樟芝多糖通过降低NLRP3 Caspase1炎性小体表达改善帕金森小鼠神经行为学的机制研究

目的:研究樟芝多糖通过降低NLRP3-Caspase1炎性小体表达改善6-OHDA构建的帕金森小鼠模型的行为学机制。方法:利用6-OHDA脑内注射构建帕金森小鼠模型，通过酪氨酸羟化酶（TH）免疫组化染色和行为学判定小鼠模型的构建成功。利用樟芝多糖进行干预，分别在干预前、干预后的第1、3、7天4个时间点进行神经行为学实验，分别采用转棒实验、爬杆实验检测小鼠自主行为能力以及协调能力，4个时间点取小鼠尾静脉外周血采用ELISA法检测外周血中Caspase1和IL-1β的表达，樟芝多糖干预第7天时待进行完行为学实验后小鼠断颈处死，取小鼠脑组织-纹状体，Western blot法检测纹状体中Caspase1、proCaspase1、NLRP3的表达，高效液相色谱检测纹状体中单胺类神经递质的表达，RT-QPCR检测Caspase1、NLRP3、IL-1β、IL-4、IL-6的mRNA表达。NISSl染色检测小鼠脑组织神经细胞凋亡情况。 结果:6-OHDA脑内注射可以造成小鼠帕金森样病变，且TH蛋白表达显著下调，樟芝多糖干预后小鼠的行为学得到显著改善（P<0.05），纹状体中Caspase1、proCaspase1、NLRP3的表达显著下调，与模型小鼠相比具有统计学差异（P<0.05），且相关炎症因子Caspase1、NLRP3、IL-1β、IL-4、IL-6的mRNA表达下调（P<0.05），纹状体中单胺类神经递质表达上升（P<0.05）。结论:樟芝多糖可以通过下调NLRP3-Caspase1炎性小体表达来改善6-OHDA构建的帕金森小鼠模型行为改善，这可能是樟芝多糖治疗帕金森的机制之一。     

Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging

Early and accurate diagnosis of Parkinson’s disease (PD) is important for early management, proper prognostication and for initiating neuroprotective therapies once they become available. Recent neuroimaging techniques such as dopaminergic imaging using single photon emission computed tomography (SPECT) with ¹²³I-Ioflupane (DaTSCAN) have shown to detect even early stages of the disease. In this paper, we use the striatal binding ratio (SBR) values that are calculated from the ¹²³I-Ioflupane SPECT scans (as obtained from the Parkinson’s progression markers initiative (PPMI) database) for developing automatic classification and prediction/prognostic models for early PD. We used support vector machine (SVM) and logistic regression in the model building process. We observe that the SVM classifier with RBF kernel produced a high accuracy of more than 96% in classifying subjects into early PD and healthy normal; and the logistic model for estimating the risk of PD also produced high degree of fitting with statistical significance indicating its usefulness in PD risk estimation. Hence, we infer that such models have the potential to aid the clinicians in the PD diagnostic process.     

基于多维筛分类器的可视化帕金森病诊断

针对目前基于模式识别的语音障碍帕金森病诊断可解释性差、可推广性差的问题,本文提出基于多维筛分类器的可视化帕金森病诊断。该分类器具有全程可视化的特点,在保证诊断精度的同时,可以将特征进行可视化表示。可视化的引入不但使操作者了解不同特征对于诊断的重要性,而且可以发现最具诊断价值的特征,有助于简化帕金森病的诊断过程并提高诊断水平。     

基于医疗文本数据聚类的帕金森病早期诊断预测

针对多发于老龄人群的帕金森病（PD）的早期智能化诊断的问题，提出基于医疗检测文本信息数据的聚类技术来对PD进行分析预测。首先，对原始数据集进行预处理以获取有效特征信息，并通过主成分分析（PCA）方法将原始特征分别降维到8个不同维度的维度空间；然后，应用5个传统的经典聚类模型和3种不同的聚类集成方法分别对8个维度空间的数据进行聚类；最后，采用4个聚类性能指标来预测数据集中的多巴胺异常PD患者、健康体和无多巴胺缺失（SWEDD） PD患者。仿真结果显示，PCA特征维度值取30时，高斯混合模型（GMM）的聚类准确度达到89.12%；PCA特征维度值取70时，谱聚类（SC）的聚类准确度达到61.41%；PCA特征维度值取80时，元聚类算法（MCLA）的聚类准确度达到59.62%。对比实验结果表明，5种经典聚类方法中，PCA的特征维度值小于40时，高斯混合模型聚类效果最佳；3种聚类集成方法中，对于不同的特征维度，MCLA的聚类性能均表现优异，进而为PD的早期智能化辅助诊断提供了技术和理论支撑。     

基于MVC的帕金森病病历管理系统的设计与构建

帕金森病病历管理系统是进行帕金森病临床数据收集和管理的重要途径,也是进行数据挖掘的重要工具。本文以Visual Studio 2008作为开发环境,C#作为开发工具,Microsoft Access 2003作为后台数据库,从程序上实现基于MVC的帕金森病病历管理系统。系统能实现数据的增删、查改以及数据备份和还原等操作,达到预期的效果。     

Parkinson’s Disease Detection Using Biogeography-Based Optimization

In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection. The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms, and consequently, it served as a promising new robust tool with excellent PD diagnosis performance.     

