Natural compounds obtained from plants are capable of garnering considerable attention from the scientific community, primarily due to their ability to check and prevent the onset and progress of cancer. These natural compounds are primarily used due to their nontoxic nature and the fewer side effects they cause compared to chemotherapeutic drugs. Furthermore, such natural products perform even better when given as an adjuvant along with traditional chemotherapeutic drugs, thereby enhancing the potential of chemotherapeutics and simultaneously reducing their undesired side effects. Curcumin, a naturally occurring polyphenol compound found in the plant Curcuma longa, is used as an Indian spice. It regulates not only the various pathways of the immune system, cell cycle checkpoints, apoptosis, and antioxidant response but also numerous intracellular targets, including pathways and protein molecules controlling tumor progression. Many recent studies conducted by major research groups around the globe suggest the use of curcumin as a chemopreventive adjuvant molecule to maximize and minimize the desired effects and side effects of chemotherapeutic drugs. However, low bioavailability of a curcumin molecule is the primary challenge encountered in adjuvant therapy. This review explores different therapeutic interactions of curcumin along with its targeted pathways and molecules that are involved in the regulation of onset and progression of different types of cancers, cancer treatment, and the strategies to overcome bioavailability issues and new targets of curcumin in the ever-growing field of cancer.

No abstract

No abstract

With the advancement of microarray technology, gene expression profiling has shown great potential in outcome prediction for different types of cancers. Microarray cancer data, organized as samples versus genes fashion, are being exploited for the classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer type. Nevertheless, small sample size remains a bottleneck to design suitable classifiers. Traditional supervised classifiers can only work with labeled data. On the other hand, a large number of microarray data that do not have adequate follow-up information are disregarded. A novel approach to combine feature (gene) selection and transductive support vector machine (TSVM) is proposed. We demonstrated that 1) potential gene markers could be identified and 2) TSVMs improved prediction accuracy as compared to the standard inductive SVMs (ISVMs). A forward greedy search algorithm based on consistency and a statistic called signal-to-noise ratio were employed to obtain the potential gene markers. The selected genes of the microarray data were then exploited to design the TSVM. Experimental results confirm the effectiveness of the proposed technique compared to the ISVM and low-density separation method in the area of semisupervised cancer classification as well as gene-marker identification.

No abstract

No abstract

DNA microarray data now permit scientists to screen thousand of genes simultaneously and determine whether those genes are active or silent in normal and cancerous tissues. With the advancement of microarray technology, new analytical methods must be developed to find out whether microarray data have discriminative signatures of gene expression over normal or cancerous tissues. In this paper, we attempt a prediction scheme that combines fuzzy preference based rough set (FPRS) method for feature (gene) selection with semisupervised SVMs. To show the effectiveness of the proposed approach, we compare the performance of this technique with the signal-to-noise ratio (SNR) and consistency based feature selection (CBFS) methods. Using six benchmark gene microarray datasets (including both binary and multi-class classification problems), we demonstrate experimentally that our proposed scheme can achieve significant empirical success and is biologically relevant for cancer diagnosis and drug discovery.

Efficient combustion and heat release in scramjet flows depend on effective mixing of the fuel in supersonic streams. Usually, transverse sonic injection in-stages are employed as one of the suitable means for efficient supersonic combustor design. Numerical simulations are carried out to study the mixing characteristics of staged sonic air injections in supersonic stream (M ¼ 2.07) behind a backward-facing step in scramjet combustor by solving threedimensional Navier -Stokes equations along with K -1 turbulence model with a commercial CFD software CFX-TASCFlow. Computed results of the jet penetration and spreading show very good agreement with the experimental values and the results of other computations. A good overall match has been obtained between the experimental values and the computation for various flow profiles at various axial locations in the combustor. However, the values differ in the near-field region at the injection plane. The assumed uniformity of the flow-field properties at the injection orifice and/or the inadequacy of the turbulence model considered in this study is conjectured to be the cause of the difference.