A multi-objective strategy in genetic algorithms for gene selection of gene expression data

A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Surface enhanced Raman spectroscopy and its application to molecular and cellular analysis

In this paper, we review the state-of-the-art in surface-enhanced Raman scattering (SERS) based optical detection techniques with an application focus on cancer diagnostics. As we describe herein, SERS has several analytical, biological and engineering advantages over other methods including extremely high sensitivity, inherent molecular specificity of unlabeled targets, and narrow spectral bands. We review advances in both in vitro and in vivo applications of SERS and examine how technical issues with the technology are being addressed. A special technology focus is given to emerging optofluidic devices which aim to merge microfluidic and optical detection technologies into simple packages. We conclude with a brief discussion of some of the emerging challenges in the field and some of the approaches that are likely to enhance their application. Y. S. Huh and A. J. Chung contributed equally.     

Automatic joint classification and segmentation of whole cell 3D images

We present a machine learning tool for automatic texton-based joint classification and segmentation of mitochondria in MNT-1 cells imaged using ion-abrasion scanning electron microscopy (IA-SEM). For diagnosing signatures that may be unique to cellular states such as cancer, automatic tools with minimal user intervention need to be developed for analysis and mining of high-throughput data from these large volume data sets (typically ). Challenges for such a tool in 3D electron microscopy arise due to low contrast and signal-to-noise ratios (SNR) inherent to biological imaging. Our approach is based on block-wise classification of images into a trained list of regions. Given manually labeled images, our goal is to learn models that can localize novel instances of the regions in test datasets. Since datasets obtained using electron microscopes are intrinsically noisy, we improve the SNR of the data for automatic segmentation by implementing a 2D texture-preserving filter on each slice of the 3D dataset. We investigate texton-based region features in this work. Classification is performed by k-nearest neighbor (k-NN) classifier, support vector machines (SVMs), adaptive boosting (AdaBoost) and histogram matching using a NN classifier. In addition, we study the computational complexity vs. segmentation accuracy tradeoff of these classifiers. Segmentation results demonstrate that our approach using minimal training data performs close to semi-automatic methods using the variational level-set method and manual segmentation carried out by an experienced user. Using our method, which we show to have minimal user intervention and high classification accuracy, we investigate quantitative parameters such as volume of the cytoplasm occupied by mitochondria, differences between the surface area of inner and outer membranes and mean mitochondrial width which are quantities potentially relevant to distinguishing cancer cells from normal cells. To test the accuracy of our approach, these quantities are compared against manually computed counterparts. We also demonstrate extension of these methods to segment 3D images obtained using electron tomography.     

Toxicoproteomics and its application to human health risk assessment

Toxicoproteomics is the use of proteomic technologies to better understand environmental and genetic factors, toxic mechanisms, and modes of action in response to acute exposure to toxicants and in the long-term development of diseases caused or influenced by these exposures. Use of toxicoproteomic technologies to identify key biochemical pathways, mechanisms, and biomarkers of exposure and toxicity will decrease the uncertainties that are associated with human health risk assessments. This review provides an overview of toxicoproteomics from human health risk assessment perspectives. Key toxicoproteomic technologies such as 2-D gel-based proteomic methods and toxicoproteomic approaches are described, and examples of applications of these technologies and methodologies in the risk assessment context are presented. The discussion includes a focus on challenges and future directions.     

Quantitative detection of gold nanoparticles on individual, unstained cancer cells by scanning electron microscopy

Gold nanoparticles are rapidly emerging for use in biomedical applications. Characterization of the interaction and delivery of nanoparticles to cells through microscopy is important. Scanning electron microscopes have the intrinsic resolution to visualize gold nanoparticles on cells. A novel sample preparation protocol was developed to enable imaging of cells and gold nanoparticles with a conventional below lens scanning electron microscopes. The negative influence of 'charging' on the quality of scanning electron microscopes' images could be limited by deposition of biological cells on a conductive (gold) surface. The novel protocol enabled high-resolution scanning electron microscopes' imaging of small clusters and individual gold nanoparticles on uncoated cell surfaces. Gold nanoparticles could be counted on cancer cells with automated routines.     

经直肠前列腺超声造影参数成像预测前列腺癌的临床研究

目的评价经直肠前列腺超声造影参数成像对临床怀疑前列腺癌但超声没有发现明确病灶患者前列腺癌预测的临床应用价值。方法对30名血清PSA升高而灰阶超声没有发现明确病灶的患者在同天先后行经直肠前列腺超声造影及经直肠超声引导下前列腺穿刺活检的操作。造影图像切面选择在前列腺底部、中部及尖部与穿刺部位保持在同一切面,超声造影及穿刺活检由同一名医师承担。采用SonoProstate软件进行参数成像并分析造影参数图像。统计分析参数图像结果与病理结果。结果19例病理证实为前列腺癌的病例中参数成像方法诊断15例,11例病理证实为前列腺增生的病例中参数成像方法诊断9例。前列腺超声造影参数成像与超声引导下前列腺穿刺活检术对前列腺癌的预测无统计学差异(P=0.687)。结论对血清PSA升高但灰阶超声未发现明确前列腺病灶的患者,经直肠前列腺超声造影参数成像有助于提示前列腺癌可疑部位并指导前列腺穿刺活检。     

Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data

BackgroundThe application of microarray data for cancer classification is important. Researchers have tried to analyze gene expression data using various computational intelligence methods.PurposeWe propose a novel method for gene selection utilizing particle swarm optimization combined with a decision tree as the classifier to select a small number of informative genes from the thousands of genes in the data that can contribute in identifying cancers.ConclusionStatistical analysis reveals that our proposed method outperforms other popular classifiers, i.e., support vector machine, self-organizing map, back propagation neural network, and C4.5 decision tree, by conducting experiments on 11 gene expression cancer datasets.     

阳江高本底辐射地区流行病学研究概述(1986～2004年阶段)

高本底辐射研究组（中国）从1972年起在广东省阳江市高本底辐射地区进行居民健康调查和流行病学研究。本文是1986～2004年研究阶段的概述：简要地报道了环境辐射的测量和个人剂量估算、癌症死亡率的研究、非癌疾病死亡率的研究、稳定性染色体畸变频率和非稳定性畸变频率研究;对研究结果中发现的两个重要问题作了讨论。1991～2004年阶段的工作是研究组与日本国京都体质研究会合作完成的。本阶段国内完成单位是：中国疾病预防控制中心辐射防护与核安全医学所、广东省职业病防治院和湖南省劳动卫生职业病防治所。     

Gold-Nanoparticle-Decorated Metal-Organic Frameworks for Anticancer Therapy

Confinement of Au nanoparticles (NPs) within the porous materials with few nanometers (2-3 nm) has been a well established research area in the past decades in heterogeneous catalysis mainly due to the unique behaviour of Au NPs than its bulk counterpart. In this aspect, Au NPs encapsulated within the pore volumes of metal−organic frameworks (MOFs) have been intensively explored as heterogeneous solid catalysts for wide range of reactions. In recent years, Au NPs confined within the porous MOFs along with the photosensitizer or drug have been effectively used for the treatment of tumor cells through the generation of reactive oxygen species via cascade reactions. This work highlights the benefits of MOFs pores in the preparation of nanomedicine with high efficiency by assembling Au NPs, photosensitizer/drug with the combination of laser either for imaging or treatment of tumor cells. Further, the existing literature is grouped based on the nature of porous materials employed in the preparation of nanomedicine. The final section comments on our view on future developments in the field.