LIF and sIL-2R plasma concentrations in IVF patients on the day of embryo transfer: predictive markers of IVF outcome.

Successful implantation is still the limiting step in IVF. We hypothesized that maternal plasma concentrations of certain cytokines at the time of embryo transfer could predict the likelihood of successful implantation and pregnancy. sIL-2R, IL-6, LIF, and MMP2 concentrations were measured in plasma from 160 IVF patients (natural and stimulated IVF cycles) on the morning of the embryo transfer (ET0) and 14days later (ET+14). Patients were ultimately subdivided into four groups depending on the IVF treatment outcome (pregnancy failure, biochemical pregnancy, first-trimester miscarriage and normal term delivery). In natural and stimulated IVF cycles at ET0, sIL-2R concentrations were threefold higher in biochemical pregnancies than in pregnancy failures (P=0.020), and in natural cycles only, 2.5-fold higher in normal term deliveries than in pregnancy failures (P=0.023). Conversely, in natural and stimulated IVF cycles at ET0, LIF concentrations were one third lower in biochemical pregnancies/first-trimester miscarriages compared with pregnancy failures (P=0.042). We suggest that high sIL-2R and low LIF concentrations in maternal plasma on the morning of the embryo transfer might be associated with increased risks of early pregnancy loss, while a basal level of sIL-2R is necessary for normal term delivery outcome. Both cytokine measurements might therefore be useful in the management of IVF patients, and modulation of their concentrations could be investigated as a therapeutic alternative for women with abnormal concentrations at the time of embryo transfer.

Testing for convergence clubs in income per-capita: A predictive density approach

The paper proposes a technique to jointly test for groupings of unknown size in the cross sectional dimension of a panel and estimates the parameters of each group, and applies it to identifying convergence clubs in income per-capita. The approach uses the predictive density of the data, conditional on the parameters of the model. The steady state distribution of European regional data clusters around four poles of attraction with different economic features. The distribution of incomeper-capita of OECD countries has two poles of attraction and each grouphas clearly identifiable economic characteristics.

MGMT promoter methylation is prognostic but not predictive for outcome to adjuvant PCV chemotherapy in anaplastic oligodendroglial tumors: a report from EORTC Brain Tumor Group Study 26951.

PURPOSE: O6-methylguanine-methyltransferase (MGMT) promoter methylation has been shown to predict survival of patients with glioblastomas if temozolomide is added to radiotherapy (RT). It is unknown if MGMT promoter methylation is also predictive to outcome to RT followed by adjuvant procarbazine, lomustine, and vincristine (PCV) chemotherapy in patients with anaplastic oligodendroglial tumors (AOT). PATIENTS AND METHODS: In the European Organisation for the Research and Treatment of Cancer study 26951, 368 patients with AOT were randomly assigned to either RT alone or to RT followed by adjuvant PCV. From 165 patients of this study, formalin-fixed, paraffin-embedded tumor tissue was available for MGMT promoter methylation analysis. This was investigated with methylation specific multiplex ligation-dependent probe amplification. RESULTS: In 152 cases, an MGMT result was obtained, in 121 (80%) cases MGMT promoter methylation was observed. Methylation strongly correlated with combined loss of chromosome 1p and 19q loss (P = .00043). In multivariate analysis, MGMT promoter methylation, 1p/19q codeletion, tumor necrosis, and extent of resection were independent prognostic factors. The prognostic significance of MGMT promoter methylation was equally strong in the RT arm and the RT/PCV arm for both progression-free survival and overall survival. In tumors diagnosed at central pathology review as glioblastoma, no prognostic effect of MGMT promoter methylation was observed. CONCLUSION: In this study, on patients with AOT MGMT promoter methylation was of prognostic significance and did not have predictive significance for outcome to adjuvant PCV chemotherapy. The biologic effect of MGMT promoter methylation or pathogenetic features associated with MGMT promoter methylation may be different for AOT compared with glioblastoma.

