Drift-barrier hypothesis and mutation-rate evolution

Mutation dictates the tempo and mode of evolution, and like all traits, the mutation rate is subject to evolutionary modification. Here, we report refined estimates of the mutation rate for a prokaryote with an exceptionally small genome and for a unicellular eukaryote with a large genome. Combined with prior results, these estimates provide the basis for a potentially unifying explanation for the wide range in mutation rates that exists among organisms. Natural selection appears to reduce the mutation rate of a species to a level that scales negatively with both the effective population size (N_e), which imposes a drift barrier to the evolution of molecular refinements, and the genomic content of coding DNA, which is proportional to the target size for deleterious mutations. As a consequence of an expansion in genome size, some microbial eukaryotes with large N_e appear to have evolved mutation rates that are lower than those known to occur in prokaryotes, but multicellular eukaryotes have experienced elevations in the genome-wide deleterious mutation rate because of substantial reductions in N_e.     

Analysis of multicentre trials with continuous outcomes: when and how should we account for centre effects?

In multicentre trials, randomisation is often carried out using permuted blocks stratified by centre. It has previously been shown that stratification variables used in the randomisation process should be adjusted for in the analysis to obtain correct inference. For continuous outcomes, the two primary methods of accounting for centres are fixed‐effects and random‐effects models. We discuss the differences in interpretation between these two models and the implications that each pose for analysis. We then perform a large simulation study comparing the performance of these analysis methods in a variety of situations. In total, we assessed 378 scenarios. We found that random centre effects performed as well or better than fixed‐effects models in all scenarios. Random centre effects models led to increases in power and precision when the number of patients per centre was small (e.g. 10 patients or less) and, in some scenarios, when there was an imbalance between treatments within centres, either due to the randomisation method or to the distribution of patients across centres. With small samples sizes, random‐effects models maintained nominal coverage rates when a degree‐of‐freedom (DF) correction was used. We assessed the robustness of random‐effects models when assumptions regarding the distribution of the centre effects were incorrect and found this had no impact on results. We conclude that random‐effects models offer many advantages over fixed‐effects models in certain situations and should be used more often in practice. Copyright © 2012 John Wiley & Sons, Ltd.     

鼻唇沟任意皮瓣修复鼻部皮肤肿瘤的临床探讨

目的评价鼻翼与鼻尖部皮肤良恶性肿瘤切除后采取鼻唇沟任意皮瓣修复治疗的临床效果。方法 2008年1月~2009年12月收治47例鼻翼或鼻尖部位皮肤肿瘤患者。男22例,女25例;年龄23~79岁,平均51.2岁,病程1~30年。鼻翼部35例,鼻尖部12例,其中血管瘤6例,基底细胞癌28例,色素痣6例,鳞癌5例,角化棘皮瘤2例。肿瘤大小范围1.0 cm×1.0 cm~3.0 cm×2.5 cm,形状不规则。根据肿瘤不同性质和范围,选择不同术式完整切除并行鼻唇沟任意皮瓣修复,术后7~10d拆线。结果术后患者切口Ⅰ期愈合47例,皮瓣均完全成活。39例获随访1年,切口瘢痕不明显,眼口鼻器官无变形,36例完全满意,3例基本满意。结论鼻尖或鼻翼部位皮肤肿瘤切除后鼻唇沟任意皮瓣修复,操作简便,并发症少,形态美观,瘢痕小,取得满意效果,是治疗的选择方案之一。     

苡仁六妙颗粒治疗慢性盆腔炎(湿热瘀结证)的随机双盲对照临床试验

目的：初步评价苡仁六妙颗粒治疗慢性盆腔炎（湿热瘀结证）的安全性、有效性。方法：分层区组随机、双盲、阳性药平行对照、多中心试验，共入组221例，其中试验组111例，完成99例；对照组110例，完成104例。试验组：苡仁六妙颗粒1袋/次，3次/日，口服。对照组：妇炎康复颗粒1袋/次，3次/日，口服。疗程设定为6周（经期不停药，连续用药）。痊愈病例随访1个月。结论：苡仁六妙颗粒治疗慢性盆腔炎（湿热瘀结证）安全、有效。     

Fixed-effect versus random-effect models for evaluating therapeutic preferences

A preference trial is a special form of cross-over trial where clinical conditions determine when patients change treatment, in a prescribed order. This leads to binary responses with variable lengths. In cross-over trials with normal responses, patient effect may be treated as either fixed or random. However, with binary responses, random- and fixed-effect assumptions may lead to very different conclusions, so that one is no longer an alternative to the other.     

