Assessment of radiation and chemotherapy efficacy for brain cancer patients is traditionally accomplished by measuring changes in tumor size several months after therapy has been administered. The ability to use noninvasive imaging during the early stages of fractionated therapy to determine whether a particular treatment will be effective would provide an opportunity to optimize individual patient management and avoid unnecessary systemic toxicity, expense, and treatment delays. We investigated whether changes in the Brownian motion of water within tumor tissue as quantified by using diffusion MRI could be used as a biomarker for early prediction of treatment response in brain cancer patients. Twenty brain tumor patients were examined by standard and diffusion MRI before initiation of treatment. Additional images were acquired 3 weeks after initiation of chemo-and͞or radiotherapy. Images were coregistered to pretreatment scans, and changes in tumor water diffusion values were calculated and displayed as a functional diffusion map (fDM) for correlation with clinical response. Of the 20 patients imaged during the course of therapy, 6 were classified as having a partial response, 6 as stable disease, and 8 as progressive disease. The fDMs were found to predict patient response at 3 weeks from the start of treatment, revealing that early changes in tumor diffusion values could be used as a prognostic indicator of subsequent volumetric tumor response. Overall, fDM analysis provided an early biomarker for predicting treatment response in brain tumor patients. diffusion MRI ͉ therapeutic response

We review the theoretical background to diffusion tensor imaging (DTI) and some of its commoner clinical applications, such as cerebral ischemia, brain maturation and traumatic brain injury. We also review its potential use in diseases such as epilepsy, multiple sclerosis, and Alzheimer's disease. The value of DTI in the investigation of brain tumors and metabolic disorders is assessed.

The source code for ACC transformation is freely available at http://www.iipl.fudan.edu.cn/demo/accpkg.html.

For users' convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided at http://bioinformatics.hitsz.edu.cn/main/~binliu/remote/

Directionally-ordered cellular structures that impede water motion, such as cell membranes and myelin, result in water mobility that is also directionally-dependent. Diffusion tensor imaging characterizes this directional nature of water motion and thereby provides structural information that cannot be obtained by standard anatomic imaging. Quantitative apparent diffusion coefficients and fractional anisotropy have emerged from being primarily research tools to methods enabling valuable clinical applications. This review describes the clinical utility of diffusion tensor imaging, including the basic principles of the technique, acquisition, data analysis, and the major clinical applications.

Protein remote homology detection is one of the most important problems in bioinformatics. Discriminative methods such as support vector machines (SVM) have shown superior performance. However, the performance of SVM-based methods depends on the vector representations of the protein sequences. Prior works have demonstrated that sequence-order effects are relevant for discrimination, but little work has explored how to incorporate the sequence-order information along with the amino acid physicochemical properties into the prediction. In order to incorporate the sequence-order effects into the protein remote homology detection, the physicochemical distance transformation (PDT) method is proposed. Each protein sequence is converted into a series of numbers by using the physicochemical property scores in the amino acid index (AAIndex), and then the sequence is converted into a fixed length vector by PDT. The sequence-order information can be efficiently included into the feature vector with little computational cost by this approach. Finally, the feature vectors are input into a support vector machine classifier to detect the protein remote homologies. Our experiments on a well-known benchmark show the proposed method SVM-PDT achieves superior or comparable performance with current state-of-the-art methods and its computational cost is considerably superior to those of other methods. When the evolutionary information extracted from the frequency profiles is combined with the PDT method, the profile-based PDT approach can improve the performance by 3.4% and 11.4% in terms of ROC score and ROC50 score respectively. The local sequence-order information of the protein can be efficiently captured by the proposed PDT and the physicochemical properties extracted from the amino acid index are incorporated into the prediction. The physicochemical distance transformation provides a general framework, which would be a valuable tool for protein-level study.

Background: Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences.

BackgroundThe dose-dependent toxicities of doxorubicin (DOX) limit its clinical applications, particularly in drug-resistant cancers, such as liver cancer. In this study, we investigated the role of quercetin on the antitumor effects of DOX on liver cancer cells and its ability to provide protection against DOX-mediated liver damage in mice.Methodology and ResultsThe MTT and Annexin V/PI staining assay demonstrated that quercetin selectively sensitized DOX-induced cytotoxicity against liver cancer cells while protecting normal liver cells. The increase in DOX-mediated apoptosis in hepatoma cells by quercetin was p53-dependent and occurred by downregulating Bcl-xl expression. Z-VAD-fmk (caspase inhibitor), pifithrin-α (p53 inhibitor), or overexpressed Bcl-xl decreased the effects of quercetin on DOX-mediated apoptosis. The combined treatment of quercetin and DOX significantly reduced the growth of liver cancer xenografts in mice. Moreover, quercetin decreased the serum levels of alanine aminotransferase and aspartate aminotransferase that were increased in DOX-treated mice. Quercetin also reversed the DOX-induced pathological changes in mice livers.Conclusion and SignificanceThese results indicate that quercetin potentiated the antitumor effects of DOX on liver cancer cells while protecting normal liver cells. Therefore, the development of quercetin may be beneficial in a combined treatment with DOX for increased therapeutic efficacy against liver cancer.