Combining multivariate density forecasts using predictive criteria

This paper combines multivariate density forecasts of output growth, inflationand interest rates from a suite of models. An out-of-sample weighting scheme based onthe predictive likelihood as proposed by Eklund and Karlsson (2005) and Andersson andKarlsson (2007) is used to combine the models. Three classes of models are considered: aBayesian vector autoregression (BVAR), a factor-augmented vector autoregression (FAVAR)and a medium-scale dynamic stochastic general equilibrium (DSGE) model. Using Australiandata, we find that, at short forecast horizons, the Bayesian VAR model is assignedthe most weight, while at intermediate and longer horizons the factor model is preferred.The DSGE model is assigned little weight at all horizons, a result that can be attributedto the DSGE model producing density forecasts that are very wide when compared withthe actual distribution of observations. While a density forecast evaluation exercise revealslittle formal evidence that the optimally combined densities are superior to those from thebest-performing individual model, or a simple equal-weighting scheme, this may be a resultof the short sample available.

Resurgence of HIV infection among men who have sex with men in Switzerland: mathematical modelling study.

BACKGROUND: New HIV infections in men who have sex with men (MSM) have increased in Switzerland since 2000 despite combination antiretroviral therapy (cART). The objectives of this mathematical modelling study were: to describe the dynamics of the HIV epidemic in MSM in Switzerland using national data; to explore the effects of hypothetical prevention scenarios; and to conduct a multivariate sensitivity analysis. METHODOLOGY/PRINCIPAL FINDINGS: The model describes HIV transmission, progression and the effects of cART using differential equations. The model was fitted to Swiss HIV and AIDS surveillance data and twelve unknown parameters were estimated. Predicted numbers of diagnosed HIV infections and AIDS cases fitted the observed data well. By the end of 2010, an estimated 13.5% (95% CI 12.5, 14.6%) of all HIV-infected MSM were undiagnosed and accounted for 81.8% (95% CI 81.1, 82.4%) of new HIV infections. The transmission rate was at its lowest from 1995-1999, with a nadir of 46 incident HIV infections in 1999, but increased from 2000. The estimated number of new infections continued to increase to more than 250 in 2010, although the reproduction number was still below the epidemic threshold. Prevention scenarios included temporary reductions in risk behaviour, annual test and treat, and reduction in risk behaviour to levels observed earlier in the epidemic. These led to predicted reductions in new infections from 2 to 26% by 2020. Parameters related to disease progression and relative infectiousness at different HIV stages had the greatest influence on estimates of the net transmission rate. CONCLUSIONS/SIGNIFICANCE: The model outputs suggest that the increase in HIV transmission amongst MSM in Switzerland is the result of continuing risky sexual behaviour, particularly by those unaware of their infection status. Long term reductions in the incidence of HIV infection in MSM in Switzerland will require increased and sustained uptake of effective interventions.

