Are risk factors necessary for pretest probability assessment of coronary artery disease? A patient similarity network analysis of the PROMISE trial

BackgroundPretest probability (PTP) calculators utilize epidemiological-level findings to provide patient-level risk assessment of obstructive coronary artery disease (CAD). However, their limited accuracies question whether dissimilarities in risk factors necessarily result in differences in CAD. Using patient similarity network (PSN) analyses, we wished to assess the accuracy of risk factors and imaging markers to identify ≥50% luminal narrowing on coronary CT angiography (CCTA) in stable chest-pain patients.MethodsWe created four PSNs representing: patient characteristics, risk factors, non-coronary imaging markers and calcium score. We used spectral clustering to group individuals with similar risk profiles. We compared PSNs to a contemporary PTP score incorporating calcium score and risk factors to identify ≥50% luminal narrowing on CCTA in the CT-arm of the PROMISE trial. We also conducted subanalyses in different age and sex groups.ResultsIn 3556 individuals, the calcium score PSN significantly outperformed patient characteristic, risk factor, and non-coronary imaging marker PSNs (AUC: 0.81 vs. 0.57, 0.55, 0.54; respectively, p ?< ?0.001 for all). The calcium score PSN significantly outperformed the contemporary PTP score (AUC: 0.81 vs. 0.78, p ?< ?0.001), and using 0, 1–100 and ?> ?100 cut-offs provided comparable results (AUC: 0.81 vs. 0.81, p ?= ?0.06). Similar results were found in all subanalyses.ConclusionCalcium score on its own provides better individualized obstructive CAD prediction than contemporary PTP scores incorporating calcium score and risk factors. Risk factors may not be able to improve the diagnostic accuracy of calcium score to predict ≥50% luminal narrowing on CCTA.     

Decreased centrality of cortical volume covariance networks in autism spectrum disorders

Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions characterized by atypical structural and functional brain connectivity. Complex network analysis has been mainly used to describe altered network-level organization for functional systems and white matter tracts in ASD. However, atypical functional and structural connectivity are likely to be also linked to abnormal development of the correlated structure of cortical gray matter. Such covariations of gray matter are particularly well suited to the investigation of the complex cortical pathology of ASD, which is not confined to isolated brain regions but instead acts at the systems level. In this study, we examined network centrality properties of gray matter networks in adults with ASD (n = 84) and neurotypical controls (n = 84) using graph theoretical analysis. We derived a structural covariance network for each group using interregional correlation matrices of cortical volumes extracted from a surface-based parcellation scheme containing 68 cortical regions. Differences between groups in closeness network centrality measures were evaluated using permutation testing. We identified several brain regions in the medial frontal, parietal and temporo-occipital cortices with reductions in closeness centrality in ASD compared to controls. We also found an association between an increased number of autistic traits and reduced centrality of visual nodes in neurotypicals. Our study shows that ASD are accompanied by atypical organization of structural covariance networks by means of a decreased centrality of regions relevant for social and sensorimotor processing. These findings provide further evidence for the altered network-level connectivity model of ASD.     

Constraint based temporal event sequence mining for Glioblastoma survival prediction

ObjectiveA significant challenge in treating rare forms of cancer such as Glioblastoma (GBM) is to find optimal personalized treatment plans for patients. The goals of our study is to predict which patients survive longer than the median survival time for GBM based on clinical and genomic factors, and to assess the predictive power of treatment patterns.MethodWe developed a predictive model based on the clinical and genomic data from approximately 300 newly diagnosed GBM patients for a period of 2 years. We proposed sequential mining algorithms with novel clinical constraints, namely, ‘exact-order’ and ‘temporal overlap’ constraints, to extract treatment patterns as features used in predictive modeling. With diverse features from clinical, genomic information and treatment patterns, we applied both logistic regression model and Cox regression to model patient survival outcome.ResultsThe most predictive features influencing the survival period of GBM patients included mRNA expression levels of certain genes, some clinical characteristics such as age, Karnofsky performance score, and therapeutic agents prescribed in treatment patterns. Our models achieved c-statistic of 0.85 for logistic regression and 0.84 for Cox regression.ConclusionsWe demonstrated the importance of diverse sources of features in predicting GBM patient survival outcome. The predictive model presented in this study is a preliminary step in a long-term plan of developing personalized treatment plans for GBM patients that can later be extended to other types of cancers.     

Online scheduling of pick-up and delivery tasks in hospitals

ObjectiveThe aim of this study was to develop an algorithm for scheduling pick-up and delivery tasks in hospitals. The number of jobs and the dynamic nature of the problem, in having jobs arriving over time, makes the use of information technology indispensable. An optimized scheduling for all types of transportation tasks occurring in a hospital accelerates medical procedures, and reduces the patient’s waiting time and costs.MethodsIn the design of the algorithm we use techniques from classical scheduling theory. In addition, due to some special properties and constraints, we model the problem using methods from graph theory. The resulting algorithm combines both approaches in a transparent manner.ConclusionsTo optimize the schedules, we define the average weighted flow time as an objective function that corresponds to a measure for the task throughput. An evaluation of the algorithm at the Natters State Hospital in Austria shows that it has a superior performance than the current scheduling mechanism.     

