乙肝后肝硬化症状学聚类研究

目的:探讨乙肝后肝硬化的症状组合规律和证候规律。方法:采用临床流行病学的原则和方法,应用系统聚类的方法对276例乙肝后肝硬化的症状、体征进行分析研究。结果:采用聚类分析研究,得出乙肝后肝硬化基本中医证候为7类:湿热内蕴证、肝肾阴虚证、肝郁脾虚证、脾虚湿盛证、脾肾阳虚证、血瘀证、气(阳)虚证。结论:聚类分析统计方法对症状组合规律、证候规律等方面的研究具有一定意义。     

Clustering in the incidence of myelodysplastic syndromes

The myelodysplastic syndromes (MDS) are a group of malignancies with poor prognosis and obscure etiology. To better understand the distribution of MDS in the population and help generate etiologic hypotheses, we assessed potential clustering in the incidence of MDS in the state of Connecticut using population-based cancer registry data that recently became available. A significant spatial clustering was identified. The most likely area with a high incidence of MDS included 46 census tracts near the west border of Connecticut, and the ratio of observed/expected cases was 2.84. The P value associated with this cluster was 0.0001. Although no temporal clustering was indicated, a space-time analysis identified a cluster in the central south of Connecticut from March 2002 through August 2003 (P = 0.008). This is the first analysis of potential clustering in the incidence of MDS using population-based data. If the intriguing finding on spatial clustering is supported by future studies with larger sample sizes and/or in other geographic areas, it would be extremely interesting to explore the “causes” of clustering, which may help shed light on the etiology of MDS.     

Wavelet filtering before spike detection preserves waveform shape and enhances single-unit discrimination

The isolation of single units in extracellular recordings involves filtering. Removing lower frequencies allows a constant threshold to be applied in order to identify and extract action potential events. However, standard methods such as Butterworth bandpass filtering perform this frequency excision at a cost of grossly distorting waveform shapes. Here, we apply wavelet decomposition and reconstruction as a filter for electrophysiology data and demonstrate its ability to better preserve spike shape. For the majority of cells, this approach also improves spike signal-to-noise ratio (SNR) and increases cluster discrimination. Additionally, the described technique is fast enough to be applied real-time.     

Nonlinear dimensionality reduction of gene expression data for visualization and clustering analysis of cancer tissue samples

Gene expression data are the representation of nonlinear interactions among genes and environmental factors. Computing analysis of these data is expected to gain knowledge of gene functions and disease mechanisms. Clustering is a classical exploratory technique of discovering similar expression patterns and function modules. However, gene expression data are usually of high dimensions and relatively small samples, which results in the main difficulty for the application of clustering algorithms. Principal component analysis (PCA) is usually used to reduce the data dimensions for further clustering analysis. While PCA estimates the similarity between expression profiles based on the Euclidean distance, which cannot reveal the nonlinear connections between genes. This paper uses nonlinear dimensionality reduction (NDR) as a preprocessing strategy for feature selection and visualization, and then applies clustering algorithms to the reduced feature spaces. In order to estimate the effectiveness of NDR for capturing biologically relevant structures, the comparative analysis between NDR and PCA is exploited to five real cancer expression datasets. Results show that NDR can perform better than PCA in visualization and clustering analysis of complex gene expression data.     

Pairwise constrained concept factorization for data representation

Concept factorization (CF) is a variant of non-negative matrix factorization (NMF). In CF, each concept is represented by a linear combination of data points, and each data point is represented by a linear combination of concepts. More specifically, each concept is represented by more than one data point with different weights, and each data point carries various weights called membership to represent their degrees belonging to that concept. However, CF is actually an unsupervised method without making use of prior information of the data. In this paper, we propose a novel semi-supervised concept factorization method, called Pairwise Constrained Concept Factorization (PCCF), which incorporates pairwise constraints into the CF framework. We expect that data points which have pairwise must-link constraints should have the same class label as much as possible, while data points with pairwise cannot-link constraints will have different class labels as much as possible. Due to the incorporation of the pairwise constraints, the learning quality of the CF has been significantly enhanced. Experimental results show the effectiveness of our proposed novel method in comparison to the state-of-the-art algorithms on several real world applications.     

某院重症监护病房聚集性CRE感染的调查及处置

目的调查某院心脏大血管外科重症监护病房(下简称心外ICU)聚集性碳青霉烯类耐药肠杆菌科细菌(CRE)感染事件,为医院感染防控提供依据。方法对南京医科大学第一附属医院心外ICU 2019年6月-7月检出CRE的患者进行流行病学调查,给予控制措施,评价防控效果。结果该病区在2019年6-7月出现5例患者检出CRE共9株,其中4例患者为医院感染,1例患者为CRE定植,CRE医院感染例次率高于4-5月,差异有统计学意义(P<0.05)。9株CRE中8株病原菌为肺炎克雷伯菌(药敏谱一致),1株为大肠埃细菌。CRE来源分析77.78%(7/9)来自于心外ICU本身。共采集标本60份,包括水池区域24份、A床床单元16份、B床床单元8份、C床床单元4份、护理移动掌上电脑(PAD)和标本传输物流系统各4份。共10个位点的标本检测出CRE,其中CRE显色平板培养出9株CRE,麦康凯平板培养出2株CRE。采取综合控制措施结合针对性加强清洁消毒,复查未检测出CRE。控制后的5个月CRE检出及感染数量下降,效果显著。结论CRE显色平板结合麦康凯平板在CRE流行病学调查中可更有效且全面的培养出CRE,通过环境采样明确具体定植位点,并进行针对性干预,可高效去除CRE在环境中的定植。     

