Finding Risk Groups by Optimizing Artificial Neural Networks on the Area under the Survival Curve Using Genetic Algorithms

We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural networks (ANN) are trained to either maximize or minimize this area using a genetic algorithm, and combined into an ensemble to predict one of low, intermediate, or high risk groups. Estimated patient risk can influence treatment choices, and is important for study stratification. A common approach is to sort the patients according to a prognostic index and then group them along the quartile limits. The Cox proportional hazards model (Cox) is one example of this approach. Another method of doing risk grouping is recursive partitioning (Rpart), which constructs a decision tree where each branch point maximizes the statistical separation between the groups. ANN, Cox, and Rpart are compared on five publicly available data sets with varying properties. Cross-validation, as well as separate test sets, are used to validate the models. Results on the test sets show comparable performance, except for the smallest data set where Rpart’s predicted risk groups turn out to be inverted, an example of crossing survival curves. Cross-validation shows that all three models exhibit crossing of some survival curves on this small data set but that the ANN model manages the best separation of groups in terms of median survival time before such crossings. The conclusion is that optimizing the area under the survival curve is a viable approach to identify risk groups. Training ANNs to optimize this area combines two key strengths from both prognostic indices and Rpart. First, a desired minimum group size can be specified, as for a prognostic index. Second, the ability to utilize non-linear effects among the covariates, which Rpart is also able to do.     

More evidence for prediction model of radiosensitivity

With the development of precision medicine, searching for potential biomarkers plays a major role in personalized medicine. Therefore, how to predict radiosensitivity to improve radiotherapy is a burning question. The definition of radiosensitivity is complex. Radiosensitive gene/biomarker can be useful for predicting which patients would benefit from radiotherapy. The discovery of radiosensitivity biomarkers require multiple pieces of evidence. A prediction model of breast cancer radiosensitivity based on six genes was established. We had put forward some supplements on the basis of the present study. We found that there were no differences between high- and low-risk scores in the non-radiotherapy group. Patients who received radiotherapy had a significantly better overall survival than non-radiotherapy patients in the predicted low-risk score patients. Furthermore, there was no difference between radiotherapy group and non-radiotherapy group in the high-risk score group. Those results firmly supported the prediction model of radiosensitivity. In addition, building a radiosensitivity prediction model was systematically discussed. Genes of model could be screened by different methods, such as Cox regression analysis, Lasso Cox regression method, random forest algorithm and other methods. In the future, precision radiotherapy might depend on the combination of multi-omics data and high dimensional image data.     

抗生素的研究及其在农业上的应用

介绍了抗生素的研究概况及其作用机制 ,并对其在农业、畜牧兽医上的应用进行了综述。同时简述了其应用过程中可能出现的负面影响 ,并提出了一些可行的解决方法。     

Including network knowledge into Cox regression models for biomarker signature discovery

Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step toward a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Most of these methods focus on classification problems, that is learn a model from data that discriminates patients into distinct clinical groups. Far less has been published on approaches that predict a patient's event risk. In this paper, we investigate eight methods that integrate network information into multivariable Cox proportional hazard models for risk prediction in breast cancer. We compare the prediction performance of our tested algorithms via cross‐validation as well as across different datasets. In addition, we highlight the stability and interpretability of obtained gene signatures. In conclusion, we find GeneRank‐based filtering to be a simple, computationally cheap and highly predictive technique to integrate network information into event time prediction models. Signatures derived via this method are highly reproducible.     

Stem cell-based tissue engineering in veterinary orthopaedics

Regenerative medicine is one of the most intensively researched medical branches, with enormous progress every year. When it comes to translating research from bench to bedside, many of the pioneering innovations are achieved by cooperating teams of human and veterinary medical scientists. The veterinary profession has an important role to play in this new and evolving technology, holding a great scientific potential, because animals serve widely as models for human medicine and results obtained from animals may serve as preclinical results for human medicine. Regenerative veterinary medicine utilizing mesenchymal stromal cells (MSC) for the treatment of acute injuries as well as chronic disorders is gradually turning into clinical routine. As orthopaedic disorders represent a major part of all cases in veterinary clinical practice, it is not surprising that they are currently taking a leading role in MSC therapies. Therefore, the purpose of this paper is to give an overview on past and current achievements as well as future perspectives in stem cell-based tissue engineering in veterinary orthopaedics.     

