SARS临床特征分析(附56例病例报告)

目的 :归纳和探讨临床诊断SARS病例的临床特征、实验室检查及X线胸片变化规律。方法 :对 5 6例临床诊断为SARS病例的临床表现、血常规、血氧饱和度和X线胸片等资料进行收集整理、归纳总结和分析。结果 :本组病例全部以高热和全身不适、乏力和肌肉酸痛为主诉症状 ;2 0例有明确一次性接触史者的平均潜伏期为 ( 4 .70± 0 .92 )d(X±SD ) ;大部分病例有干咳 ( 71 .4% )、气短 ( 4 8.2 % ) ,少数病例在病程中有短暂的胸痛、头痛和鼻塞 ,但无 1例发生腹泻。血常规检查 ,1 0 0 %的患者有白细胞减少 ( <4× 1 0 9/L)和淋巴细胞计数减少 ( <1 .5× 1 0 9/L) ,5 3.6%的病例出现血氧饱和度降低 ( <90 % )。X线胸片异常改变中双侧云雾状阴影占 62 .5 % ,单侧病变占 32 .1 % ,单一肺叶病变的仅占 5 .4%。结论 :本组SARS病人的主要临床症状与WHO提示的有关症状基本符合 ,但未见腹泻和血小板下降者 ;X线胸片单侧病变也占相当比例 ( 32 .1 + 5 .4) %。胸片病变局限可能与及时治疗干预有关。     

Learning and modeling biosignatures from tissue images

Ideally biosignatures can be detected at the early infection phase and used both for developing diagnostic patterns and for prognostic triage. Such biosignatures are important for vaccine validation and to provide risk stratification to a population such as for the identification of individuals who are exposed to biological or chemical agents and who are at high risk for developing an infection. The research goal is to detect broad based biosignature models and is initially focused on developing effective computer-augmented pathology tied to animal models developed at the University of New Mexico (UNM). Using lung tissue from infected and nai ve mice, feature extraction from images of the tissue under a specialized microscope, and Bayesian networks to analyze the data sets of features, we were able to differentiate normal from diseased samples and viral from bacterial samples in mid to late stages of infection. This effort has shown the potential effectiveness of computer-augmented pathology in this application. The extended research intends to couple analysis of serum, microarray analysis of organs, proteomic data and the pathology. The rational for the current invasive procedure on animal models is to facilitate the development of data analysis and machine learning techniques that can eventually be generalized to the task of discovering non-invasive and early stage biosignatures for human models.     

Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy

In this paper, segmentation of blood vessels from colour retinal images using a novel clustering algorithm with a partial supervision strategy is proposed. The proposed clustering algorithm, which is a RAdius based Clustering ALgorithm (RACAL), uses a distance based principle to map the distributions of the data by utilising the premise that clusters are determined by a distance parameter, without having to specify the number of clusters. Additionally, the proposed clustering algorithm is enhanced with a partial supervision strategy and it is demonstrated that it is able to segment blood vessels of small diameters and low contrasts. Results are compared with those from the KNN classifier and show that the proposed RACAL performs better than the KNN in case of abnormal images as it succeeds in segmenting small and low contrast blood vessels, while it achieves comparable results for normal images. For automation process, RACAL can be used as a classifier and results show that it performs better than the KNN classifier in both normal and abnormal images.     

抗肿瘤多肽类药物的来源及其机制的研究进展

恶性肿瘤已成为严重影响人类健康的一种疾病,其治疗手段一直是人们所探索的内容,药物治疗就是其中一种治疗方法。与传统化疗药物相比,抗肿瘤多肽类药物以其分子量小、特异性强、毒性低等特点作为新的治疗肿瘤药物一直广受人们关注。多肽的来源广泛,有存在于天然动植物及微生物体内,也可以通过蛋白质酶解或人工合成得到。近年来,越来越多的多肽类药物被发现除抗菌外还具有抗肿瘤的作用,其抗肿瘤的机制多种多样但尚未完全清楚,是许多研究者研究的重点。本文从多肽类药物的来源与特点及它们的抗肿瘤机制等方面对多肽类药物的研究进展进行综述。     

