Immune parameter analysis of children with sickle cell disease on hydroxycarbamide or chronic transfusion therapy

Sickle cell disease (SCD) is increasingly appreciated as an inflammatory condition associated with alterations in immune phenotype and function. In this cross‐sectional study we performed a multiparameter analysis of 18 immune markers in 114 paediatric SCD patients divided by treatment group [those receiving hydroxycrabamide (HC, previously termed hydroxyurea), chronic transfusion (CT), or no disease‐modifying therapy] and 29 age‐matched African American healthy controls. We found global elevation of most immune cell counts in SCD patients receiving no disease‐modifying therapy at steady state. Despite the decrease in percentage of haemoglobin S associated with CT therapy, the abnormal cellular immune phenotype persisted in patients on CT. In contrast, in both univariate and multivariate analysis, treatment with HC was associated with normalization of the vast majority of leucocyte populations. This study provides additional support for HC treatment in SCD, as it appears that HC decreases the abnormally elevated immune cell counts in patients with SCD.     

CpG岛甲基化表型肺癌的分型标记及临床病理特征

目的 比较新的CpG岛甲基化表型(CIMP)筛选标记基因和经典CIMP筛选标记基因在CIMP肺癌筛选中的作用,并分析CIMP肺癌的临床病理特征.方法 取第二军医大学长海医院呼吸科50例肺癌患者的肺癌组织和癌旁组织,提取DNA,进行甲基化转换后,利用甲基化特异性PCR (MSP)对新的CIMP筛选标记基因(SHISA3、CTSL1、C1ORF103和TMEM200B)和经典的CIMP筛选标记基因(CACNA1G、IGF2、NEUROG1、RUNX3)的启动子CpG岛区域进行扩增,采用琼脂糖凝胶电泳分析其甲基化状态.运用SPSS统计软件对结果进行统计分析.结果 肺癌组织发生明显的甲基化,所研究的8个基因甲基化水平均明显高于癌旁组织(P=0.014).其中RUNX3甲基化与淋巴结转移及功能状态(PS)评分有关(P值分别为0.017、0.018).年龄＞60岁的肺癌患者甲基化率高于≤60岁者(P=0.031).吸烟对CTSL1基因甲基化的影响也很大(P=0.018).结论 CpG岛甲基化表型肺癌具有独特的临床病理特征;新的和经典的甲基化基因组合在CIMP肺癌筛选上都具有较高的灵敏度和特异性.     

Building bridges across electronic health record systems through inferred phenotypic topics

ObjectiveData in electronic health records (EHRs) is being increasingly leveraged for secondary uses, ranging from biomedical association studies to comparative effectiveness. To perform studies at scale and transfer knowledge from one institution to another in a meaningful way, we need to harmonize the phenotypes in such systems. Traditionally, this has been accomplished through expert specification of phenotypes via standardized terminologies, such as billing codes. However, this approach may be biased by the experience and expectations of the experts, as well as the vocabulary used to describe such patients. The goal of this work is to develop a data-driven strategy to (1) infer phenotypic topics within patient populations and (2) assess the degree to which such topics facilitate a mapping across populations in disparate healthcare systems.MethodsWe adapt a generative topic modeling strategy, based on latent Dirichlet allocation, to infer phenotypic topics. We utilize a variance analysis to assess the projection of a patient population from one healthcare system onto the topics learned from another system. The consistency of learned phenotypic topics was evaluated using (1) the similarity of topics, (2) the stability of a patient population across topics, and (3) the transferability of a topic across sites. We evaluated our approaches using four months of inpatient data from two geographically distinct healthcare systems: (1) Northwestern Memorial Hospital (NMH) and (2) Vanderbilt University Medical Center (VUMC).ResultsThe method learned 25 phenotypic topics from each healthcare system. The average cosine similarity between matched topics across the two sites was 0.39, a remarkably high value given the very high dimensionality of the feature space. The average stability of VUMC and NMH patients across the topics of two sites was 0.988 and 0.812, respectively, as measured by the Pearson correlation coefficient. Also the VUMC and NMH topics have smaller variance of characterizing patient population of two sites than standard clinical terminologies (e.g., ICD9), suggesting they may be more reliably transferred across hospital systems.ConclusionsPhenotypic topics learned from EHR data can be more stable and transferable than billing codes for characterizing the general status of a patient population. This suggests that EHR-based research may be able to leverage such phenotypic topics as variables when pooling patient populations in predictive models.     

New quantitative aPTT waveform analysis and its application in laboratory management of haemophilia A patients

Diagnosis of haemophilia A is usually made by the measurement of factor VIII (FVIII) activity that allows categorization of the disease severity. However, tests that assess global haemostasis may better reflect clinical features and give additional clinically relevant information. The aim of this study was to develop a new quantitative activated partial thromboplastin time (aPTT) waveform analysis and compare it with FVIII activities to find out whether waveform parameters are superior determinants of clinical phenotype. A total of 81 haemophilia A patients divided into two groups (37 severe, 44 non‐severe) were included in the study. The control group comprised 101 healthy male volunteers. Quantitative aPTT waveform analysis was performed with Actin FS on BCS (Siemens Healthcare Diagnostics, Marburg, Germany) using three parameters (DELTA, RATIO‐1, RATIO‐2) obtained from a single aPTT measurement with two evaluation modes. FVIII activities were measured by one‐stage clotting and two‐stage chromogenic assay. Statistically significant difference (P < 0.001) between control group and all haemophilia A patients, as well as between severe and non‐severe haemophilia A patients was obtained for all quantitative waveform parameters. Our study revealed parameter DELTA as the best waveform parameter, showing significant correlation with FVIII activities and clinical parameters, and excellent performance for distinguishing between severe and non‐severe haemophilia A patients (ROC analysis: sensitivity 97.3%, specificity 93.2%). The results obtained by new quantitative aPTT waveform analysis were superior to those obtained by standard laboratory methods. The simplicity and cost‐benefit of the method make this approach a reasonable and promising tool for assessing coagulation in haemophilia A patients.