Male Inmate Profiles and Their Biological Correlates

Objective:

Borderline and antisocial personality disorders (PDs) share common clinical features (impulsivity, aggressiveness, substance use disorders [SUDs], and suicidal behaviours) that are greatly overrepresented in prison populations. These disorders have been associated biologically with testosterone and cortisol levels. However, the associations are ambiguous and the subject of controversy, perhaps because these heterogeneous disorders have been addressed as unitary constructs. A consideration of profiles of people, rather than of exclusive diagnoses, might yield clearer relationships.

Methods:

In our study, multiple correspondence analysis and cluster analysis were employed to identify subgroups among 545 newly convicted inmates. The groups were then compared in terms of clinical features and biological markers, including levels of cortisol, testosterone, estradiol, progesterone, and sulfoconjugated dehydroepiandrosterone (DHEA-S).

Results:

Four clusters with differing psychiatric, criminal, and biological profiles emerged. Clinically, one group had intermediate scores for each of the tested clinical features. Another group comprised people with little comorbidity. Two others displayed severe impulsivity, PD, and SUD. Biologically, cortisol levels were lowest in the last 2 groups and highest in the group with less comorbidity. In keeping with previous findings reported in the literature, testosterone was higher in a younger population with severe psychiatric symptoms. However, some apparently comparable behavioural outcomes were found to be related to distinct biological profiles. No differences were observed for estradiol, progesterone, or DHEA-S levels.

Conclusions:

The results not only confirm the importance of biological markers in the study of personality features but also demonstrate the need to consider the role of comorbidities and steroid coregulation.     

Unsupervised classification of major depression using functional connectivity MRI

The current diagnosis of psychiatric disorders including major depressive disorder based largely on self‐reported symptoms and clinical signs may be prone to patients' behaviors and psychiatrists' bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting‐state functional magnetic resonance imaging scans in the absence of clinical information. Twenty‐four medication‐naive patients with major depression and 29 demographically similar healthy individuals underwent resting‐state functional magnetic resonance imaging. We first clustered the voxels within the perigenual cingulate cortex into two subregions, a subgenual region and a pregenual region, according to their distinct resting‐state functional connectivity patterns and showed that a maximum margin clustering‐based unsupervised machine learning approach extracted sufficient information from the subgenual cingulate functional connectivity map to differentiate depressed patients from healthy controls with a group‐level clustering consistency of 92.5% and an individual‐level classification consistency of 92.5%. It was also revealed that the subgenual cingulate functional connectivity network with the highest discriminative power primarily included the ventrolateral and ventromedial prefrontal cortex, superior temporal gyri and limbic areas, indicating that these connections may play critical roles in the pathophysiology of major depression. The current study suggests that subgenual cingulate functional connectivity network signatures may provide promising objective biomarkers for the diagnosis of major depression and that maximum margin clustering‐based unsupervised machine learning approaches may have the potential to inform clinical practice and aid in research on psychiatric disorders. Hum Brain Mapp 35:1630–1641, 2014. © 2013 Wiley Periodicals, Inc.     

国际宫颈癌放疗领域研究热点探析

放疗是一种重要的宫颈癌治疗方式,对宫颈癌放疗进行研究对改善宫颈癌患者的预后具有重要的意义。主要对PubMed收录的近10年宫颈癌放疗领域文献的高频主要主题词/副主题词进行共现聚类分析,探究该领域的研究热点,并绘制战略坐标图,展示研究热点的发展现状和趋势,为该领域的研究人员了解宫颈癌放疗的现状及开展相关研究提供参考。     

基于双聚类方法的乳腺癌相关酶研究前沿

以2009-2011年和2012-2014年PubMed收录的乳腺癌相关酶研究的文献为数据源,利用TDA进行数据清洗、实体抽取和构建共词矩阵 ;利用gCLUTO进行双聚类,形成类团,识别研究前沿热点;利用相关算法识别出类团的演化关系。     

Evaluation of a Two‐Stage Approach in Trans‐Ethnic Meta‐Analysis in Genome‐Wide Association Studies

