Polycythaemia associated with homozygosity for the abnormal haemoglobin Sherwood Forest (β104 (G6) Arg → Thr)

Summary. We describe a 22-year-old Pakistani male with polycythaemia associated with homozygosity for a highaffinity haemoglobin mutant, Hb Sherwood Forest. This haemoglobin variant has an amino acid substitution in the β globin chain at position 104, Arg → Thr. In the two previously reported instances of this haemoglobin mutant the individuals were heterozygotes and were haematologically normal. We show here that the homozygous state for the mutation is associated with a compensatory erythrocytosis resulting from decreased delivery of oxygen to the tissues. A family study showed that both parents and two siblings are heterozygotes for the haemoglobin mutant and are haematologically normal. To our knowledge, this represents the first example of a β-globin mutation producing polycythaemia in homozygotes but not in heterozygotes.     

基于随机森林的中药寒、热药性代谢组学判别方法研究

目的:建立中药寒、热药性判别模型与方法。方法:利用中药寒、热药动物实验,获取代谢组学数据;再采用随机森林算法构建中药寒、热药性分类判别模型。结果:基于随机森林构建的中药寒、热药性代谢组学分类判别模型,能够很好地实现分类判别,总体准确率超过90%;用前30个最重要的M/Z值构建的分类判别模型,同样有很高的分类准确率;经7∶3测试,准确率也超过90%。结论:基于随机森林的中药寒、热药性代谢组学分类判别模型,经实验数据建模验证表明其可行有效。     

Glycated Albumin Predicts the Risk of Mortality in Type 2 Diabetic Patients on Hemodialysis: Evaluation of a Target Level for Improving Survival

Glycated albumin (GA) is considered a more reliable marker than glycated hemoglobin (HbA1c) for monitoring glycemic control, particularly in diabetic hemodialysis patients. We investigated the associations of GA, HbA1c, and random serum glucose levels with survival, and evaluated possible targets for improving survival in diabetic hemodialysis patients. In this prospective, longitudinal, observational study, we enrolled 90 diabetic hemodialysis patients across six dialysis centers in Japan. The median duration of follow‐up was 36.0 months (mean follow‐up, 29.8 months; range, 3–36 months). There were 11 deaths during the observation period. GA was a significant predictor for mortality (hazard ratio, 1.143 per 1% increase in GA; 95% confidence interval, 1.011–1.292; P = 0.033), whereas HbA1c and random glucose levels were not predictors for mortality. Receiver operating characteristics curve analysis showed that the cutoff value of GA for predicting the risk of mortality was 25%. In the Kaplan–Meier analysis, the cumulative survival rate was significantly greater in patients with GA ≤25% than in patients with GA >25%. GA predicted the risk of all‐cause and cardiovascular mortality in diabetic hemodialysis patients. Our results suggest that GA ≤25% is an appropriate target for improving survival in diabetic hemodialysis patients.     

Partial least squares and logistic regression random-effects estimates for gene selection in supervised classification of gene expression data

Our main interest in supervised classification of gene expression data is to infer whether the expressions can discriminate biological characteristics of samples. With thousands of gene expressions to consider, a gene selection has been advocated to decrease classification by including only the discriminating genes. We propose to make the gene selection based on partial least squares and logistic regression random-effects (RE) estimates before the selected genes are evaluated in classification models. We compare the selection with that based on the two-sample t-statistics, a current practice, and modified t-statistics. The results indicate that gene selection based on logistic regression RE estimates is recommended in a general situation, while the selection based on the PLS estimates is recommended when the number of samples is low. Gene selection based on the modified t-statistics performs well when the genes exhibit moderate-to-high variability with moderate group separation. Respecting the characteristics of the data is a key aspect to consider in gene selection.     

Multilevel models for repeated measures research designs in psychophysiology: an introduction to growth curve modeling

Psychophysiologists often use repeated measures analysis of variance (RMANOVA) and multivariate analysis of variance (MANOVA) to analyze data collected in repeated measures research designs. ANOVA and MANOVA are nomothetic approaches that focus on group means. Newer multilevel modeling techniques are more informative than ANOVA because they characterize both group-level (nomothetic) and individual-level (idiographic) effects, yielding a more complete understanding of the phenomena under study. This article was written as an introduction to growth curve modeling for applied researchers. A growth model is defined that can be used in place of RMANOVAs and MANOVAs for single-group and mixed repeated measures designs. The model is expanded to test and control for the effects of baseline levels of physiological activity on stimulus-specific responses. Practical, conceptual, and statistical advantages of growth curve modeling are discussed.     