A Bayesian ordinal model for heterogeneity in a multi-centre myocardial infarction clinical trial

A Bayesian methodology is developed to investigate the homogeneity of the treatment effects in a multi-centre clinical trial with an ordinal response. A hierarchical model is formulated for the ordinal response, and the marginal posterior distributions of the covariates, overall treatment and the centre effects are calculated using the Gibbs sampler. The methodology is applied to data arising from a multi-centre clinical trial of therapies for acute myocardial infarction. In this trial, the overall results show that the treatment is effective. However, there appears to be substantial differences in both the baseline risk and treatment effect across centres. Thus, the observed treatment effects may not be generalized to a broader patient population, and exploratory analyses to ascertain reasons for the treatment-by-centre interaction and its possible effect on the study conclusions would be useful.     

Analysis of ulcer data using hierarchical generalized linear models

In multi-centre clinical trials, heterogeneities in individual hospital treatment effects can be modelled as random effects. Estimates of the individual hospital treatment effects and estimate of the mean treatment effect, allowing for the presence of overall hospital differences, are required, together with some measure of their uncertainty. Systematic inferences from the hierarchical-likelihood are now possible, using hierarchical generalized linear models. We show how to construct profile likelihoods for the treatment effects of individual hospitals.     

Broadened ligand responsiveness of androgen receptor mutants obtained by random amino acid substitution of H874 and mutation hot spot T877 in prostate cancer

In a subset of endocrine therapy-resistant prostate cancers, amino acid substitutions H874Y, T877A and T877S, which broaden ligand specificity of the ligand binding domain (LBD) of the androgen receptor (AR), have been detected. To increase our knowledge of the role of amino acid substitutions at these specific positions in prostate cancer, codons 874 and 877 were subjected to random mutagenesis. AR mutants were screened in a yeast readout system for responsiveness to 5 alpha-dihydrotestosterone, progesterone and dehydroepiandrosterone. At position 874, only the histidine to tyrosine substitution could broaden AR ligand specificity. At position 877, 4 ligand specificity broadening substitutions were found: T877A, T877S, T877C and T877G. The latter 2 were not found in prostate cancer. The AR mutants were tested in mammalian (Hep3B) cells for responsiveness to 13 different ligands. All mutants displayed their own ligand specificity spectrum. Importantly, AR(H874Y) and AR(T877A) could be activated by cortisol. According to the 3-dimensional structure of the AR LBD, T877 interacts directly with the 17 beta-hydroxyl group of androgens. All amino acid substitutions identified at position 877 had smaller side chains than the threonine in the wild-type receptor, indicating that increased space in the ligand binding pocket is important in broadened ligand specificity. Because H874 does not interact directly with the ligand, its substitution by a tyrosine is expected to change the ligand binding pocket conformation indirectly. For T877C and T877G substitutions, 2-point mutations are required, and for H874Y, T877A and T877S substitutions, only a 1-point mutation is sufficient. This most likely explains that the latter 3 have been found in prostate cancer.     

红花随机扩增多态性DNA反应体系的建立与优化

目的：考察不同因素对红花随机扩增多态性DNA反应体系的影响，建立并优化反应体系。方法：提取红花基因组DNA，分别设计Taq酶浓度（三水平：0．02、0．04、0．06U／μL）、dNTP浓度（三水平：0．10、0．20、0．30mmol／L）和引物（primer）浓度（三水平；0．16、0．32、0．48pmol／L）的三因素实验，进行PCR扩增。根据PCR产物的琼脂糖凝胶电泳图确定最优的Taq酶浓度、dNTP浓度和引物浓度。进一步没计Mg^2＋浓度（三水平：1．0、1．5、2．0mmol／L）和模板浓度（四水平：1．00、1．25、1．50、1．75mg／L）的两因素实验，进行PCR扩增，确定最佳Mg^2＋浓度和模板浓度。结果：三因素实验中，第17条组合泳道和两因素实验中第6条组合泳道扩增主带稳定、均匀、清晰，条带数日多。结论：确定红花随机扩增多态性DNA反应25μL反应体系中最佳组合为：Taq酶0．04 U／μL、dNTP0．30mmol／L、引物0．32pmol／L、Mg^2＋ 1．5mmol／L、模板浓度1．00mg／L。