实验仪器使用技术

To maximize the success of any translational research endeavor, sensitive and reliable behavioral outcome measures in valid animal models are essential. A common goal of preclinical studies in both Parkinson’s disease and stroke is to reduce or reverse sensorimotor impairments ass ...

Animal behaviours that are easy to measure make great test systems for drug development, but we sometimes neglect to try to understand how their four-legged world view translates to our own. In this brief essay, I try to relate the turning behaviour that has been so useful in the development of drugs that a ...

Rotation is one of the most widely used tests in behavioural neuroscience. It is designed to detect motor turning and side biases in animals with lesions of basal ganglia circuits of the brain, and most notably �following unilateral dopamine-depleting 6-OHDA lesions of the nigrostriatal bu ...

Huntington’s disease (HD) is a monogenetic, neurodegenerative disease. It is fatal, and although treatments are available for minor symptomatic relief, it remains incurable. Careful study of models of HD remains critical for elucidation of disease mechanisms and for the development ...

The 6-hydroxydopamine (6-OHDA) lesion of the rat nigrostriatal pathway is the most widely used animal model of Parkinson’s disease. 6-OHDA is a highly specific neurotoxin which targets catecholamine neurones via the dopamine active transporter (DAT). When injected stereotaxical ...

Parkinson’s disease (PD) is a chronic, progressive neurodegenerative movement disorder. To understand the pathomechanisms and to develop new drugs and therapies for PD, it is important to have animal models that recapitulate the slow progression and symptoms of the disease. The genera ...

Peptides scanned from whole protein sequences are the core information for many peptide bioinformatics research subjects, such as functional site prediction, protein structure identification, and protein function recognition. In these applications, we normally need to ass ...

This chapter critically reviews some of the important methods being used for building quantitative structure-activity relationship (QSAR) models using the artificial neural networks (ANNs). It attends predominantly to the use of multilayer ANNs in the regression analysis of str ...

The emergence of drug resistant pathogens can reduce the efficacy of drugs commonly used to treat infectious diseases. Human immunodeficiency virus (HIV) is particularly sensitive to drug selection pressure, rapidly evolving into drug resistant variants on exposure to anti-HIV dr ...

This chapter covers a part of the spectrum of neural-network uses in analytical chemistry. Different architectures of neural networks are described briefly. The chapter focuses on the development of three-layer artificial neural network for modeling the anti-HIV activity of the HETP ...

Artificial neural networks are increasingly used in environmental toxicology to find complex relationships between the ecotoxicity of xenobiotics and their structure or physicochemical properties. The raison d'�tre of these nonlinear tools is their ability to derive powerf ...

The principles of learning strategy of Kohonen and counterpropagation neural networks are introduced. The advantages of unsupervised learning are discussed. The self-organizing maps produced in both methods are suitable for a wide range of applications. Here, we present an example ...

Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a “wel ...

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modern drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships bet ...

Once regarded as an eccentric and unpromising algorithm for the analysis of scientific data, the neural network has been developed in the last decade into a powerful computational tool. Its use now spans all areas of science, from the physical sciences and engineering to the life sciences and all ...

In the past, neural networks were viewed as classification and regression systems whose internal representations were incomprehensible. It is now becoming apparent that algorithms can be designed that extract comprehensible representations from trained neural networks, e ...

Both supervised and unsupervised neural networks have been applied to the prediction of protein structure and function. Here, we focus on feedforward neural networks and describe how these learning machines can be applied to protein prediction. We discuss how to select an appropriate da ...

An associative neural network (ASNN) is an ensemble-based method inspired by the function and structure of neural network correlations in brain. The method operates by simulating the short- and long-term memory of neural networks. The long-term memory is represented by ensemble of neural ...

The field of neural transplantation has rapidly progressed during the past two decades. Since the first published observations on the functional effects of transplanted fetal dopamine (DA) neurons in rodents (Perlow et al., 1979; Bj�rklund and Stenevi, 1979), there are now several ongoing ...

The aim of this chapter is to provide an overview of techniques for protecting central nervous system (CNS) implants from the immune system. It is necessarily more theoretical than methodological. The detailed methodology of the various transplantation techniques themselves are co ...