Comprehensive Serum Profiling for the Discovery of Epithelial Ovarian Cancer Biomarkers

FDA-cleared ovarian cancer biomarkers are limited to CA-125 and HE4 for monitoring and recurrence and OVA1, a multivariate panel consisting of CA-125 and four additional biomarkers, for referring patients to a specialist. Due to relatively poor performance of these tests, more accurate and broadly applicable biomarkers are needed. We evaluated the dysregulation of 259 candidate cancer markers in serum samples from 499 patients. Sera were collected prospectively at 11 monitored sites under a single well-defined protocol. All stages of ovarian cancer and common benign gynecological conditions were represented. To ensure consistency and comparability of biomarker comparisons, all measurements were performed on a single platform, at a single site, using a panel of rigorously calibrated, qualified, high-throughput, multiplexed immunoassays and all analyses were conducted using the same software. Each marker was evaluated independently for its ability to differentiate ovarian cancer from benign conditions. A total of 175 markers were dysregulated in the cancer samples. HE4 (AUC = 0.933) and CA-125 (AUC = 0.907) were the most informative biomarkers, followed by IL-2 receptor α, α1-antitrypsin, C-reactive protein, YKL-40, cellular fibronectin, CA-72-4 and prostasin (AUC>0.800). To improve the discrimination between cancer and benign conditions, a simple multivariate combination of markers was explored using logistic regression. When combined into a single panel, the nine most informative individual biomarkers yielded an AUC value of 0.950, significantly higher than obtained when combining the markers in the OVA1 panel (AUC 0.912). Additionally, at a threshold sensitivity of 90%, the combination of the top 9 markers gave 88.9% specificity compared to 63.4% specificity for the OVA1 markers. Although a blinded validation study has not yet been performed, these results indicate that alternative biomarker combinations might lead to significant improvements in the detection of ovarian cancer

Effectiveness of acupuncture, special dressings and simple, low-adherence dressings for healing venous leg ulcers in primary healthcare: study protocol for a cluster-randomized open-labeled trial

<p>Abstract</p> <p>Background</p> <p>Venous leg ulcers constitute a chronic recurring complaint that affects 1.0–1.3% of the adult population at some time in life, and which corresponds to approximately 75% of all chronic ulcers of the leg. Multilayer compression bandaging is, at present, the only treatment that has been proved to be effective in treating this type of ulcer. There is no consensus, however, about the dressings that may be applied, beneath the compression, to promote the healing of this type of ulcer, as there does not seem to be any added benefit from using special dressings rather than simple, low-adherence ones. As well as analgesia, acupuncture provokes peripheral vasodilation, in skin and muscles – which has been demonstrated both experimentally and in clinical practice – probably due to the axon reflex, among other mechanisms. The aim of the present study is to measure the effectiveness and cost of compression treatment for venous leg ulcers combined with special dressings, in comparison with low-adherence ones and acupuncture.</p> <p>Methods/design</p> <p>Cluster-randomized open-labeled trial, at 15 primary healthcare clinics in the Sevilla-Sur Healthcare District, with a control group treated with compression bandaging and low-adherence dressings; the experiment will consist, on the one hand, of the compression treatment applied in combination with special dressings (Treatment 1), and on the other, the compression treatment applied in association with low-adherence dressings, together with acupuncture (Treatment 2).</p> <p>Discussion</p> <p>The results will be measured and recorded in terms of the median time elapsed until complete healing of the ulcer, and the rate of complete healing at 3 months after beginning the treatment. An economic analysis will also be made.</p> <p>This study, carried out in the context of real clinical practice, will provide information for decision-taking concerning the effectiveness of special dressings. Moreover, for the first time a high-quality study will evaluate the effectiveness of acupuncture in the process of healing venous leg ulcers.</p> <p>Trial registration</p> <p>Current Controlled Trials ISRCTN26438275.</p

<Book Reviews> Ingemar Fagerlind and Lawrence J Saha Education and National Development : A Comparative Perspective

textabstractVarious modeling methods have been proposed to estimate the potential predictive ability of polygenic risk variants that predispose to various common diseases. However, it is unknown whether differences between them affect their conclusions on predictive ability. We reviewed input parameters, assumptions and output of the five most common methods and compared their estimates of the area under the receiver operating characteristic (ROC) curve (AUC) using hypothetical data representing effect sizes and frequencies of genetic variants, population disease risk and number of variants. To assess the accuracy of the estimated AUCs, we aimed to reproduce the AUCs of published empirical studies. All methods assumed that the combined effect of genetic variants on disease risk followed a multiplicative risk model of independent genetic effects, but they either assumed per allele, per genotype or dominant/recessive effects for the genetic variants. Modeling strategy and input parameters differed. Methods used simulation analysis or analytical formulas with effect sizes quantified by odds ratios (ORs) or relative risks. Estimated AUC values were similar for lower ORs (0.7) due to variants with strong effects, differences in estimated AUCs between methods increased. The simulation methods accurately reproduced the AUC values of empirical studies, but the analytical methods did not. We conclude that despite differences in input parameters, the modeling methods estimate similar AUC for realistic values of the ORs. When one or more variants have stronger effects and AUC values are higher, the simulation methods tend to be more accurate