Driver scheduling problem modelling

The Drivers Scheduling Problem (DSP) consists of selecting a set of duties for vehicle drivers, for example buses, trains, plane or boat drivers or pilots, for the transportation of passengers or goods. This is a complex problem because it involves several constraints related to labour and company rules and can also present different evaluation criteria and objectives. Being able to develop an adequate model for this problem that can represent the real problem as close as possible is an important research area.The main objective of this research work is to present new mathematical models to the DSP problem that represent all the complexity of the drivers scheduling problem, and also demonstrate that the solutions of these models can be easily implemented in real situations. This issue has been recognized by several authors and as important problem in Public Transportation. The most well-known and general formulation for the DSP is a Set Partition/Set Covering Model (SPP/SCP). However, to a large extend these models simplify some of the specific business aspects and issues of real problems. This makes it difficult to use these models as automatic planning systems because the schedules obtained must be modified manually to be implemented in real situations. Based on extensive passenger transportation experience in bus companies in Portugal, we propose new alternative models to formulate the DSP problem. These models are also based on Set Partitioning/Covering Models; however, they take into account the bus operator issues and the perspective opinions and environment of the user.We follow the steps of the Operations Research Methodology which consist of: Identify the Problem; Understand the System; Formulate a Mathematical Model; Verify the Model; Select the Best Alternative; Present the Results of theAnalysis and Implement and Evaluate. All the processes are done with close participation and involvement of the final users from different transportation companies. The planner s opinion and main criticisms are used to improve the proposed model in a continuous enrichment process. The final objective is to have a model that can be incorporated into an information system to be used as an automatic tool to produce driver schedules. Therefore, the criteria for evaluating the models is the capacity to generate real and useful schedules that can be implemented without many manual adjustments or modifications. We have considered the following as measures of the quality of the model: simplicity, solution quality and applicability. We tested the alternative models with a set of real data obtained from several different transportation companies and analyzed the optimal schedules obtained with respect to the applicability of the solution to the real situation. To do this, the schedules were analyzed by the planners to determine their quality and applicability. The main result of this work is the proposition of new mathematical models for the DSP that better represent the realities of the passenger transportation operators and lead to better schedules that can be implemented directly in real situations.

A test of the predictive validity of non-linear QALY models using time trade-off utilities

This paper presents a test of the predictive validity of various classes ofQALY models (i.e., linear, power and exponential models). We first estimatedTTO utilities for 43 EQ-5D chronic health states and next these states wereembedded in health profiles. The chronic TTO utilities were then used topredict the responses to TTO questions with health profiles. We find that thepower QALY model clearly outperforms linear and exponential QALY models.Optimal power coefficient is 0.65. Our results suggest that TTO-based QALYcalculations may be biased. This bias can be avoided using a power QALY model.

New evidence on inflation persistence and price stickiness in the Euro area: Implications for macro modelling

This paper evaluates new evidence on price setting practices and inflation persistence in the euro area with respect to its implications for macro modelling. It argues that several of the most commonly used assumptions in micro-founded macro models are seriously challenged by the new findings.

Modelling state dependence and feedback effects between poverty, employment and parental home emancipation among European youth

Youth is one of the phases in the life-cycle when some of the most decisivelife transitions take place. Entering the labour market or leaving parentalhome are events with important consequences for the economic well-beingof young adults. In this paper, the interrelationship between employment,residential emancipation and poverty dynamics is studied for eight Europeancountries by means of an econometric model with feedback effects. Resultsshow that youth poverty genuine state dependence is positive and highly significant.Evidence proves there is a strong causal effect between poverty andleaving home in Scandinavian countries, however, time in economic hardshipdoes not last long. In Southern Europe, instead, youth tend to leave theirparental home much later in order to avoid falling into a poverty state that ismore persistent. Past poverty has negative consequences on the likelihood ofemployment.

Demand for pharmaceutical drugs: A choice modelling experiment

Despite the importance of supplier inducement and brand loyalty inthe drug purchasing process, little empirical evidence is to be foundwith regard to the influence that these factors exert on patients decisions. Under the new scenario of easier access to information,patients are becoming more demanding and even go as far asquestioning their physicians prescription. Furthermore, newregulation also encourages patients to adopt an active role in thedecision between brand-name and generic drugs. Using a statedpreference model based on a choice survey, I have found evidenceof how significant physicians prescription and pharmacists recommendation become throughout the drug purchase process and,to what extent, brand loyalty influences the final decision. Asfar as we are aware, this paper is the first to explicitlytake consumers preferences into account rather than focusingon the behavior of health professionals.

The predictive value of trabecular bone score (TBS) on whole lumbar vertebrae mechanics: an ex vivo study.