Evaluation of Twenty Genes in Prognosis of Patients with Ovarian Cancer Using Four Different Clustering Methods

Background: Comparison of gene expression algorithms may be beneficial for obtaining disease pattern or grouping patients based on the gene expression profile. The current study aimed to investigate whether the knowledge within these data is able to group the ovarian cancer patients with similar disease pattern. Methods: Four different clustering methods were applied on 20 genes expression data of 37 women with ovarian cancer. All selected genes in this study had prominent roles in the control of the activity of the immune system, as well as the chemotaxis, angiogenesis, apoptosis, and etc. Comparison of different clustering methods such as K-means, Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Expectation-Maximization (EM) algorithm was the other aim of the present study. In addition, the percentage of correct prediction, Robustness-Performance Trade-off (RPT), and Silhouette criteria were used to evaluate the performance of clustering methods. Results: Six out of 20 genes (IFN-γ, Foxp3, IL-4, BCL-2, Oct4 and survivin) selected by the Laplacian score showed key roles in the development of ovarian cancer and their prognostic values were clinically and statistically confirmed. The results indicated proper capability of the expression pattern of these genes in grouping the patients with similar prognosis, i.e. patients alive after 5 years or dead (62.12%). Conclusion: The results revealed the better performance for k-means and hierarchical clustering methods, and confirmed the fact that by using the expression profile of these genes, patients with similar behavior can be grouped in the same cluster with acceptable accuracy level. Certainly, the useful information from these data may contribute to the prediction of prognosis in ovarian cancer patients along with other features of patients.     

大田县居民心血管病危险因素聚集及其与发病的关联性研究

目的 了解福建省大田县居民心血管病（CVD）危险因素分布、聚集及其与发病的关系,为制定本地区CVD防控策略和措施提供科学依据。方法 基于国家心血管病高危人群早期筛查与综合干预项目大田县项目点2018年数据,分析CVD常见危险因素（吸烟S、饮酒A、超重Ov、肥胖Ob、高血压H、糖尿病G、血脂异常D）的聚集特征;并通过logistic回归分别分析常见危险因素聚集个数及聚集组合与CVD患病的关系。结果 共纳入13 838名35～75岁常住居民的筛查数据,CVD患病率为1.6%,男性（2.4%）高于女性（1.0%）（P＜0.001）,随年龄增加呈增高趋势（P趋势＜0.001）。检出率前3位的CVD常见危险因素为Ov（38.7%）、H（36.7%）、S（22.2%）。具有至少1个CVD常见危险因素者的占比为77.7%,其中具有1个、2个和≥3个CVD常见危险因素者分别占34.1%、26.8%和16.8%;男性、农民、年龄≥55岁者常见危险因素聚集个数较多。控制人口学特征后,与无常见危险因素者相比,具有2个和≥3个常见危险因素者患CVD的风险增加,OR（95%CI）分别为1.6（1.0～2.6）和2.0（1.2～2.1）; CVD患病风险较高的常见危险因素组合为DHGS、DHS、DG,OR（95%CI）分别为11.7（1.9～71.7）、9.1（2.1～39.7）和7.5（1.5～36.9）。结论 大田县居民CVD危险因素检出率与聚集程度较高,尤其是男性、农民、年龄≥55岁者。多个危险因素聚集使CVD患病风险骤增,尤其是包含D、H的组合。     

The endless quarantine: the impact of the COVID-19 outbreak on healthcare workers after three months of mandatory social isolation in Argentina

ObjectivesAt the end of 2019 the SARS-CoV-2 outbreak spread around the globe with a late arrival to South America. The objective of this study was to evaluate the impact of the long period of mandatory social isolation that took place in Argentina on the general psychological well-being of healthcare workers due to the COVID-19 pandemic.MethodsA survey was conducted during June 2020, in healthcare workers. Pittsburgh Sleep Quality Index, Insomnia Severity Index, Sleepiness-Wakefulness Inability and Fatigue Test, and Goldberg depression and anxiety scale, were used to analyze the effects of the SARS-Cov 2 outbreak after three months of mandatory social isolation. Analyses were performed by logistic regression and a clustering algorithm in order to classify subjects in the function of their outcome's severity.ResultsFrom 1059 surveys, the majority reported symptoms of depression (81.0%), anxiety (76.5%), poor sleep quality (84.7%), and insomnia (73.7%) with 58.9% suffering from nightmares. Logistic regression showed that being in contact with COVID-19 patients, age, gender and the consumption of sleep medication during the mandatory social isolation were relevant predictors for insomnia, anxiety, and depression. Clustering analysis classified healthcare workers in three groups with healthy/mild, moderate, and severe outcomes. The most vulnerable group was composed mainly of younger people, female, non-medical staff, or physicians in training.ConclusionAn extremely high proportion of Argentinian healthcare workers suffered from sleep problems, anxiety, and depression symptoms. The clustering algorithm successfully separates vulnerable from non-vulnerable populations suggesting the need to carry out future studies involving resilience and vulnerability factors.