亲水凝胶骨架材料制备盐酸多奈哌齐缓释片的可行性研究

目的 采用亲水凝胶骨架材料制备盐酸多奈哌齐缓释片,并对其质量进行评价。方法 采用亲水凝胶骨架材料HPMC K100LV和K4M联用通过干法制粒工艺制备盐酸多奈哌齐缓释片,以溶出曲线相似性f2值作为评价指标,通过正交设计进行处方优化,用高效液相色谱法进行含量和杂质检测,通过加速和长期试验考察片剂稳定性。结果 以该方法制备的盐酸多奈哌齐缓释片质量稳定,具有与原研制剂一致的溶出特征。结论 以该方法制备盐酸多奈哌齐缓释片具有可行性。     

替代对照品法在镇静安神类药物HPLC快速检验方法中的应用

目的 采用替代对照品法建立镇静安神类药物氯氮卓、马来酸咪达唑仑、硝西泮、艾司唑仑、奥沙西泮、劳拉西泮和阿普唑仑的高效液相色谱快速检验方法。方法 采用Agilent ZORBAX Eclipse Plus C₈色谱柱(4.6 mm×150 mm,5 μm),流动相：0.01 mol·L^-1磷酸二氢钾溶液(pH 2.5)-(甲醇-乙腈1∶1)(57∶43),流速：1.0 mL·min^-1,柱温：30 ℃。采用相对容量因子和紫外光谱相似度双指标进行定性;采用相对校正因子法进行定量分析。结果 在确定的色谱条件下,氯氮?、咪达唑仑、硝西泮、艾司唑仑、奥沙西泮、劳拉西泮和阿普唑仑完全分离;采用紫外光谱相似度和相对容量因子进行定性,结果准确可靠;采用替代对照品法,计算药物的相对校正因子进行含量测定,能有效减少对照品的使用,加快高效液相色谱分析速度。结论 该方法快速、简便、可靠,适用于快速检验镇静安神类药物。     

Dissolution Curve Comparisons Through the F2 Parameter,a Bayesian Extension of the f2 Statistic

Dissolution (or in vitro release) studies constitute an important aspect of pharmaceutical drug development. One important use of such studies is for justifying a biowaiver for post-approval changes which requires establishing equivalence between the new and old product. We propose a statistically rigorous modeling approach for this purpose based on the estimation of what we refer to as the F₂ parameter, an extension of the commonly used f₂ statistic. A Bayesian test procedure is proposed in relation to a set of composite hypotheses that capture the similarity requirement on the absolute mean differences between test and reference dissolution profiles. Several examples are provided to illustrate the application. Results of our simulation study comparing the performance of f₂ and the proposed method show that our Bayesian approach is comparable to or in many cases superior to the f₂ statistic as a decision rule. Further useful extensions of the method, such as the use of continuous-time dissolution modeling, are considered.     

基于论文相似网络拓扑结构的聚类方法比较

以R语言中的复杂网络处理包igraph为工具,基于语义相似性算法构建论文相似网络,然后采用四种代表性网络聚类算法（随机游走法、标签传播法、最大模块度法、边介数法）对构建出的网络进行聚类分析。最后结合金标准和网络社团划分评价指标D函数比较四种算法的准确性和稳定性,发现随机游走算法最为卓越,同时明确了复杂网络的预处理也是一个影响聚类效果的重要因素。     

The brain network organization during sleep onset after deprivation

ObjectiveAim of the present study is to investigate the alterations of brain networks derived from EEG analysis in pre- and post-sleep onset conditions after 40 h of sleep deprivation (SD) compared to sleep onset after normal sleep in 39 healthy subjects.MethodsFunctional connectivity analysis was made on electroencelographic (EEG) cortical sources of current density and small world (SW) index was evaluated in the EEG frequency bands (delta, theta, alpha, sigma and beta).ResultsComparing pre- vs. post-sleep onset conditions after a night of SD a significant decrease of SW in delta and theta bands in post-sleep onset condition was found together with an increase of SW in sigma band. Comparing pre-sleep onset after sleep SD versus pre-sleep onset after a night of normal sleep a decreased of SW index in beta band in pre-sleep onset in SD compared to pre-sleep onset in normal sleep was evidenced.ConclusionsBrain functional network architecture is influenced by the SD in different ways. Brain networks topology during wake resting state needs to be further explored to reveal SD-related changes in order to prevent possible negative effects of SD on behaviour and brain function during wakefulness.SignificanceThe SW modulations as revealed by the current study could be used as an index of an altered balance between brain integration and segregation processes after SD.