南充市社区中老年人群血脂血糖现状水平分析

【摘要】 目的 了解社区中老年人群的血脂、血糖现状水平，探讨其与性别和年龄的关系，为本地区心脑血管疾病、糖尿病等疾病的防治提供诊疗依据。方法 对我市38078名社区居民采集清晨空腹血清标本，检测血糖（GLU）、甘油三酯（TG）、总胆固醇（TC）、高密度脂蛋白胆固醇（HDL C）和低密度脂蛋白胆固醇（LDL C）的结果，并对其进行年龄、性别分组统计分析。结果 不同性别和年龄组间的血脂、血糖水平差异有统计学意义（P＜005），74~89岁年龄组血脂、血糖总体水平高于其他各年龄组（P＜005）；女性血脂水平普遍高于男性（P＜005）。结论 中老年人群血脂、血糖异常率高，应定期对中老年人进行血脂、血糖监测，开展宣传教育活动，提高自我保健意识，预防高血脂、高血糖的发生和发展。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Proximal load transfer with a stemless uncemented femoral implant

Bone stock preservation is crucial when performing total hip replacement in young patients. The aim is to save good bone stock for a possible revision procedure. Furthermore, there is an increasing demand from young and active patients to receive a new joint which allows a normal or nearly normal life style. With this in mind, we began, in 1993, to develop a new femoral implant. The purpose of this ultra-short stem was a physiologic strain distribution on the proximal femur with a proximal load transfer from the implant to the femoral bone. Main features were an almost complete absence of the diaphyseal portion of the stem, a well defined lateral flare with load transfer on the lateral column of the femur, and a very high femoral neck cut. These innovations resulted in a conservative implant on both the bone stock and the soft tissues. This implant, in the first years, was recommended only for young and active patients. Over the last thirteen years, this project has undergone several modifications but the basic principles of the implant have remained the same. In the present review, we present the rationale, the surgical technique and the clinical and experimental results so far obtained with this implant.     

丹参对持续癫痫幼鼠脑损伤保护作用的实验研究

目的 探讨丹参对持续癫痫发作诱发幼鼠脑神经元损伤是否具有保护作用。方法 皮下及腹腔注射贝美格针诱发健康幼龄鼠癫痫持续状态发作。光镜下观察神经元病变情况；电镜观察海马神经元超微结构的改变。结果 持续癫痴组幼鼠脑组织光镜下可见明显的神经元病变。电镜下可见海马区神经元的超微结构病变。丹参治疗组神经元病变均轻于持续癫痴组；而正常对照组未见类似病变。结论 丹参在组织、细胞和亚细胞水平对持续癫病幼鼠脑神经元损伤具有一定的保护作用，为临床有效防治小儿惊厥性脑损伤提供了可靠的实验依据。