Diterpenoids of Roylea calycina(Roxb.) Briq.

The structures of three new diterpenes, calyone, calyenone, and precalyone, isolated from the aerial portion of Roylea calycina have been shown to be 3-acetoxy-15,16-epoxy-9-hydroxylabda-1 3(16),14-dien-7-one (2), 3-acetoxy-15,16-epoxylabda-8,13(16),14-trien-7-one (5), and 3-acetoxy-9,13;15,16-diepoxylabda-14-en-7-one (7), respectively, by chemical and spectroscopic studies. Precalyone showed antitumor activity against P-388 lymphocytic leukaemia.

Extracts of Strawberry Fruits Induce Intrinsic Pathway of Apoptosis in Breast Cancer Cells and Inhibits Tumor Progression in Mice

Background: The consumption of berry fruits, including strawberries, has been suggested to have beneficial effects against oxidative stress mediated diseases. Berries contain multiple phenolic compounds and secondary metabolites that contribute to their biological properties. Methodology/Principal Findings: Current study investigates the anticancer activity of the methanolic extract of strawberry (MESB) fruits in leukaemia (CEM) and breast cancer (T47D) cell lines ex vivo, and its cancer therapeutic and chemopreventive potential in mice models. Results of MTT, trypan blue and LDH assays suggested that MESB can induce cytotoxicity in cancer cells, irrespective of origin, in a concentration-and time-dependent manner. Treatment of mice bearing breast adenocarcinoma with MESB blocked the proliferation of tumor cells in a time-dependent manner and resulted in extended life span. Histological and immunohistochemical studies suggest that MESB treatment affected tumor cell proliferation by activating apoptosis and did not result in any side effects. Finally, we show that MESB can induce intrinsic pathway of apoptosis by activating p73 in breast cancer cells, when tumor suppressor gene p53 is mutated. Conclusions/Significance: The present study reveals that strawberry fruits possess both cancer preventive and therapeutic values and we discuss the mechanism by which it is achieved.

Framework for morphometric classification of cells in imaging flow cytometry

Imaging flow cytometry is an emerging technology that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy. It allows high-throughput imaging of cells with good spatial resolution, while they are in flow. This paper proposes a general framework for the processing/classification of cells imaged using imaging flow cytometer. Each cell is localized by finding an accurate cell contour. Then, features reflecting cell size, circularity and complexity are extracted for the classification using SVM. Unlike the conventional iterative, semi-automatic segmentation algorithms such as active contour, we propose a noniterative, fully automatic graph-based cell localization. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label-free leukaemia cell-lines MOLT, K562 and HL60 from video streams captured using custom fabricated cost-effective microfluidics-based imaging flow cytometer. The proposed system is a significant development in the direction of building a cost-effective cell analysis platform that would facilitate affordable mass screening camps looking cellular morphology for disease diagnosis. Lay description In this article, we propose a novel framework for processing the raw data generated using microfluidics based imaging flow cytometers. Microfluidics microscopy or microfluidics based imaging flow cytometry (mIFC) is a recent microscopy paradigm, that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy, which allows us imaging cells while they are in flow. In comparison to the conventional slide-based imaging systems, mIFC is a nascent technology enabling high throughput imaging of cells and is yet to take the form of a clinical diagnostic tool. The proposed framework process the raw data generated by the mIFC systems. The framework incorporates several steps: beginning from pre-processing of the raw video frames to enhance the contents of the cell, localising the cell by a novel, fully automatic, non-iterative graph based algorithm, extraction of different quantitative morphological parameters and subsequent classification of cells. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label-free leukaemia cell-lines MOLT, K562 and HL60 from video streams captured using cost-effective microfluidics based imaging flow cytometer. The cell lines of HL60, K562 and MOLT were obtained from ATCC (American Type Culture Collection) and are separately cultured in the lab. Thus, each culture contains cells from its own category alone and thereby provides the ground truth. Each cell is localised by finding a closed cell contour by defining a directed, weighted graph from the Canny edge images of the cell such that the closed contour lies along the shortest weighted path surrounding the centroid of the cell from a starting point on a good curve segment to an immediate endpoint. Once the cell is localised, morphological features reflecting size, shape and complexity of the cells are extracted and used to develop a support vector machine based classification system. We could classify the cell-lines with good accuracy and the results were quite consistent across different cross validation experiments. We hope that imaging flow cytometers equipped with the proposed framework for image processing would enable cost-effective, automated and reliable disease screening in over-loaded facilities, which cannot afford to hire skilled personnel in large numbers. Such platforms would potentially facilitate screening camps in low income group countries; thereby transforming the current health care paradigms by enabling rapid, automated diagnosis for diseases like cancer.