Episodic memory and metamemory in Parkinson’s disease patients.

This study investigated episodic memory and metamemory for verbs and nouns in patients who have cognitive impairments associated with Parkinson’s disease (PD). PD patients and healthy control participants were asked to recall word pairs and provide feeling-of-knowing (FOK) judgments for the items they were unable to recall. This was followed by a 4-alternative recognition test. PD patients were impaired in both recall and recognition, compared with controls. In terms of metamemory, PD patients were less confident in their ability to recognize the unrecalled items in a future recognition test. Most important, accuracy of PD patients’ FOK judgments was not above chance and was lower than that of control participants. The PD group correctly recognized fewer verbs than nouns, but type of material (verb vs. noun) had no impact on recall or FOK judgments. In addition, contribution of executive functions to FOK accuracy was different in PD patients and controls. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

“Parkinson”药物药理反应测试仪的设计

本文介绍了一种基于单片机AT89C52的自行开发、构思新颖的“Parkinson”药物药理反应测试仪的工作原理及软、硬件的设计与实现方法。     

基于掩蔽自监督语音特征提取的帕金森病检测方法

帕金森病是一种常见的慢性神经系统疾病,构音障碍是帕金森病的早期症状之一。基于语音进行帕金森病的辅助诊疗有助于更早发现病情和观测病情的发展。传统方法常通过对语音特征(如频率微扰、振幅微扰等)的参数计算来进行疾病评估,然而这些特征可能无法全面反映所有的病理现象,从而影响了检测和评估的准确率。为更好地提取帕金森病患者语音中的病理信息,提升检测和评估的准确率,该文提出一种基于掩蔽自监督语音特征提取的帕金森病检测方法。首先,从帕金森病患者的原始语音中提取Mel语谱图特征,得到患者富含病理特征的全局时序化表示;然后,对部分Mel语谱图特征进行掩蔽,并通过掩蔽自监督模型对掩蔽部分进行重构,从而学习到帕金森病患者语音特征的更高级表示。为解决帕金森病语音数据稀缺的问题,该文先在LibriSpeech公开数据集上进行掩蔽自监督模型的预训练,然后基于迁移学习的思想,利用帕金森病语音数据对预训练好的掩蔽自监督模型进行微调和加权求和,以提升该模型特征表示学习的性能。最终,使用随机森林和支持向量机分类器分别对提取好的语音特征进行分类,以实现帕金森病的检测。该文在MaxLittle公开数据集和课题组自采数据集上,采用10折交叉验证的方法验证了所提方法的有效性。结果表明,与传统的Mel语谱图特征检测方法和其他经典的自监督特征提取方法相比,所提方法在准确率、敏感度、特异度性能方面均有明显提升。     

Behavioral Phenotyping in a Murine Model of GBA1-Associated Parkinson Disease

Mutations in GBA1, the gene encoding glucocerebrosidase, are common genetic risk factors for Parkinson disease (PD). While the mechanism underlying this relationship is unclear, patients with GBA1-associated PD often have an earlier onset and faster progression than idiopathic PD. Previously, we modeled GBA1-associated PD by crossing gba haploinsufficient mice with mice overexpressing a human mutant α-synuclein transgene (SNCA^A53T), observing an earlier demise, shorter life span and faster symptom progression, although behavioral testing was not performed. To assess whether gba^+/−//SNCA^A53T mice exhibit a prodromal behavioral phenotype, we studied three cardinal PD features: olfactory discrimination, memory dysfunction, and motor function. The longitudinal performance of gba^+/−//SNCA^A53T (n = 8), SNCA^A53T (n = 9), gba^+/− (n = 10) and wildtype (n = 6) mice was evaluated between ages 8 and 23 months using the buried pellet test, novel object recognition test and the beam walk. Fifteen-month-old gba^+/−//SNCA^A53T mice showed more olfactory and motor deficits than wildtype mice. However, differences between gba^+/−//SNCA^A53T and SNCA^A53T mice generally did not reach statistical significance, possibly due to small sample sizes. Furthermore, while gba haploinsufficiency leads to a more rapid demise, this might not result in an earlier prodromal stage, and other factors, including aging, oxidative stress and epigenetics, may contribute to the more fulminant disease course.