Computational structure-activity study directs synthesis of novel antitumor enkephalin analogs.

The capability of a Support Vector Machines QSAR model to predict the antiproliferative ability of small peptides was evaluated by screening a virtual library of enkephalin-like analogs modified by incorporation of the (R,S)-(1-adamantyl)glycine (Aaa) residue. From an initial set of 390 compounds, the peptides, Tyr-Aaa-Gly-Phe-Met (2), Tyr-Aaa-Gly-Phe-Phe (3), Phe-Aaa-Gly-Phe-Phe (4) and Phe-Aaa-Gly-Phe-Met (5) were selected, synthesized and their antitumor activity was tested and compared to that of Met-enkephalin (1). The antiproliferative activity correlated with the computational prediction and with the foldamer-forming ability of the studied peptides. The most active compounds were the hydrophobic peptides, Phe-Aaa-Gly-Phe-Phe (4) and Phe-Aaa-Gly-Phe-Met (5), having a greater propensity to adopt folded structures than the other peptides.

Classification accuracy of algorithms for blood chemistry data for three aquaculture-affected marine fish species.

The objective of this study was determination and discrimination of biochemical data among three aquaculture-affected marine fish species (sea bass, Dicentrarchus labrax; sea bream, Sparus aurata L., and mullet, Mugil spp.) based on machine-learning methods. The approach relying on machine-learning methods gives more usable classification solutions and provides better insight into the collected data. So far, these new methods have been applied to the problem of discrimination of blood chemistry data with respect to season and feed of a single species. This is the first time these classification algorithms have been used as a framework for rapid differentiation among three fish species. Among the machine-learning methods used, decision trees provided the clearest model, which correctly classified 210 samples or 85.71%, and incorrectly classified 35 samples or 14.29% and clearly identified three investigated species from their biochemical traits.

Determination of small objects from pinhole scintigrams.

This study assessed the possibility of measuring the linear dimensions of small structures using pinhole scintigraphy. A number of glass objects were made with a spherical, cylindrical or conical shape. Their maximum dimensions (diameters and heights) were 3.5-22.5 mm. These glass objects were filled with 131I, placed inside a plastic neck phantom and imaged using a gamma camera equipped with a pinhole collimator. The source-to-collimator distance was varied from 2 to 12 cm. An algorithm for image segmentation (threshold selection) was used to divide the image into object and background. On the segmented image, the number of non-zero pixels in the direction of the principal axes was multiplied by the appropriate calibration factor to obtain the linear dimensions of the object. Spatial resolution of the pinhole collimator, expressed as the full-width at half-maximum (FWHM), varied from 8 to 10 mm for the range of source-to-collimator distances examined. We found that, for dimensions up to 1.5 x FWHM, finite spatial resolution affects the accuracy of measurement. Non-linear correlation between true and calculated dimensions was used to take the latter into account. Our results are now being used to improve quantitation of remnant thyroid tissue masses for the calculation of radioiodine ablation doses.

Expression of 17beta-hydroxysteroid dehydrogenases and other estrogen-metabolizing enzymes in different cancer cell lines.

Estrogen action is regulated at the receptor level by regulation of expression of estrogen receptors, and at the pre-receptor level by interconversions between the active hormone (estradiol) and its inactive counterparts (estrone, estrone-sulfate). In peripheral tissues, estrogens can be produced via the aromatase or the sulfatase pathways. Aromatase converts androstenedione and testosterone to estrone and estradiol, respectively, and sulfatase releases estrogens from inactive sulfates, while sulfotransferase catalyzes the reverse reaction. In both pathways, 17beta-hydroxysteroid dehydrogenases (17beta-HSDs) are of paramount importance as they catalyze activation of estrone to estradiol and inactivation of estradiol to estrone. These enzymes belong to either the short-chain dehydrogenase/reductase (SDR) or the aldo-keto reductase (AKR) protein superfamilies. Differential expression of these pre-receptor regulatory enzymes can lead to high estradiol concentrations, which have been implicated in the development of different diseases. Here, we have examined gene expression levels of estrogen-metabolizing enzymes, as six SDRs (17beta-HSD types 1, 2, 4, 7, 8, 12) and one AKR (17beta-HSD type 5; AKR1C3), of aromatase, steroid sulfatase (STS) and estrogen sulfotransferase (SULT1E1), and of the alpha and beta estrogen receptors (ERs), in breast cancer (MCF-7), endometrial cancer (Ishikawa), choriocarcinoma (JEG3) and liver cancer (HepG2) cell lines. After RNA isolation and cDNA synthesis, real-time PCR analyses were performed. The expression of AKR1C3 was examined also at the protein level. Our data show that in all four cancer cell lines, estradiol can be synthesized from estrone by the action of 17beta-HSD type 12, or from estrone-sulfate by sulfatase. In JEG3 and HepG2 cells, estradiol can be formed from androgens by aromatase and 17beta-HSD type 1. Also in HepG2 cells, AKR1C3, which converts androstenedione to testosterone, in concert with aromatase might be responsible for estradiol formation. In MCF7 and Ishikawa cells, estradiol exerts its actions through ERalpha, while in JEG3 and HepG2 cells, it may act through non-ER-mediated pathways.