Analysis of contingency table data on torus mandibularis using a log linear model

The hypothesized sexual difference in the incidence of torus mandibularis in Eskimoid groups, as well as age and group differences, was studied in two Northwest Territories Eskimo groups and in an Aleut group (examined by Moorrees). The data were analyzed using statistical methods new to the field of anthropology. The method analyzes data sampled from a multinomial distribution. A hierarchy of log linear models are fitted to the cell counts of a contingency table. An iterative proportional fitting procedure is used to obtain expected cell counts under each log linear model. The “goodness of fit” of each model is tested by the log likelihood ratio. This statistic can be partitioned into additive components such that differences between models can be tested. In this way a “best” model, from the hierarchy of models, is chosen. Among these three groups of Eskimos, the incidence of torus mandibularis was not affected by sex but was affected by age and was different between the three groups.     

Biosensor Systems for Antibiotic Detection

Antibiotics are widely used in medicine, veterinary medicine, and the food industry. However, the active use of antibacterial drugs leads to environmental pollution. In this regard, there is a great need for monitoring and determining antibiotics in various environments such as drinking water, food, drinks, waste water from pharmaceutical factories, etc. A number of methods, including those based on biosensors, have been developed to determine antibiotics. Biosensor methods of analysis are widely used and are an integral part of environmental monitoring. Electrochemical, optical, acoustic, microbial biosensors, immuno- and aptasensors, as well as sensors based on molecularly imprinted polymers are in the most demand for the analysis of antibiotics. This article provides a brief overview of biosensor methods and approaches for the determination of antibiotics. The most promising biosensor systems for determining antibacterial drugs were analyzed.

     

The use of animal models for stroke research: a review

Stroke has been identified as the second leading cause of death worldwide. Stroke is a focal neurologic deficit caused by a change in cerebral circulation. The use of animal models in recent years has improved our understanding of the physiopathology of this disease. Rats and mice are the most commonly used stroke models, but the demand for larger models, such as rabbits and even nonhuman primates, is increasing so as to better understand the disease and its treatment. Although the basic mechanisms of stroke are nearly identical among mammals, we here discuss the differences between the human encephalon and various animals. In addition, we compare common surgical techniques used to induce animal models of stroke. A more complete anatomic knowledge of the cerebral vessels of various model species is needed to develop more reliable models for objective results that improve knowledge of the pathology of stroke in both human and veterinary medicine.     

Fluoroquinolone resistance of Escherichia coli and Salmonella from healthy livestock and poultry in the EU

The potential for transmission of antibiotic-resistant enteric zoonotic bacteria from animals to humans has been a public health concern for several decades. Bacteria carrying antibiotic resistance genes found in the intestinal tract of food animals can contaminate carcasses and may lead to food-borne disease in humans that may not respond to antibiotic treatment. It is consequently important to monitor changes in antimicrobial susceptibility of zoonotic and commensal organism; in this context, there are a number of veterinary monitoring programmes that collect bacteria in food-producing animals at slaughter and determine their susceptibility against antibiotics relevant for human medicine. The data generated are part of the risk analysis for potential food-borne transmission of resistance. There has been much debate about the use of fluoroquinolones in veterinary medicine, and so, this review will consider the fluoroquinolone data from two surveys and compare them to national surveillance programmes. At the outset, it must be pointed out that there is, however, a lack of agreement between several programmes on what is meant by the term 'fluoroquinolone resistance' through use of different definitions of resistance and different resistance breakpoints. An additional aim of this paper is to clarify some of those definitions. Despite the debate about the contribution of antibiotic use in veterinary medicine to the overall resistance development in human pathogens, the data suggest that clinical resistance to fluoroquinolones in Escherichia coli and nontyphoidal Salmonella is generally uncommon, except for a few countries. Ongoing surveillance will continue to monitor the situation and identify whether this situation changes within the respective animal populations. For the benefit of both the epidemiologist and the clinician, it would be strongly advantageous that national monitoring surveys report both percentages of clinical resistance and decreased susceptibility.     

Determination of ciprofloxacin,enrofloxacin and flumequine in pig plasma samples by capillary isotachophoresis--capillary zone electrophoresis

Quinolones are a group of synthetic antibiotics that are widely used in veterinary medicine. Their residues may remain in tissues, milk, etc. intended for human consumption. The European Union fixes the maximum residue limits (MRLs) of veterinary medicinal products in foodstuffs of animal origin. Analytical methods are therefore needed to determine them in biological samples. In this study, we describe capillary isotachophoresis-capillary zone electrophoresis (ITP-CZE) to analyze three quinolones, enrofloxacin (ENR), ciprofloxacin (CPR) and flumequine (FLU), in pig plasma samples. We used solid-phase extraction with Oasis HLB cartridges as a sample pretreatment clean-up step. Capillary zone electrophoresis (CZE) requires low amounts of sample and is not as sensitive as one would wish. ITP-CZE is an easy way to increase the sample loadability and sensitivity. With this system sensitivity increases 40-fold. The detection limits for CPR, ENR and FLU were 70, 85 and 50 microg l(-1), respectively, which were lower than their MRLs in different kinds of samples. This method is simple and sensitive, and is therefore an alternative tool to the existing HPLC methods for analyzing the residuals of these quinolones in biological samples.     