We investigated the association of trabecular bone score (TBS) with microarchitecture and mechanical behavior of human lumbar vertebrae. We found that TBS reflects vertebral trabecular microarchitecture and is an independent predictor of vertebral mechanics. However, the addition of TBS to areal BMD (aBMD) did not significantly improve prediction of vertebral strength. INTRODUCTION: The trabecular bone score (TBS) is a gray-level measure of texture using a modified experimental variogram which can be extracted from dual-energy X-ray absorptiometry (DXA) images. The current study aimed to confirm whether TBS is associated with trabecular microarchitecture and mechanics of human lumbar vertebrae, and if its combination with BMD improves prediction of fracture risk. METHODS: Lumbar vertebrae (L3) were harvested fresh from 16 donors. The anteroposterior and lateral bone mineral content (BMC) and areal BMD (aBMD) of the vertebral body were measured using DXA; then, the TBS was extracted using TBS iNsight software (Medimaps SA, France). The trabecular bone volume (Tb.BV/tissue volume, TV), trabecular thickness (Tb.Th), degree of anisotropy, and structure model index (SMI) were measured using microcomputed tomography. Quasi-static uniaxial compressive testing was performed on L3 vertebral bodies to assess failure load and stiffness. RESULTS: The TBS was significantly correlated to Tb.BV/TV and SMI (râeuro0/00=âeuro0/000.58 and -0.62; pâeuro0/00=âeuro0/000.02, 0.01), but not related to BMC and BMD. TBS was significantly correlated with stiffness (râeuro0/00=âeuro0/000.64; pâeuro0/00=âeuro0/000.007), independently of bone mass. Using stepwise multiple regression models, we failed to demonstrate that the combination of BMD and TBS was better at explaining mechanical behavior than either variable alone. However, the combination TBS, Tb.Th, and BMC did perform better than each parameter alone, explaining 79 % of the variability in stiffness. CONCLUSIONS: In our study, TBS was associated with microarchitecture parameters and with vertebral mechanical behavior, but TBS did not improve prediction of vertebral biomechanical properties in addition to aBMD.

The predictive value of preoperative B-type natriuretic peptide in children undergooing heart surgery.

Introduction: Low cardiac output syndrome is frequent in childrenafter heart surgery for congenital heart disease and may result in pooroutcome and increased morbidity. In the adult population, preoperativebrain natriuretic peptide (BNP) was shown to be predictive of postoperative complications. In children, the value of preoperative BNP onpostoperative outcome is not so clear. The aim of this study was todetermine the predictive value of preoperative BNP on postoperativeoutcome and low cardiac output syndrome in children after heartsurgery for congenital heart disease.Methods: We examined, retrospectively, the postoperative course of97 pediatric patients (mean age 3.7 years, range 0-14 years old) whounderwent heart surgery in a tertiary care pediatric intensive caresetting. NTproBNP was measured preoperatively in all patients(median 412 pg/ml, range 12-35'000 pg/ml). Patients were divided intothree groups according to their NTproBNP levels (group 1: 0-300 pg/ml, group 2: 300-600 pg/ml, group 3: >600 pg/ml) and then,correlations with postoperative outcomes were examined.Results: We found that patients with a high preoperative BNP requiredmore frequently prolonged (>2 days) mechanical ventilation (33%vs 40% vs 61%, p = 0.045) and stayed more frequently longer than6 days in the intensive care unit (42% vs 50% vs 71%, p = 0.03).However, high preoperative BNP was not correlated with occurrenceof low cardiac output syndrome.Conclusion: Preoperative BNP cannot be used, in children, as areliable and sole predictor of postoperative low cardiac outputsyndrome. However it may help identify, before surgery, those patientsat risk of having a difficult postoperative course.

How quickly should we titrate antihypertensive medication? Systematic review modelling blood pressure response from trial data.

