ICA在糖尿病诊疗中的应用研究

用免疫酶法分析了糖尿病患者及甲状腺疾病患者血清中胰岛细胞抗体（ＩＣＡ）的水平。结果为经胰岛素吸收后,ＩＤＤＭ组阳性率为４６．７％（最高）,ＮＩＤＤＭ组阳性率为１３．３％,甲状腺疾病组（其中１例Ｇｒａｖｅｓ’病伴糖尿病ＩＣＡ阳性）阳性率为４％,健康组０。统计学分析,Ｐ〈０．０５,具有显著性差异,表明ＩＣＡ是Ⅰ型糖尿病的特异性免疫标志物,ＩＣＡ检测作为对ＩＤＤＭ与ＮＩＤＤＭ的鉴别诊断,特别是对ＬＡＤＡ     

Mast cell activation syndrome

In 1984, interstitial cystitis (IC) was considered a rare psychosomatic disorder in post-menopausal women. In 2014, the Interstitial Cystitis Association of America (ICA) celebrated its 30^th anniversary. We’ve come a long way since 1984 and great progress has been made. IC is now recognized as a condition that afflicts both men and women of all ages, including children and teenagers. It is not a psychiatric disorder. Though it was once thought to be an orphan disease (defined as affecting less than 200,000 people), we now know that there are millions of women and men who suffer from IC/BPS (bladder pain syndrome). In looking back over this period, there were seven key reasons why the ICA became so successful: an extremely dedicated ICA staff, Board of Directors and volunteers; a very strong Medical Advisory Board and participation of many other urologists from across the country and around the world; cooperation of the media; epidemiological studies; the ICA’s Pilot Research Program; our representation in Congress; and a strong working partnership with the National Institutes of Health (NIH). Our history may prove useful to other advocacy groups.     

Antineuroinflammatory Effect of SMTP-7 in Ischemic Mice

Background

Stachybotrys microspora triprenyl phenol-7 (SMTP-7) has both potentials of thrombolytic and neuroprotective effects, but its detailed neuroprotective mechanisms in ischemic stroke are still unclear. Here, we assessed the neuroprotective effects of SMTP-7 for anti-inflammatory and antiapoptosis mechanisms after 60 minutes of transient middle cerebral artery occlusion (tMCAO) in mice.

The role of diversity in data‐driven analysis of multi‐subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics

Data‐driven methods have been widely used in functional magnetic resonance imaging (fMRI) data analysis. They extract latent factors, generally, through the use of a simple generative model. Independent component analysis (ICA) and dictionary learning (DL) are two popular data‐driven methods that are based on two different forms of diversity—statistical properties of the data—statistical independence for ICA and sparsity for DL. Despite their popularity, the comparative advantage of emphasizing one property over another in the decomposition of fMRI data is not well understood. Such a comparison is made harder due to the differences in the modeling assumptions between ICA and DL, as well as within different ICA algorithms where each algorithm exploits a different form of diversity. In this paper, we propose the use of objective global measures, such as time course frequency power ratio, network connection summary, and graph theoretical metrics, to gain insight into the role that different types of diversity have on the analysis of fMRI data. Four ICA algorithms that account for different types of diversity and one DL algorithm are studied. We apply these algorithms to real fMRI data collected from patients with schizophrenia and healthy controls. Our results suggest that no one particular method has the best performance using all metrics, implying that the optimal method will change depending on the goal of the analysis. However, we note that in none of the scenarios we test the highly popular Infomax provides the best performance, demonstrating the cost of exploiting limited form of diversity.     

Resting-state EEG gamma power and theta–gamma coupling enhancement following high-frequency left dorsolateral prefrontal rTMS in patients with depression

Objective

We aimed to investigate neuromodulatory effects of high-frequency left dorsolateral prefrontal cortex repetitive transcranial magnetic stimulation (rTMS) on resting-state electroencephalography (EEG) and their clinical and cognitive correlates in patients with depression.

Reproducibility of the implant crown aesthetic index--rating aesthetics of single-implant crowns and adjacent soft tissues with regard to observer dental specialization

Aim: The Implant Crown Aesthetic (ICA) Index evaluates the aesthetic outcome of implant-supported single crowns in the anterior zone by awarding nine points for the shape, color, and surface characteristics of the crowns and surrounding soft tissue. The aim of this study was to measure the reproducibility of the ICA Index and assess the influence exerted by the examiner's degree of dental specialization.
Materials and Methods: Ten examiners (two general dentists, two prosthodontists, two oral surgeons, two orthodontists, and two dental technicians) applied the ICA Index to 23 implant-supported single crowns twice at an interval of 4 weeks. The inter- and intra-examiner ratings were analyzed. Cohen's kappa (K) was used to measure the interexaminer reliability of estimations by two appraisers at a significance level of p  < .05.
Results: Within the various parameters, the observer agreement ranged 53 to 81%. All the examiners achieved moderate agreement between the first and second ratings, whereby Cohen's kappa was 0.49 ( p  < .001). The most agreement was obtained by surgeons (K = 0.62, substantial) and the least by orthodontists (K = 0.24, sufficient). The lowest level of agreement with other occupational groups was manifested by the orthodontists (≤44.3%). The ICA Index produced a moderate Cohen's kappa of 0.42 and agreement amounting to 67% between the two ratings within the occupational groups. There was minimum agreement among the occupational groups (Cohen's K = 0.11–0.37, observer agreement: 40.2–66.3%); again, the least agreement was between the orthodontists and others.
Conclusions: The ICA Index resulted in poor to moderate intra- and interexaminer agreement. The validity and reproducibility of the ICA indexing as an objective tool in rating implant aesthetics is questionable.     

基于独立分量分析算法的脑电伪迹分离研究

简要介绍了独立分量分析（ICA）的基本思想及算法．ICA是盲信源分离理论发展的新技术．本研究以Fast ICA为例．将其应用在基于临床多导脑电的伪迹分析与分离研究。实验结果表明：ICA可以将脑电信号中包含的眼电、工频干扰等多种干扰信号成功地分离出来．较好地完成了脑电伪迹消除处理工作。     

A method for multitask fMRI data fusion applied to schizophrenia

It is becoming common to collect data from multiple functional magnetic resonance imaging (fMRI) paradigms on a single individual. The data from these experiments are typically analyzed separately and sometimes directly subtracted from one another on a voxel-by-voxel basis. These comparative approaches, although useful, do not directly attempt to examine potential commonalities between tasks and between voxels. To remedy this we propose a method to extract maximally spatially independent maps for each task that are "coupled" together by a shared loading parameter. We first compute an activation map for each task and each individual as "features," which are then used to perform joint independent component analysis (jICA) on the group data. We demonstrate our approach on a data set derived from healthy controls and schizophrenia patients, each of which carried out an auditory oddball task and a Sternberg working memory task. Our analysis approach revealed two interesting findings in the data that were missed with traditional analyses. First, consistent with our hypotheses, schizophrenia patients demonstrate "decreased" connectivity in a joint network including portions of regions implicated in two prevalent models of schizophrenia. A second finding is that for the voxels identified by the jICA analysis, the correlation between the two tasks was significantly higher in patients than in controls. This finding suggests that schizophrenia patients activate "more similarly" for both tasks than do controls. A possible synthesis of both findings is that patients are activating less, but also activating with a less-unique set of regions for these very different tasks. Both of the findings described support the claim that examination of joint activation across multiple tasks can enable new questions to be posed about fMRI data. Our approach can also be applied to data using more than two tasks. It thus provides a way to integrate and probe brain networks using a variety of tasks and may increase our understanding of coordinated brain networks and the impact of pathology upon them.