Work‐related injuries in the Alaska logging industry, 1991‐2014

Background

Although loggers in Alaska are at high risk for occupational injury, no comprehensive review of such injuries has been performed since the mid‐1990s. We investigated work‐related injuries in the Alaska logging industry during 1991‐2014.

Methods

Using data from the Alaska Trauma Registry and the Alaska Occupational Injury Surveillance System, we described fatal and nonfatal injuries by factors including worker sex and age, timing and geographic location of injuries, and four injury characteristics. Annual injury rates and associated 5‐year simple moving averages were calculated.

Results

We identified an increase in the 5‐year simple moving averages of fatal injury rates beginning around 2005. While injury characteristics were largely consistent between the first 14 and most recent 10 years of the investigation, the size of logging companies declined significantly between these periods.

Conclusions

Factors associated with declines in the size of Alaska logging companies might have contributed to the observed increase in fatal injury rates.

     

孕妇肠道菌群对新生儿出生体质量的影响研究

背景 新生儿出生体质量与个体健康息息相关,低出生体质量是早期新生儿死亡的高危因素,而巨大儿的发生不仅可增加母婴产时并发症发生风险,还可增加个体成年后罹患各种慢性病的风险。因此,寻找新生儿出生体质量的影响因素十分重要。 目的 探讨孕妇肠道菌群对新生儿出生体质量的影响。 方法 以2017年1—9月在广州市妇女儿童医疗中心出生的516例新生儿及其孕母为研究对象,根据新生儿出生体质量将其分为低出生体质量儿组（LW组,n=24）、正常体质量儿组（NW组,n=479）、巨大儿组（OW组,n=13）。采集孕母的肠道菌群参数及临床实验室检测指标,采用QIIME软件进行孕期肠道菌群组成分析和多样性分析;采用LEfSe分析,分别对三组孕妇肠道菌群属水平上的相对丰度进行两两比较,识别组间具有明显差异的菌群;通过线性模型MaAsLin进行多元分析,以捕获各实验室检测指标与微生物属之间的相关性;通过Boruta随机森林分类器模型分别基于实验室检测指标和肠道菌群分类操作单元（OTUs）构建新生儿出生体质量分类预测模型,探究孕妇肠道菌群对新生儿体质量的影响。 结果 三组孕母的肠道菌群组成分析发现,门水平中厚壁菌门（Firmicutes）物种丰富度最高,属水平里普拉梭菌（Faecalibacterium）明显富集,三组间门水平的香农指数和辛普森指数比较,差异无统计学意义（P>0.05）。三组间的LEfSe分析发现：与LW组比较,NW组链球菌（Streptococcus）和罗氏菌（Roseburia）明显富集（P<0.05）,而芽孢杆菌（Bacillaceae）、萝卜属菌（Raphanus）、甲烷球形菌（Methanosphaera）、巴氏杆菌（Barnesiellaceae）、普雷沃氏菌（Paraprevotella）丰度明显降低（P<0.05）;与NW组比较,OW组属巨单胞菌（Megamonas）、属粪球菌（Coprococcus）、韦荣氏菌（Veillonellaceae）、cc-115、梭菌（Closrtidiaceae）、另枝杆菌（Alistipes）明显富集（P<0.05）,而布劳特氏菌（Blautia）和伊格尔兹氏菌（Eggerthella）丰度明显降低（P<0.05）;与LW组比较,OW组Closrtidiaceae、Alistipes菌群明显富集（P<0.05）,而Barnesiellaceae丰度明显降低（P<0.05）。基于实验室检测指标分类器模型、肠道菌群OTUs分类器模型,区分NW组与LW组的受试者工作特征曲线下面积（AUC）分别为0.62、0.77,区分NW组与OW组的AUC分别为0.65、0.78。 结论 不同出生体质量新生儿对应孕母的肠道菌群存在差异,孕母肠道菌群OTUs模型可区分新生儿出生体质量的大小,孕妇肠道菌群可能是预测新生儿体质量的一个良好指标。     

