Nutrition in cancer prevention: an integrated approach

There is considerable evidence that the war on cancer is not being won. There is, however, strong evidence that a substantial fraction of cancer can be prevented by using existing nutritional knowledge. In this paper we discuss strategies for reducing cancer incidence by implementing this knowledge. The most obvious route for persuading large numbers to change their diets is by individual counseling in a health-care setting, public education campaigns and interventions at the worksite. However, such health promotion actions have met with only limited success. For efforts to change population diets to be successful, a vital component must include changes in government policies. Examples of the tools that need to be employed are restrictions on advertising and marketing. Effective action will likely require an economic dimension, namely the employment of taxation and subsidies, for instance, by taxing unhealthy food choices and by subsidizing fruit and vegetables

Nest expansion assay: a cancer systems biology approach to in vitro invasion measurements

<p>Abstract</p> <p>Background</p> <p>Traditional <it>in vitro </it>cell invasion assays focus on measuring one cell parameter at a time and are often less than ideal in terms of reproducibility and quantification. Further, many techniques are not suitable for quantifying the advancing margin of collectively migrating cells, arguably the most important area of activity during tumor invasion. We have developed and applied a highly quantitative, standardized, reproducible Nest Expansion Assay (NEA) to measure cancer cell invasion <it>in vitro</it>, which builds upon established wound-healing techniques. This assay involves creating uniform circular "nests" of cells within a monolayer of cells using a stabilized, silicone-tipped drill press, and quantifying the margin expansion into an overlaid extracellular matrix (ECM)-like component using computer-assisted applications.</p> <p>Findings</p> <p>The NEA was applied to two human-derived breast cell lines, MCF10A and MCF10A-CA1d, which exhibit opposite degrees of tumorigenicity and invasion <it>in vivo</it>. Assays were performed to incorporate various microenvironmental conditions, in order to test their influence on cell behavior and measures. Two types of computer-driven image analysis were performed using Java's freely available <it>ImageJ </it>software and its <it>FracLac </it>plugin to capture nest expansion and fractal dimension, respectively – which are both taken as indicators of invasiveness. Both analyses confirmed that the NEA is highly reproducible, and that the ECM component is key in defining invasive cell behavior. Interestingly, both analyses also detected significant differences between non-invasive and invasive cell lines, across various microenvironments, and over time.</p> <p>Conclusion</p> <p>The spatial nature of the NEA makes its outcome susceptible to the global influence of many cellular parameters at once (e.g., motility, protease secretion, cell-cell adhesion). We propose the NEA as a mid-throughput technique for screening and simultaneous examination of factors contributing to cancer cell invasion, particularly suitable for parameterizing and validating Cancer Systems Biology approaches such as mathematical modeling.</p

Automated detection of proliferative retinopathy in clinical practice

Timely intervention for diabetic retinopathy (DR) lessens the possibility of blindness and can save considerable costs to health systems. To ensure that interventions are timely and effective requires methods of screening and monitoring pathological changes, including assessing outcomes. Fractal analysis, one method that has been studied for assessing DR, is potentially relevant in today’s world of telemedicine because it provides objective indices from digital images of complex patterns such as are seen in retinal vasculature, which is affected in DR. We introduce here a protocol to distinguish between nonproliferative (NPDR) and proliferative (PDR) changes in retinal vasculature using a fractal analysis method known as local connected dimension (Dconn) analysis. The major finding is that compared to other fractal analysis methods, Dconn analysis better differentiates NPDR from PDR (p = 0.05). In addition, we are the first to show that fractal analysis can be used to differentiate between NPDR and PDR using automated vessel identification. Overall, our results suggest this protocol can complement existing methods by including an automated and objective measure obtainable at a lower level of expertise that experts can then use in screening for and monitoring DR

Fractal analysis of cervical intraepithelial neoplasia.

INTRODUCTION: Cervical intraepithelial neoplasias (CIN) represent precursor lesions of cervical cancer. These neoplastic lesions are traditionally subdivided into three categories CIN 1, CIN 2, and CIN 3, using microscopical criteria. The relation between grades of cervical intraepithelial neoplasia (CIN) and its fractal dimension was investigated to establish a basis for an objective diagnosis using the method proposed. METHODS: Classical evaluation of the tissue samples was performed by an experienced gynecologic pathologist. Tissue samples were scanned and saved as digital images using Aperio scanner and software. After image segmentation the box counting method as well as multifractal methods were applied to determine the relation between fractal dimension and grades of CIN. A total of 46 images were used to compare the pathologist's neoplasia grades with the predicted groups obtained by fractal methods. RESULTS: Significant or highly significant differences between all grades of CIN could be found. The confusion matrix, comparing between pathologist's grading and predicted group by fractal methods showed a match of 87.1%. Multifractal spectra were able to differentiate between normal epithelium and low grade as well as high grade neoplasia. CONCLUSION: Fractal dimension can be considered to be an objective parameter to grade cervical intraepithelial neoplasia

A @dual flow bioreactor for cartilage tissue engineering: Effect of synovial clearance and compressive load

Intra-articular injection of drug depots is considered as a therapeutic strategy for the treatment of osteoarthritis. In this study, we designed an in vitro assay in a previously described bioreactor system to evaluate the uptake of a small molecule drug mimic as a function of drug clearance by the synovium and compressive load. Bromophenol blue (BPB) loaded hydrogels were placed on top of bovine articular cartilage explants and were compressed in a dual flow bioreactor. As a control, BPB was directly injected in the bioreactor compartment mimicking the synovial fluid. Subsequently, diffusion coefficients of the dye were estimated based on Fick's law. Mimicking synovial clearance revealed that dye penetration of BPB when released from a drug delivery system placed on top of a cartilage explant was enhanced compared to direct injection of BPB into a simulated synovial fluid. Furthermore, we show the synergistic effect of the amount of load and the frequency on drug uptake by the cartilage. In the described model, we have shown that, under compressive load, drug delivery from a depot was beneficial over conventional intra-articular drug administration. The assay mimics the complexity of the knee joint in several key aspects, which results in a more close representation of the expected drug outcome. In this study, we have evaluated the penetration of a model small molecule drug into articular cartilage under compressive conditions, and future development will focus on incorporating synovial(-like) fluid, synovium, and bone to increase the predictive potential of the assay further

Relation between grades of neoplasia and normal epithelium.

<p>(A) Multifractal spectra and (B) Box plots of box counting dimension <i>d_BOX</i>.</p