for the elderly may become a new strategy for the development of rehabilitation. According to the development of three different models and situation of China, we focus on improving the function of the elderly based on the families, the communities and institutions. Finally, the elderly feel happy and respected by getting professional means of rehabilitation while aging. The insurance system is the guarantee of promoting rehabilitation for the elderly and the " ternary theory” rehabilitation theory should be the guide concept. In the future, the trend is to build China’s three-level " intelligent rehabilitation for the elderly system” by artificial intelligence and big data cloud platform.
Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detection and classification. To address the negative effect of label imbalance in ECG data on arrhythmia classification, this paper proposes a nested long short-term memory network (NLSTM) model for unbalanced ECG signal classification. The NLSTM is built to learn and memorize the temporal characteristics in complex signals, and the focal loss function is used to reduce the weights of easily identifiable samples. Then the residual attention mechanism is used to modify the assigned weights according to the importance of sample characteristic to solve the sample imbalance problem. Then the synthetic minority over-sampling technique is used to perform a simple manual oversampling process on the Massachusetts institute of technology and Beth Israel hospital arrhythmia (MIT-BIH-AR) database to further increase the classification accuracy of the model. Finally, the MIT-BIH arrhythmia database is applied to experimentally verify the above algorithms. The experimental results show that the proposed method can effectively solve the issues of imbalanced samples and unremarkable features in ECG signals, and the overall accuracy of the model reaches 98.34%. It also significantly improves the recognition and classification of minority samples and has provided a new feasible method for ECG-assisted diagnosis, which has practical application significance.
At present, the incidence of Parkinson’s disease (PD) is gradually increasing. This seriously affects the quality of life of patients, and the burden of diagnosis and treatment is increasing. However, the disease is difficult to intervene in early stage as early monitoring means are limited. Aiming to find an effective biomarker of PD, this work extracted correlation between each pair of electroencephalogram (EEG) channels for each frequency band using weighted symbolic mutual information and k-means clustering. The results showed that State1 of Beta frequency band (P = 0.034) and State5 of Gamma frequency band (P = 0.010) could be used to differentiate health controls and off-medication Parkinson’s disease patients. These findings indicated that there were significant differences in the resting channel-wise correlation states between PD patients and healthy subjects. However, no significant differences were found between PD-on and PD-off patients, and between PD-on patients and healthy controls. This may provide a clinical diagnosis reference for Parkinson’s disease.