Applications of carbon nanotubes for cancer research

Carbon nanotubes have many unique properties such as high surface area, hollow cavities, and excellent mechanical and electrical properties. Interfacing carbon nanotubes with biological systems could lead to significant applications in various disease diagnoses. Significant progress in interfacing carbon nanotubes with biological materials has been made in key areas such as aqueous solubility, chemical and biological functionalization for biocompatibility and specificity, and electronic sensing of proteins. In addition, the bioconjugated nanotubes combined with the sensitive nanotube-based electronic devices would enable sensitive biosensors toward medical diagnostics. Furthermore, recent findings of improved cell membrane permeability for carbon nanotubes would also expand medical applications to therapeutics using carbon nanotubes as carriers in gene delivery systems. This article reviews the current trends in biological functionalization of carbon nanotubes and their potential applications for breast cancer diagnostics. The article also reports the applications of confocal microscopy for use in understanding the interactions of biological materials such as antibodies on carbon nanotubes that are specific to surface receptors in breast cancer cells. Furthermore, a nanotube-field-effect transistor is demonstrated for electronic sensing of antibodies that are specific to surface receptors in cancer cells.     

Fundamentally low reproducibility in molecular genetic cancer research

The review discusses the causes of multiple failures in cancer treatment, which might primarily result from the excessive variability of cancer genomes. They are capable of changing their spatial and temporal architecture during tumor development. The key reasons of irreproducibility of biomedical data and the presumable means for improvement of therapeutic results aiming at targeting the most stable tumor traits are suggested.     

Complexities of cancer research: mouse genetic models

Cancer susceptibility is a complex interaction of an individual's genetic composition and environmental exposures. Huge strides have been made in understanding cancer over the past 100 yr, from recognition of cancer as a genetic disease, to identification of specific carcinogens, isolation of oncogenes, and recognition of tumor suppressors. A tremendous amount of knowledge has accumulated about the etiology of cancer. Cancer genetics has played a significant role in these discoveries. Analysis of high-risk familial cancers has led to the discovery of new tumor suppressor genes and important cancer pathways. These families, however, represent only a small fraction of cancer in the general population. Most cancer is instead probably the result of an intricate interaction of polymorphic susceptibility genes with the sea of environmental exposures that humans experience. Although the central cadre of cancer genes is known, little is understood about the peripheral genes that likely comprise the polymorphic susceptibility loci. The challenge for cancer genetics is therefore to move forward from the mendelian genetics of the rare familial cancer syndromes into the field of quantitative trait loci, susceptibility factors, and modifier genes. By identifying the genes that modulate an individual's susceptibility to cancer after an environmental exposure, researchers will be able to gain important insights into human biology, cancer prevention, and cancer treatment. This article summarizes the current state of quantitative trait genetic analysis and the tools, both proven and theoretical, that may be used to unravel one of the great challenges in cancer genetics.     

Adapting genetic regulatory models by genetic programming

In this paper, we focus on the task of adapting genetic regulatory models based on gene expression data from microarrays. Our approach aims at automatic revision of qualitative regulatory models to improve their fit to expression data. We describe a type of regulatory model designed for this purpose, a method for predicting the quality of such models, and a method for adapting the models by means of genetic programming. We also report experimental results highlighting the ability of the methods to infer models on a number of artificial data sets. In closing, we contrast our results with those of alternative methods, after which we give some suggestions for future work.     

Mining parasite data using genetic programming

Genetic programming is a technique that can be used to tackle the hugely demanding data-processing problems encountered in the natural sciences. Application of genetic programming to a problem using parasites as biological tags demonstrates its potential for developing explanatory models using data that are both complex and noisy.     

蜜蜂线粒体DNA在遗传分析中的应用

线粒体DNA是动物细胞核外唯一的遗传物质,具有结构简单、进化速度快、母系遗传和基因重组率小等特点。文章在介绍蜜蜂线粒体DNA的结构大小及其多态性的研究的基础上,对其在蜜蜂的种间多态性、亚种间及亚种内多态性、起源进化、群体遗传结构及基因流动、亚种及类型的分类等方面的研究应用进行阐述。     

Applications of proteomics in hepatic diseases research

Proteomics has become an important part in the leading research area and been widely used in the disease-associated study. In hepatic research field, proteomics could be applied in study of hepatic diseases including liver cancer, cirrhosis and hepatotoxicities, etc. Significant proteins could be identified as biomarkers, drug targets and clues for pathogenesis illumination.     

Template learning of cellular neural network using genetic programming

A new learning algorithm for space invariant Uncoupled Cellular Neural Network is introduced. Learning is formulated as an optimization problem. Genetic Programming has been selected for creating new knowledge because they allow the system to find new rules both near to good ones and far from them, looking for unknown good control actions. According to the lattice Cellular Neural Network architecture, Genetic Programming will be used in deriving the Cloning Template. Exploration of any stable domain is possible by the current approach. Details of the algorithm are discussed and several application results are shown.     

AFLP分子标记技术在昆虫学研究中的应用

AFLP分子标记技术是一种建立在PCR技术和RFLP标记基础上的新的DNA指纹分析技术 ,具有多态性丰富、结果稳定可靠、重复性好、所需DNA量少、可以在不知道基因组序列的情况下进行研究等特点 ,现已广泛用于构建遗传图谱、遗传多样性研究、系统进化及分类学、遗传育种和品质鉴定以及基因定位等方面。该文介绍了AFLP标记技术的原理以及在昆虫学研究中的应用。     

Human subject protections in genetic research

The Certificate of Confidentiality (COC) is a voluntary tool to protect researchers from being compelled to release identifying information about their subjects. Institutional review board (IRB) review and informed consent (IC) procedures are mandatory tools to protect human subjects. Although many studies reveal poor documentation of IRB and IC procurement, most published research undergoes IRB review and has appropriate IC procedures. There are no empirical data about the use of COCs. We examined the procurement and documentation of all these human subject protections in the genetics literature. A total of 112 (55%) articles documented IRB review, 108 (53%) document IC, and 82 (41%) documented both. None documented the procurement of a COC. Returned surveys provided additional information that confirmed that at least 74% (n = 150) of research had received appropriate IRB review, at least 71% (n = 143) had procured IC, and at least 10% (n = 21) had obtained a COC. An additional 22 respondents had procured COCs for other research, whereas 17 respondents were unaware of them and their purpose. In this era of public scrutiny of medical research, we recommend greater familiarity with and documentation of all human subject protections.     

Mouse genetic models in alcohol research

Animal models offer several advantages for the study of complex human disorders such as alcoholism. No animal model replicates all aspects of alcoholism but different components of the disorder can be investigated using various rodent models. In this article, we review a select subset of the most widely used mouse genetic models in alcohol research. Different genetically defined strains and stocks of mice are useful for genetic, physiologic, behavioral and pharmacological studies of this devastating disorder. In the past decade, numerous genomic regions associated with a tendency for various behavioral components of alcoholism have been identified; recent applications of new methods are shedding light on quantitative trait genes. Many of the underlying genes should be identified in the near future.