Sulfonamide drugs: structure,antibacterial property,toxicity, and biophysical interactions

Sulfonamide (or sulphonamide) functional group chemistry (SN) forms the basis of several groups of drug. In vivo sulfonamides exhibit a range of pharmacological activities, such as anti-carbonic anhydrase and anti-t dihydropteroate synthetase allowing them to play a role in treating a diverse range of disease states such as diuresis, hypoglycemia, thyroiditis, inflammation, and glaucoma. Sulfamethazine (SMZ) is a commonly used sulphonamide drug in veterinary medicine that acts as an antibacterial compound to treat livestock diseases such as gastrointestinal and respiratory tract infections. Sulfadiazine (SDZ) is another frequently employed sulphonamide drug that is used in combination with the anti-malarial drug pyrimethamine to treat toxoplasmosis in warm-blooded animals. This study explores the research findings and the work behaviours of SN (SMZ and SDZ) drugs. The areas covered include SN drug structure, SN drug antibacterial activity, SN drug toxicity, and SN environmental toxicity.     

Bringing Conservation Medicine into the Veterinary Curriculum: The Tufts Example

The Center for Conservation Medicine at Tufts University School of Veterinary Medicine (TuftsCCM), has helped to define the concept of conservation medicine as a new science that examines the interaction between human, animal, and environmental health. One the Center’s main objectives in pursuing this new science has been to incorporate conservation medicine and ecosystem health principles into the veterinary curriculum. Environmental influences on disease dynamics in animals has always had a place in veterinary medicine, but often has not been adequately explored. Many opportunities exist within a traditional veterinary curriculum to strengthen this perspective, and to bring depth and new meaning to the understanding of disease and the role of animals in ecosystem health. The Tufts program is designed to reach both the general veterinary student and the student interested in a career in conservation medicine through core teaching, elective opportunities, research opportunities, and extracurricular seminars and workshops. The core curriculum exposes every veterinary student to an ecosystem health perspective of veterinary medicine that helps them realize the impact that this approach can have on their professional lives, regardless of their chosen specialty. Committed conservation medicine students benefit from specialty courses, a wide range of experiential and field research opportunities and active mentoring. Future challenges call for development of more graduate opportunities, continued interdisciplinary collaboration with other educational institutions, and continued curricular integration of this new paradigm of health and disease into veterinary medical education.     

Heterogeneity in IBD allele sharing among covariate-defined subgroups: issues and findings for affected relatives

OBJECTIVES: Modelling of variation in identical-by-descent (IBD) allele sharing using covariates can increase power to detect linkage, identify covariate-defined subgroups linked to particular marker regions, and improve the design of subsequent studies to localize genes and characterize their effects. In this report, we highlight issues that arise in studies of families with affected relatives. METHODS: Mirea et al. [Genet Epidemiol 2003, in press] extended linear and exponential linkage likelihood models [Kong and Cox, Am J Hum Genet 1997;61: 1179-1188] to model variation in NPL scores among covariate-defined groups of families, and proposed likelihood ratio (LR) and t statistics to detect differences in allele sharing between groups defined by a binary covariate. Here we evaluate factors affecting the power of these tests analytically and by example, as well as effects of constraints, nuisance parameters, and incomplete data on test validity by simulation of locus heterogeneity in families with affected siblings or affected cousins. RESULTS: Provided constraints on the parameters are avoided, these tests are particularly useful when one subgroup has less than expected IBD sharing. The distribution of the LR statistic depends on the extent of linkage, particularly in the presence of constraints. The t statistic may be biased by group differences in information content. CONCLUSIONS: We recommend that constraints be applied cautiously, and covariate effects in IBD allele sharing models interpreted with care.     

Advantages of nonlinear mixed models for fitting avian growth curves

Our understanding of avian growth rates can benefit from the use of two statistical approaches that explicitly model the sources of intraspecific variation. First, random effects can evaluate whether there are consistent differences between individuals and groups of siblings within a population, and also account for any lack of statistical independence among data points. Second, nonlinear fixed‐effect functions can be extended to test specific biological hypotheses of interest, such as for differences between groups or populations. We illustrate the advantages of these methods by using nonlinear mixed models to study variation in the growth trajectories of nestling orange‐crowned warblers Oreothylpis celata. Specifically, we quantify the sources of variation within populations, analyze the effects of asynchronous hatching, and test for a difference in the growth rates of populations in Alaska and California, which are at the northern and southern limits of the species’ breeding distribution. We found that growth rates did not consistently vary between nests and individuals within populations and were not affected by asynchronous hatching, but were higher in Alaska than in California. Our extensions of traditional methods allowed us to accurately quantify this difference between populations, which is consistent with life history theory but has rarely been demonstrated in previous comparisons of intraspecific passerine populations. The methods we present can be applied to any taxonomic group and adjusted to fit any nonlinear function, and we provide code and implementation advice to facilitate the use of this analytical framework in future studies.     

