Modeling human pollution in water bodies using somatic coliphages and bacteriophages that infect Bacteroides thetaiotaomicron strain GA17.

The ability to detect human fecal pollution in water is of great importance when assessing the associated health risks. Many microbial source tracking (MST) markers have been proposed to determine the origin of fecal pollution, but their application remains challenging. A range of factors, not yet sufficiently analyzed, may affect MST markers in the environment, such as dilution and inactivation processes. In this work, a statistical framework based on Monte Carlo simulations and non-linear regression was used to develop a classification procedure for use in MST studies. The predictive model tested uses only two parameters: somatic coliphages (SOMCPH), as an index of general fecal pollution, and human host-specific bacteriophages that infect Bacteroides thetaiotaomicron strain GA17 (GA17PH). Taking into account bacteriophage dilution and differential inactivation, the threshold concentration of SOMCPH was calculated to be around 500 PFU/100 mL for a limit of detection of 10 PFU/100 mL. However, this threshold can be lowered by increasing the analyzed volume sample, which in turn lowers the limit of detection. The resulting model is sufficiently accurate for application in practical cases involving MST and could be easily used with markers other than those tested here.

Comparative efficiency research (COMER): meta-analysis of cost-effectiveness studies.

BACKGROUND: The aim of this study was to create a new meta-analysis method for cost-effectiveness studies using comparative efficiency research (COMER). METHODS: We built a new score named total incremental net benefit (TINB), with inverse variance weighting of incremental net benefits (INB). This permits determination of whether an alternative is cost-effective, given a specific threshold (TINB > 0 test). Before validation of the model, the structure of dependence between costs and quality-adjusted life years (QoL) was analysed using copula distributions. The goodness-of-fit of a Spanish prospective observational study (n = 498) was analysed using the Independent, Gaussian, T, Gumbel, Clayton, Frank and Placket copulas. Validation was carried out by simulating a copula distribution with log-normal distribution for costs and gamma distribution for disutilities. Hypothetical cohorts were created by varying the sample size (n: 15-500) and assuming three scenarios (1-cost-effective; 2-non-cost-effective; 3-dominant). The COMER result was compared to the theoretical result according to the incremental cost-effectiveness ratio (ICER) and the INB, assuming a margin of error of 2,000 and 500 monetary units, respectively. RESULTS: The Frank copula with positive dependence (-0.4279) showed a goodness-of-fit sufficient to represent costs and QoL (p-values 0.524 and 0.808). The theoretical INB was within the 95% confidence interval of the TINB, based on 15 individuals with a probability > 80% for scenarios 1 and 2, and > 90% for scenario 3. The TINB > 0 test with 15 individuals showed p-values of 0.0105 (SD: 0.0411) for scenario 1, 0.613 (SD: 0.265) for scenario 2 and < 0.0001 for scenario 3. CONCLUSIONS: COMER is a valid tool for combining cost-effectiveness studies and may be of use to health decision makers.

Prognostic factors and long-term outcome of pancreatic neuroendocrine neoplasms: Ki-67 index shows a greater impact on survival than disease stage. The large experience of the Spanish National Tumor Registry (RGETNE).

INTRODUCTION: Pancreatic neuroendocrine neoplasms (PNENs) are uncommon neoplasms with a wide spectrum of clinical behavior. The objective of this study was to assess in a large cohort of patients the relative impact of prognostic factors on survival. METHODS: From June 2001 through October 2010, 1,271 patients were prospectively registered online (www.getne.org) at the Spanish National Cancer Registry for Gastroenteropancreatic Neuroendocrine Tumors (RGETNE) by participating centers. Clinical and histopathological features were assessed as potential prognostic factors by uni- and multivariate analyses. RESULTS: Of 483 PNENs, 171 (35%) were functional (F) and 312 (65%) non-functional (NF). NF-PNENs were associated with a higher incidence of histological features denoting more aggressive disease, such as poor tumor differentiation, Ki-67 >20%, or vascular invasion (NF- vs. F-PNENs, respectively, p < 0.05). Nevertheless, functionality was not a significant predictor of survival (p = 0.19). Stage at diagnosis, Ki-67 index, tumor differentiation and surgical resection of the primary tumor were all significant prognostic factors in univariate analysis. However, Ki-67 (>20 vs. ≤2%) (hazard ratio (HR) 2.21, p = 0.01) and surgical resection (yes vs. no) (HR 0.92, p = 0.001) were the only independent predictors of survival in multivariate analysis. Among patients who underwent surgery, high Ki-67 index (HR 10.37, p = 0.02) and poor differentiation (HR 8.16, p = 0.03) were the only independent predictors of clinical outcome. CONCLUSION: Ki-67 index and tumor differentiation are key prognostic factors influencing survival of patients with PNENs and, in contrast to what it is observed for other solid malignancies, they seem to have a greater impact on survival than the extent of disease. This should be borne in mind by physicians in order to appropriately tailor therapeutic strategies and surveillance of these patients.