米诺环素对不完全脊髓损伤大鼠脊髓BDNF和NT-3表达的影响

目的观察腹腔注射米诺环素对改良Allen’s法造成的不完全脊髓损伤大鼠脊髓中脑源性神经营养因子以及神经营养因子3表达的影响,探讨米诺环素治疗脊髓损伤的作用机制。方法成年雌性Sprague-Dawley(SD)大鼠54只,改良Allen’s法造成不完全脊髓损伤,根据实验需要可以分为3组,空白组,只打开脊柱椎板,不损伤;治疗组,大鼠脊髓损伤,并腹腔注射米诺环素;损伤组,大鼠脊髓损伤,腹腔注射等剂量的生理盐水。观察各组大鼠的后肢能力Basso-Beattie-Bresnahan评分,并于不同时段(3d、7d,14d)取大鼠脊髓T8-9段采用逆转录PCR,以及免疫化学组织染色法测定脑源性神经营养因子以及神经营养因子3的表达。结果米诺环素能够明显改善不完全脊髓损伤大鼠的功能,逆转录PCR和脊髓组织冰冻切片免疫组织化学染色DAB都能证实米诺环素治疗组脑源性神经营养因子以及神经营养因子3表达显著增多。结论米诺环素在治疗不完全脊髓损伤大鼠的机制还应与其上调了大鼠体内的脑源性神经营养因子以及神经营养因子3表达有关。     

Binary Responses and the Three-period Cross-over

Problems with carry-over effects in the simple two-period cross-over have lead to interest in more complex cross-over designs. A method for analysing the optimum two-treatment three-period design with binary response variables is given by making a simple extension to Gart's logistic model. The method gives independent tests for, and estimates of the difference in treatment and first-order carry-over effects. An example of the analysis is given, using the loglinear models facility in GLIM.     

Two‐sample nonparametric likelihood inference based on incomplete data with an application to a pneumonia study

The clinical pulmonary infection score (CPIS) and bronchoalveolar lavage (BAL) are important diagnostic variables of pneumonia for forcefully ventilated patients who are susceptible to nosocomial infection. Because of its invasive nature, BAL is performed for patients only if the CPIS is greater than a certain threshold value. Thus, CPIS and BAL are closely related, yet BAL values are substantially missing. In a randomized clinical trial, the control and oral treatment groups were compared based on the outcomes from these procedures. Because of the relevance of both outcomes with respect to evaluating the efficacy of treatments, we propose and examine a nonparametric test based on these outcomes, which employs the empirical likelihood methodology. While efficient parametric methods are available when data are observed incompletely, performing appropriate goodness‐of‐fit tests to justify the parametric assumptions is difficult. Our motivation is to provide an approach based on no particular distributional assumption, which enables us to use all observed bivariate data, whether completed or not in an approximate likelihood manner. A broad Monte Carlo study evaluates the asymptotic properties and efficiency of the proposed method based on various sample sizes and underlying distributions. The proposed technique is applied to a data set from a pneumonia study demonstrating its practical worth.     

Multiple imputation for multivariate data with missing and below-threshold measurements: time-series concentrations of pollutants in the Arctic

Many chemical and environmental data sets are complicated by the existence of fully missing values or censored values known to lie below detection thresholds. For example, week-long samples of airborne particulate matter were obtained at Alert, NWT, Canada, between 1980 and 1991, where some of the concentrations of 24 particulate constituents were coarsened in the sense of being either fully missing or below detection limits. To facilitate scientific analysis, it is appealing to create complete data by filling in missing values so that standard complete-data methods can be applied. We briefly review commonly used strategies for handling missing values and focus on the multiple-imputation approach, which generally leads to valid inferences when faced with missing data. Three statistical models are developed for multiply imputing the missing values of airborne particulate matter. We expect that these models are useful for creating multiple imputations in a variety of incomplete multivariate time series data sets.     

Likelihood-based experimental design for estimation of ED50

In selecting the best dosage choice for the estimation of ED50, it is natural to try to minimize the length of the confidence intervals. In this presentation, the dose allocation that minimizes the length of the likelihood-based confidence intervals is presented and compared with alternative allocations that have been proposed based on the length of different types of confidence intervals, such as those based on the asymptotic variance or on Fieller's Theorem. Effective strategies to deal with the parameter dependence of these allocations are explored. A series of experiments to evaluate the effect of small doses per fraction on the radiation tolerance of the rat cervical spinal cord provide the motivation and an illustration for the proposed procedures.     

High-throughput optimization by statistical designs: example with rat liver slices cryopreservation

The purpose of this study was to optimize cryopreservation conditions of rat liver slices in a high-throughput format, with focus on reproducibility. A statistical design of 32 experiments was performed and intracellular lactate dehydrogenase (LDHi) activity and antipyrine (AP) metabolism were evaluated as biomarkers. At freezing, modified University of Wisconsin solution was better than Williams'E medium, and pure dimethyl sulfoxide was better than a cryoprotectant mixture. The best cryoprotectant concentrations were 10% for LDHi and 20% for AP metabolism. Fetal calf serum could be used at 50 or 80%, and incubation of slices with the cryoprotectant could last 10 or 20 min. At thawing, 42 degrees C was better than 22 degrees C. After thawing, 1h was better than 3h of preculture. Cryopreservation increased the interslice variability of the biomarkers. After cryopreservation, LDHi and AP metabolism levels were up to 84 and 80% of fresh values. However, these high levels were not reproducibly achieved. Two factors involved in the day-to-day variability of LDHi were identified: the incubation time with the cryoprotectant and the preculture time. In conclusion, the statistical design was very efficient to quickly determine optimized conditions by simultaneously measuring the role of numerous factors. The cryopreservation procedure developed appears suitable for qualitative metabolic profiling studies.     

Conditional logistic analysis of case-control studies with complex sampling

Methods for the analysis of unmatched case-control data based on a finite population sampling model are developed. Under this model, and the prospective logistic model for disease probabilities, a likelihood for case-control data that accommodates very general sampling of controls is derived. This likelihood has the form of a weighted conditional logistic likelihood. The flexibility of the methods is illustrated by providing a number of control sampling designs and a general scheme for their analyses. These include frequency matching, counter-matching, case-base, randomized recruitment, and quota sampling. A study of risk factors for childhood asthma illustrates an application of the counter-matching design. Some asymptotic efficiency results are presented and computational methods discussed. Further, it is shown that a 'marginal' likelihood provides a link to unconditional logistic methods. The methods are examined in a simulation study that compares frequency and counter-matching using conditional and unconditional logistic analyses and indicate that the conditional logistic likelihood has superior efficiency. Extensions that accommodate sampling of cases and multistage designs are presented. Finally, we compare the analysis methods presented here to other approaches, compare counter-matching and two-stage designs, and suggest areas for further research.To whom correspondence should be addressed.     

Minimax D-optimal designs for the logistic model

We propose an algorithm for constructing minimax D-optimal designs for the logistic model when only the ranges of the values for both parameters are assumed known. Properties of these designs are studied and compared with optimal Bayesian designs and Sitter's (1992, Biometrics, 48, 1145-1155) minimax D-optimal kk-designs. Examples of minimax D-optimal designs are presented for the logistic and power logistic models, including a dose-response design for rheumatoid arthritis patients.