Survival model predictive accuracy and ROC curves

The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.     

Sieve Estimation for the Cox Model with Clustered Interval-Censored Failure Time Data

Clustered interval-censored failure time data occur when the failure times of interest are clustered into small groups and known only to lie in certain intervals. A number of methods have been proposed for regression analysis of clustered failure time data, but most of them apply only to clustered right-censored data. In this paper, a sieve estimation procedure is proposed for fitting a Cox frailty model to clustered interval-censored failure time data. In particular, a two-step algorithm for parameter estimation is developed and the asymptotic properties of the resulting sieve maximum likelihood estimators are established. The finite sample properties of the proposed estimators are investigated through a simulation study and the method is illustrated by the data arising from a lymphatic filariasis study.     

Spatial Uncertainty and Ecological Models

Applied ecological models that are used to understand and manage natural systems often rely on spatial data as input. Spatial uncertainty in these data can propagate into model predictions. Uncertainty analysis, sensitivity analysis, error analysis, error budget analysis, spatial decision analysis, and hypothesis testing using neutral models are all techniques designed to explore the relationship between variation in model inputs and variation in model predictions. Although similar methods can be used to answer them, these approaches address different questions. These approaches differ in (a) whether the focus is forward or backward (forward to evaluate the magnitude of variation in model predictions propagated or backward to rank input parameters by their influence); (b) whether the question involves model robustness to large variations in spatial pattern or to small deviations from a reference map; and (c) whether processes that generate input uncertainty (for example, cartographic error) are of interest. In this commentary, we propose a taxonomy of approaches, all of which clarify the relationship between spatial uncertainty and the predictions of ecological models. We describe existing techniques and indicate a few areas where research is needed.     

Research with animals: requirement, responsibility, welfare

Recognition of unacceptable cruelty to animals in pasttimes such as bull-baiting, dates in Britain from the early 19th century. The Society for the Prevention of Cruelty to Animals was founded in 1824. Several bills to curb cruelty were discussed in Parliament, and the Cruel and Improper Treatment of Cattle Act was passed in 1822. Other Acts have followed over the years. Cruelty in the form of painful scientific experiments, including dissection of living, conscious animals, vivisection, was proscribed by the Cruelty to Animals Act 1876. That Act required anyone wishing to experiment with animals to obtain a licence from the Secretary of State. Conditions for issue of licences were strict and remain so to this day. The Act is still valid, and is enforced by the Home Office, with its medical and veterinary Inspectors. The Cruelty to Animals Act 1876 allows experiments on animals under strictly controlled conditions. Experiments must have the clear objective of improving the welfare of man and/or animals. Benefits from experiments carried out under the Act have been enormous, covering every aspect of diagnosis, treatment, and prophylaxis in human and veterinary medicine. Coincidentally, the welfare of laboratory animals has also been greatly improved. There has always been some opposition to the use of animals in biomedical research. The subject is emotive but, by and large, discussion has been rational and within the law. In recent years, however, the morality of using experimental animals has been examined more closely. The possibility of replacing them by alternative methods has been investigated. Where these alternatives are applicable, they are used and further research on them continues. The questioning of animal experiments has emphasized the need to look constantly at animal welfare to ensure humane treatment of all animals, especially those restricted in a laboratory or on a farm. Attention has been drawn in this work to our existing laws protecting animals, but new legislation is being demanded, not only by some lay welfare groups but also by scientists. Hence, it has become very important to discuss various ways of ensuring animal welfare, including by legislation, especially with those knowledgeable in laboratory animal science and animal experiments.(ABSTRACT TRUNCATED AT 400 WORDS)     

CCl4肝损伤与免疫性肝损伤小鼠模型间差异性比较研究

本文对比研究了CCl4肝损伤与免疫性肝损伤小鼠模型肝组织病理切片、生理、生化指标及差异基因表达谱的变化,根据两种肝损伤模型的差异表达基因,初步探讨了它们致肝损伤的机理。结果表明,两组模型小鼠的ALT、AST等多项生化指标与正常小鼠相比存在显著性差异：通过表达谱芯片实验,在CCl4组和免疫组中分别筛选得到379条和293条与正常组差异表达基因;其中有105条基因在两个模型组中均差异表达（表达下调基因58条,表达上调基因47条）。提示CCl4肝损伤与免疫性肝损伤小鼠模型间有相似性,但差异亦较明显。