吸水链霉菌ATCC 29253产Hygrocin A发酵条件的优化

【背景】Hygrocins是一种萘安莎抗生素,具有良好的新药开发潜能。但在常见培养基及发酵条件下菌体内Hygrocin A含量一般很低,甚至难以直接进行准确检测。【目的】提高吸水链霉菌ATCC 29253发酵物中Hygrocin A的产量。【方法】采用单因素与正交试验设计优化相结合的方法系统考察碳源、氮源、磷酸盐、MgCl_2浓度、NaCl浓度、种子菌龄等因素对吸水链霉菌ATCC 29253产Hygrocin A能力的影响。【结果】最佳发酵条件为(g/L):葡萄糖4.0,黄豆饼粉8.0,麦芽提取物10.0,K_2HPO_4 1.5,KH_2PO_4 1.5,NaCl 1.5,Mg Cl2 1.0;种子最佳活化时间为48 h;培养参数:摇床转速200 r/min,初始pH为6.8-7.0,瓶装量50 m L/250 m L,接种量5%,30°C培养10 d。在优化条件下,Hygrocin A产量与其原始培养基M10相比提高了500%,Rapamycin产量同时下降了95%。【结论】通过培养基优化,可显著提高吸水链霉菌ATCC 29253中Hygrocin A产量,为Hygrocin A合成应用研究奠定基础,同时可使Rapamycin产量明显下降。这说明可通过选择培养条件有目的地调节两种抗生素的代谢通量,进而开展多种抗生素同时表达的代谢调控研究。     

苯扎溴铵与艾叶水提物的协同杀菌效果

采用悬液定量杀菌试验,对苯扎溴铵与艾叶水提物的协同杀菌效果进行了实验室研究。研究发现,0.1%苯扎溴铵与艾叶水提物混合溶液对金黄色葡萄球菌、大肠埃希菌、枯草芽胞杆菌、白假丝酵母菌和黑曲霉均呈现出显著的协同杀菌作用。结果表明,苯扎溴铵与艾叶水提物具有良好的协同杀菌效果。     

女性生殖道炎症支原体多重耐药性分析

了解本地区在采用常规抗生素治疗支原体后多重耐药株的产生情况,为临床针对性用药提供科学依据。采用培养法对女性生殖道炎患者分泌物进行支原体培养、体外药敏试验及分析。结果可见,感染数量≥10~4 cfu/mL的支原体阳性率为52.9%(82/155);13种抗生素中,耐药种数构成比为6R>2R、4R、5R>3R>1R>8R>7R、10R>0R>9R、12R、13R;耐药率为林可霉素>壮观霉素>环丙沙星>诺氟沙星>司帕沙星>罗红霉素>氧氟沙星>克拉霉素>左氧氟沙星>阿齐霉素>强力霉素,美满霉素>交沙霉素。体外药敏试验对临床治疗支原体感染及减少多重耐药株的产生具有重要指导意义。     

On the use of the variogram in checking for independence in spatial data

The variogram is a standard tool in the analysis of spatial data, and its shape provides useful information on the form of spatial correlation that may be present. However, it is also useful to be able to assess the evidence for the presence of any spatial correlation. A method of doing this, based on an assessment of whether the true function underlying the variogram is constant, is proposed. Nonparametric smoothing of the squared differences of the observed variables, on a suitably transformed scale, is used to estimate variogram shape. A statistic based on a ratio of quadratic forms is proposed and the test is constructed by investigating the distributional properties of this statistic under the assumption of an independent Gaussian process. The power of the test is investigated. Reference bands are proposed as a graphical follow-up. An example is discussed.     

Modeling heterogeneity in nest survival data

Current statistical methods for estimating nest survival rates assume that nests are identical in their propensity to succeed. However, there are several biological reasons to question this assumption. For example, experience of the nest builder, number of nest helpers, genetic fitness of individuals, and site effects may contribute to an inherent disparity between nests with respect to their daily mortality rates. Ignoring such heterogeneity can lead to incorrect survival estimates. Our results show that constant survival models can seriously underestimate overall survival in the presence of heterogeneity. This paper presents a flexible random-effects approach to model heterogeneous nest survival data. We illustrate our methods through data on redwing blackbirds.     

Power and sample size for testing homogeneity of relative risks in prospective studies

Power and sample-size formulas for testing the homogeneity of relative risks using the score method are presented. The homogeneity score test (Gart, 1985, Biometrika 72, 673-677) is formally equivalent to the Pearson chi-square test, although they look different. Results of this paper may be useful in assessing the validity of the model of a common relative risk before combining several 2 x 2 tables or in designing a prospective study for detecting heterogeneity of relative risks.     

Reduced sensitivity to hidden bias at upper quantiles in observational studies with dilated treatment effects

When a treatment has a dilated effect, with larger effects when responses are higher, there can be much less sensitivity to bias at upper quantiles than at lower quantiles; i.e., small, plausible hidden biases might explain the ostensible effect of the treatment for many subjects, and yet only quite large hidden biases could explain the effect on a few subjects having dramatically elevated responses. An example concerning kidney function of cadmium workers is discussed in detail. In that example, the treatment effect is far from additive: It is plausibly zero at the lower quartile of responses to control, and it is large and fairly insensitive to bias at the upper quartile.     

Statistical inference for self-designing clinical trials with a one-sided hypothesis

In the process of monitoring clinical trials, it seems appealing to use the interim findings to determine whether the sample size originally planned will provide adequate power when the alternative hypothesis is true, and to adjust the sample size if necessary. In the present paper, we propose a flexible sequential monitoring method following the work of Fisher (1998), in which the maximum sample size does not have to be specified in advance. The final test statistic is constructed based on a weighted average of the sequentially collected data, where the weight function at each stage is determined by the observed data prior to that stage. Such a weight function is used to maintain the integrity of the variance of the final test statistic so that the overall type I error rate is preserved. Moreover, the weight function plays an implicit role in termination of a trial when a treatment difference exists. Finally, the design allows the trial to be stopped early when the efficacy result is sufficiently negative. Simulation studies confirm the performance of the method.     

Valid inference in random effects meta-analysis

The standard approach to inference for random effects meta-analysis relies on approximating the null distribution of a test statistic by a standard normal distribution. This approximation is asymptotic on k, the number of studies, and can be substantially in error in medical meta-analyses, which often have only a few studies. This paper proposes permutation and ad hoc methods for testing with the random effects model. Under the group permutation method, we randomly switch the treatment and control group labels in each trial. This idea is similar to using a permutation distribution for a community intervention trial where communities are randomized in pairs. The permutation method theoretically controls the type I error rate for typical meta-analyses scenarios. We also suggest two ad hoc procedures. Our first suggestion is to use a t-reference distribution with k-1 degrees of freedom rather than a standard normal distribution for the usual random effects test statistic. We also investigate the use of a simple t-statistic on the reported treatment effects.