心脏磁共振特征追踪技术定量评价肥厚型心肌病患者心肌形变

目的 探讨心脏磁共振（CMR）特征追踪（CMR-FT）技术定量评价肥厚型心肌病（HCM）患者心肌形变的价值。方法 对16例HCM患者（HCM组）及18名健康志愿者（正常对照组）采用CMR-FT技术行CMR检查。比较两组间左心室舒张末期容积（LVEDV）、收缩末期容积（LVESV）、左心室射血分数（LVEF）、左心室质量（LVMASS）及左心室心肌整体径向应变（RS）、环向应变（CS）的差异。并分析HCM患者各节段室壁厚度与节段性RS、CS的相关性及整体RS、CS与LVEDV、LVESV、LVEF、LVMASS间的相关性。结果 HCM组的LVMASS高于正常对照组[（133.74±79.13）g vs（76.87±14.15）g,P=0.01]。两组间LVEDV、LVESV、LVEF差异均无统计学意义（P均>0.05）。HCM组心肌整体RS、CS值均明显低于正常对照组[RS：（27.05±13.35）% vs（40.62±4.92）%,P<0.01;CS：（-8.68±5.56）% vs（-20.73±1.56）%,P<0.01]。HCM患者各节段室壁厚度与节段性RS（r=-0.41,P<0.01）、CS（r=0.28,P<0.01）间无相关性;心肌整体RS（r=-0.36、-0.41、0.22、-0.36）、CS（r=0.34、0.10、0.22、0.42）与LVEDV、LVESV、LVEF、LVMASS间无相关性（P均>0.05）。结论 CMR-FT有助于定量评估HCM患者心肌形变情况。     

引入关联维的表面肌电信号的特征提取

考虑到表面肌电信号的非平稳特性,本研究在传统特征提取的基础上,又引入了非线性动力学中的关联维,通过小波系数的标准差和关联维重新构造特征向量,将其送入自组织映射网络对拇指弯曲、食指弯曲和无名指弯曲三种手势动作进行分类识别。结果表明:引入关联维的特征提取方法其识别正确率明显优于传统的小波变换的方法。可见,关联维作为一种新的特征参数,为肌电信号的特征提取提供了新的思路。     

术前访视可有效缓解病人恐惧情绪

目的：探讨术前访视可有效缓解病人恐惧情绪。方法：选择100例中、下腹部择期手术病人,年龄20～50岁。每组50例。观察组（Ⅰ组）术前一天对病人进行访视,与病人进行面对面的交谈,让病人讲出对手术中的顾虑与想法,与此同时,测量病人的血压、脉搏、呼吸等生命体征。对照组（Ⅱ组）,术前不与病人交谈,只测生命体征。结果：通过术前一天对病人进行访视,病人进入手术室后,心态平稳,情绪稳定,既无恐惧感,又无陌生感,病人入手术室时测血压、脉搏、呼吸与术前所测的生命体征基本相同。Ⅱ组血压、脉搏、呼吸明显升高。两组P〈0．05,有显著意义。结论：术前访视病人,明显使病人情绪稳定,有利于手术的顺利进行。     

基于多生理信号的情绪初步识别

以生理信号分析为主,表情行为观察和情绪主观感受评价为辅,对多名被试的情绪进行识别.60名大学女生接受恐惧-快乐-轻松的情绪诱发,有效数据55名,对应每个情绪片段,根据信号标记以及GSR微分,截取1min的生理信号进行处理和分析,应用SPSS对各生理参数进行情绪的单因素方差分析,然后采用逐步多类判别法,提取特征参数以识别情绪.结果表明HR、HRV、R波、T波各生理参数对情绪较敏感;提取出HFP,HR max,PNN50,LF/HF,Ratio,LFP,Mean NN 7个特征参数,构建情绪判别函数Fuction1,Fuction2和Z1、Z2,Z3;轻松的判别正确率为88.0%,快乐的为92.0%,恐惧的为80.0%,总体判别正确率为86.7%.以生理信号分析为主,辅助表情行为观察和情绪主观感受报告,是一种有效的情绪识别方法,所得数据客观、准确,提高了情绪识别率.     

A time-frequency feature extraction scheme for the automated detection of binaural interaction in auditory brainstem responses

The binaural interaction component (BIC), the difference between the summed monaurally evoked potentials of each ear and the binaurally evoked brainstem potentials, has been shown to be related to directional hearing. However, the detection of the ß-peak as the most consistent part of the BIC is often difficult. Furthermore, there is no clearly defined signal feature characterizing the difference between the monaurally and the binaurally evoked brainstem responses. A closer look at the signals shows that amplitude differences as well as latency differences and variations in wave V slopes could be the reason for the formation of a ß-peak. Using a time-scale feature extraction scheme, we were able to define a signal feature (morphological local discriminant bases ( MLDB) coefficient 1) that accounts for the difference between the sum of the monaurally and binaurally evoked brainstem potentials. With use of this signal feature, reliable automated detection of differences between monaurally and binaurally evoked potentials is possible. As coefficient 1 replicates the behaviour of subjective measurements as well as of the BIC measurements, it can also be seen as a correlate of binaural interaction. With use of this signal feature, it is possible to judge from a given binaurally evoked potential whether it contains information on binaural interaction or not, without comparing it to the sum of the monaurally evoked brainstem responses. Consequently, binaural interaction can be assessed by one, instead of three, measurements by using the method described in this paper.     

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.