Context There are no evidence syntheses available to guide clinicians on when to titrate antihypertensive medication after initiation. Objective To model the blood pressure (BP) response after initiating antihypertensive medication. Data sources electronic databases including Medline, Embase, Cochrane Register and reference lists up to December 2009. Study selection Trials that initiated antihypertensive medication as single therapy in hypertensive patients who were either drug naive or had a placebo washout from previous drugs. Data extraction Office BP measurements at a minimum of two weekly intervals for a minimum of 4 weeks. An asymptotic approach model of BP response was assumed and non-linear mixed effects modelling used to calculate model parameters. Results and conclusions Eighteen trials that recruited 4168 patients met inclusion criteria. The time to reach 50% of the maximum estimated BP lowering effect was 1 week (systolic 0.91 weeks, 95% CI 0.74 to 1.10; diastolic 0.95, 0.75 to 1.15). Models incorporating drug class as a source of variability did not improve fit of the data. Incorporating the presence of a titration schedule improved model fit for both systolic and diastolic pressure. Titration increased both the predicted maximum effect and the time taken to reach 50% of the maximum (systolic 1.2 vs 0.7 weeks; diastolic 1.4 vs 0.7 weeks). Conclusions Estimates of the maximum efficacy of antihypertensive agents can be made early after starting therapy. This knowledge will guide clinicians in deciding when a newly started antihypertensive agent is likely to be effective or not at controlling BP.

Predictive value of the MGMT promoter methylation status in metastatic melanoma patients receiving first-line temozolomide plus bevacizumab in the trial SAKK 50/07.

The O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is a predictive parameter for the response of malignant gliomas to alkylating agents such as temozolomide. First clinical trials with temozolomide plus bevacizumab therapy in metastatic melanoma patients are ongoing, although the predictive value of the MGMT promoter methylation status in this setting remains unclear. We assessed MGMT promoter methylation in formalin-fixed, primary tumor tissue of metastatic melanoma patients treated with first-line temozolomide and bevacizumab from the trial SAKK 50/07 by methylation-specific polymerase chain reaction. In addition, the MGMT expression levels were also analyzed by MGMT immunohistochemistry. Eleven of 42 primary melanomas (26%) revealed a methylated MGMT promoter. Promoter methylation was significantly associated with response rates CR + PR versus SD + PD according to RECIST (response evaluation criteria in solid tumors) (p<0.05) with a trend to prolonged median progression-free survival (8.1 versus 3.4 months, p>0.05). Immunohistochemically different protein expression patterns with heterogeneous and homogeneous nuclear MGMT expression were identified. Negative MGMT expression levels were associated with overall disease stabilization CR+PR+SD versus PD (p=0.05). There was only a poor correlation between MGMT methylation and lack of MGMT expression. A significant proportion of melanomas have a methylated MGMT promoter. The MGMT promoter methylation status may be a promising predictive marker for temozolomide therapy in metastatic melanoma patients. Larger sample sizes may help to validate significant differences in survival type endpoints.

Which is the optimal sampling strategy for habitat suitability modelling

Designing an efficient sampling strategy is of crucial importance for habitat suitability modelling. This paper compares four such strategies, namely, 'random', 'regular', 'proportional-stratified' and 'equal -stratified'- to investigate (1) how they affect prediction accuracy and (2) how sensitive they are to sample size. In order to compare them, a virtual species approach (Ecol. Model. 145 (2001) 111) in a real landscape, based on reliable data, was chosen. The distribution of the virtual species was sampled 300 times using each of the four strategies in four sample sizes. The sampled data were then fed into a GLM to make two types of prediction: (1) habitat suitability and (2) presence/ absence. Comparing the predictions to the known distribution of the virtual species allows model accuracy to be assessed. Habitat suitability predictions were assessed by Pearson's correlation coefficient and presence/absence predictions by Cohen's K agreement coefficient. The results show the 'regular' and 'equal-stratified' sampling strategies to be the most accurate and most robust. We propose the following characteristics to improve sample design: (1) increase sample size, (2) prefer systematic to random sampling and (3) include environmental information in the design'