Unravelling animal exposure profiles of human Q fever cases in Queensland,Australia, using natural language processing

Q fever, caused by the zoonotic bacterium Coxiella burnetii, is a globally distributed emerging infectious disease. Livestock are the most important zoonotic transmission sources, yet infection in people without livestock exposure is common. Identifying potential exposure pathways is necessary to design effective interventions and aid outbreak prevention. We used natural language processing and graphical network methods to provide insights into how Q fever notifications are associated with variation in patient occupations or lifestyles. Using an 18‐year time‐series of Q fever notifications in Queensland, Australia, we used topic models to test whether compositions of patient answers to follow‐up exposure questionnaires varied between demographic groups or across geographical areas. To determine heterogeneity in possible zoonotic exposures, we explored patterns of livestock and game animal co‐exposures using Markov Random Fields models. Finally, to identify possible correlates of Q fever case severity, we modelled patient probabilities of being hospitalized as a function of particular exposures. Different demographic groups consistently reported distinct sets of exposure terms and were concentrated in different areas of the state, suggesting the presence of multiple transmission pathways. Macropod exposure was commonly reported among Q fever cases, even when exposure to cattle, sheep or goats was absent. Males, older patients and those that reported macropod exposure were more likely to be hospitalized due to Q fever infection. Our study indicates that follow‐up surveillance combined with text modelling is useful for unravelling exposure pathways in the battle to reduce Q fever incidence and associated morbidity.     

Hippocampus-specific fMRI group activation analysis using the continuous medial representation

We present a new shape-based approach for regional group activation analysis in fMRI studies. The method restricts anatomical normalization, spatial smoothing and random effects statistical analysis to the space inside and around a structure of interest. Normalization involves finding intersubject correspondences between manually outlined masks, and it leverages the continuous medial representation, which makes it possible to extend surface-based shape correspondences to the space inside and outside of structures. Our approach is an alternative to whole-brain normalization in cases where the latter may fail due to anatomical variability or pathology. It also provides an opportunity to analyze the shape and thickness of structures concurrently with functional activation. We apply the technique to the hippocampus and evaluate it using data from a visual scene encoding fMRI study, where activation in the hippocampus is expected. We produce detailed statistical maps of hippocampal activation, as well as maps comparing activation inside and outside of the hippocampus. We find that random effects statistics computed by the new approach are more significant than those produced using the Statistical Parametric Mapping framework (Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.-P., Firth, C.D., Frackowiak, R.S.J. 1994, Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapping, 2(4): 189-210) at low levels of smoothing, suggesting that greater specificity can be achieved by the new method without a severe tradeoff in sensitivity.     

Standardization for Subgroup Analysis in Randomized Controlled Trials

Randomized controlled trials (RCTs) emphasize the average or overall effect of a treatment (ATE) on the primary endpoint. Even though the ATE provides the best summary of treatment efficacy, it is of critical importance to know whether the treatment is similarly efficacious in important, predefined subgroups. This is why the RCTs, in addition to the ATE, also present the results of subgroup analysis for preestablished subgroups. Typically, these are marginal subgroup analysis in the sense that treatment effects are estimated in mutually exclusive subgroups defined by only one baseline characteristic at a time (e.g., men versus women, young versus old). Forest plot is a popular graphical approach for displaying the results of subgroup analysis. These plots were originally used in meta-analysis for displaying the treatment effects from independent studies. Treatment effect estimates of different marginal subgroups are, however, not independent. Correlation between the subgrouping variables should be addressed for proper interpretation of forest plots, especially in large effectiveness trials where one of the goals is to address concerns about the generalizability of findings to various populations. Failure to account for the correlation between the subgrouping variables can result in misleading (confounded) interpretations of subgroup effects. Here we present an approach called standardization, a commonly used technique in epidemiology, that allows for valid comparison of subgroup effects depicted in a forest plot. We present simulations results and a subgroup analysis from parallel-group, placebo-controlled randomized trials of antibiotics for acute otitis media.