Vascular endothelial growth factor in premenopausal women--indicator of the best time for breast cancer surgery?

Timing of surgery in premenopausal patients with breast cancer remains controversial. Angiogenesis is essential for tumour growth and vascular endothelial growth factor (VEGF) is one of the most potent angiogenic cytokines. We aimed to determine whether the study of VEGF in relation to the menstrual cycle could help further the understanding of this issue of surgical intervention. Fourteen premenopausal women were recruited, along with three post-menopausal women, a woman on an oral contraceptive pill and a single male subject. Between eight and 11 samples were taken per person, over one menstrual cycle (over 1 month in the five controls) and analysed for sex hormones and VEGF165. Serum VEGF was significantly lower in the luteal phase and showed a significant negative correlation with progesterone in all 14 premenopausal women. No inter-sample variations of VEGF were noted in the controls. Serum from both phases of the cycle from one subject was added to MCF-7 breast cancer cells; VEGF expression in the supernatant was lower in the cells to which the luteal phase serum was added. The lowering of a potent angiogenic cytokine in the luteal phase suggests a possible decreased potential for micrometastasis establishment in that phase. This fall in VEGF may be an effect of progesterone and should be the focus of future studies

A simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.

Estimation of pest density is a basic requirement for integrated pest management in agriculture and forestry, and efficiency in density estimation is a common goal. Sequential sampling techniques promise efficient sampling, but their application can involve cumbersome mathematics and/or intensive warm-up sampling when pests have complex within- or between-site distributions. We provide tools for assessing the efficiency of sequential sampling and of alternative, simpler sampling plans, using computer simulation with "pre-sampling" data. We illustrate our approach using data for balsam gall midge (Paradiplosis tumifex) attack in Christmas tree farms. Paradiplosis tumifex proved recalcitrant to sequential sampling techniques. Midge distributions could not be fit by a common negative binomial distribution across sites. Local parameterization, using warm-up samples to estimate the clumping parameter k for each site, performed poorly: k estimates were unreliable even for samples of n ∼ 100 trees. These methods were further confounded by significant within-site spatial autocorrelation. Much simpler sampling schemes, involving random or belt-transect sampling to preset sample sizes, were effective and efficient for P. tumifex. Sampling via belt transects (through the longest dimension of a stand) was the most efficient, with sample means converging on true mean density for sample sizes of n ∼ 25-40 trees. Pre-sampling and simulation techniques provide a simple method for assessing sampling strategies for estimating insect infestation. We suspect that many pests will resemble P. tumifex in challenging the assumptions of sequential sampling methods. Our software will allow practitioners to optimize sampling strategies before they are brought to real-world applications, while potentially avoiding the need for the cumbersome calculations required for sequential sampling methods

Performance of random sampling for estimating mean <i>P. tumifex</i> density (top panels) and decision making against infestation thresholds (bottom panels) at representative sites C and G.

<p>Dashed lines show two representative randomizations; 95% of the 10,000 randomizations lie between the solid lines. Confidence envelopes still have finite width at <i>n</i> = 200 (the size of the total site sample) because sampling is conducted with replacement. Plots for all seven sites appear in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082618#pone.0082618.s005" target="_blank">Figure S5</a>.</p

Negative binomial fits for percentage of needles galled, sites B and C.

<p>Site B is typical of sites with acceptable fits, whereas Site C is the worst-fitting site. Fits for all seven sites appear in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082618#pone.0082618.s003" target="_blank">Figure S3</a>.</p

Negative-binomial fits for gall counts and for (rounded) percentage of needles galled.

<p>Entries for χ², df, and <i>P</i> are for likelihood-ratio goodness-of-fits tests. For % galling, “precision” is half the width of the 95% confidence envelope divided by the estimated infestation (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082618#pone.0082618.s005" target="_blank">Figure S5</a>).</p

Variation in estimation of negative-binomial <i>k</i> at sites A and G, based on subsampling.

<p>Horizontal line indicates the true value of <i>k</i> (estimated using the full data set). Boxes show central 50%, and whiskers central 90%, of estimates. Plots for all seven sites appear in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082618#pone.0082618.s004" target="_blank">Figure S4</a>.</p

Tests for within-site spatial autocorrelation in <i>P. tumifex</i> infestation.

<p>Tests for within-site spatial autocorrelation in <i>P. tumifex</i> infestation.</p

Spatial structure in <i>P. tumifex</i> infestation at sites with significant spatial autocorrelation.

<p>Spatial structure in <i>P. tumifex</i> infestation at sites with significant spatial autocorrelation.</p

Estimation errors for <i>P. tumifex</i> infestation as a function of number of sampled trees.

<p>Estimation errors for <i>P. tumifex</i> infestation as a function of number of sampled trees.</p