Meta‐analysis of genome‐wide association studies (GWAS) has achieved great success in detecting loci underlying human diseases. Incorporating GWAS results from diverse ethnic populations for meta‐analysis, however, remains challenging because of the possible heterogeneity across studies. Conventional fixed‐effects (FE) or random‐effects (RE) methods may not be most suitable to aggregate multiethnic GWAS results because of violation of the homogeneous effect assumption across studies (FE) or low power to detect signals (RE). Three recently proposed methods, modified RE (RE‐HE) model, binary‐effects (BE) model and a Bayesian approach (Meta‐analysis of Transethnic Association [MANTRA]), show increased power over FE and RE methods while incorporating heterogeneity of effects when meta‐analyzing trans‐ethnic GWAS results. We propose a two‐stage approach to account for heterogeneity in trans‐ethnic meta‐analysis in which we clustered studies with cohort‐specific ancestry information prior to meta‐analysis. We compare this to a no‐prior‐clustering (crude) approach, evaluating type I error and power of these two strategies, in an extensive simulation study to investigate whether the two‐stage approach offers any improvements over the crude approach. We find that the two‐stage approach and the crude approach for all five methods (FE, RE, RE‐HE, BE, MANTRA) provide well‐controlled type I error. However, the two‐stage approach shows increased power for BE and RE‐HE, and similar power for MANTRA and FE compared to their corresponding crude approach, especially when there is heterogeneity across the multiethnic GWAS results. These results suggest that prior clustering in the two‐stage approach can be an effective and efficient intermediate step in meta‐analysis to account for the multiethnic heterogeneity.     

Design and analysis of trials with a partially nested design and a binary outcome measure

Where treatments are administered to groups of patients or delivered by therapists, outcomes for patients in the same group or treated by the same therapist may be more similar, leading to clustering. Trials of such treatments should take account of this effect. Where such a treatment is compared with an un‐clustered treatment, the trial has a partially nested design. This paper compares statistical methods for this design where the outcome is binary. Investigation of consistency reveals that a random coefficient model with a random effect for group or therapist is not consistent with other methods for a null treatment effect, and so this model is not recommended for this design. Small sample performance of a cluster‐adjusted test of proportions, a summary measures test and logistic generalised estimating equations and random intercept models are investigated through simulation. The expected treatment effect is biased for the logistic models. Empirical test size of two‐sided tests is raised only slightly, but there are substantial biases for one‐sided tests. Three formulae are proposed for calculating sample size and power based on (i) the difference of proportions, (ii) the log‐odds ratio or (iii) the arc‐sine transformation of proportions. Calculated power from these formulae is compared with empirical power from a simulations study. Logistic models appeared to perform better than those based on proportions with the likelihood ratio test performing best in the range of scenarios considered. For these analyses, the log‐odds ratio method of calculation of power gave an approximate lower limit for empirical power. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.     

基于数据挖掘方法的椎动脉型颈椎病用药规律研究

目的:运用中医传承辅助平台分析椎动脉型颈椎病中药处方的用药规律。方法:通过中国知网、重庆维普、万方数据库中收集2010年1月—2014年12月治疗椎动脉型颈椎病的中药处方,采用关联规则算法、复杂系统熵聚类等无监督数据挖掘方法,分析处方中各种药物的使用频次、性味归经、药物之间的关联规则及核心药物组合。结果:对筛选出的118个中药处方进行分析,确定其常用药物频次,发现常用药物关联规则共57个,核心组合30个。结论:椎动脉型颈椎病常用的药物以活血化瘀药、温经通络药和祛风止眩药为主,四气分布以温、平、凉为主,五味分布以甘、辛、苦为主,主要归于肝、脾、心经,使用频次较高的药味为川芎、葛根、当归、天麻、丹参等。     

UPLC结合化学计量学方法的白及指纹图谱分析

目的:建立不同产地白及药材UPLC指纹图谱,为建立快速而高效的白及药材整体质量控制方法提供实验依据。方法:采用UPLC-PDA检测法,ACQUITY UPLC BEH C_(18)色谱柱(2.1 mm×150 mm,1.7μm),流动相乙腈-水溶液梯度洗脱,柱温45℃,检测波长280 nm,流速0.3 m L·min~(-1),建立白及药材指纹图谱;通过相似度评价,结合聚类分析和主成分分析,对不同批次的白及药材进行质量评价。结果:建立的白及药材UPLC指纹图谱方法确定了20个共有峰,并对9个共有峰进行了明确化学成分指认;43批白及药材的相似度在0.540~0.942;通过聚类分析可大致聚成2类;PCA大致聚成4类,并发现指纹图谱中6个造成43批次白及药材差异性的化合物。结论:白及的UPLC指纹图谱的构建和化学模式的识别为药材质量控制提供更全面的参考。     

基于中医传承辅助系统分析钟孟良教授治疗痹证的用药经验

目的:基于中医传承辅助系统(V2.5)软件,分析钟孟良教授治疗痹证的用药经验。方法:收集钟孟良教授治疗痹证的病案,采用关联规则Apriori算法、复杂系统熵聚类及无监督的熵层次聚类等数据挖掘方法,结合专家校审,共同分析所选处方中药物的使用频次、配伍及组方规律等。结果:对筛选出的138个处方进行分析,得出钟教授治疗痹证的常用药物有黄芪、附子、木瓜等20种,得到核心组合22个和治疗痹证的新处方11个。结论:钟孟良教授对于痹证的治疗有着丰富的经验,多用益气温阳,祛风除湿,通络止痛之品随症加减,在临床均取得很好的疗效。