Structural basis for cooperativity of human monoclonal antibodies to meningococcal factor H-binding protein

Monoclonal antibody (mAb) cooperativity is a phenomenon triggered when mAbs couples promote increased bactericidal killing compared to individual partners. Cooperativity has been deeply investigated among mAbs elicited by factor H-binding protein (fHbp), a Neisseria meningitidis surface-exposed lipoprotein and one of the key antigens included in both serogroup B meningococcus vaccine Bexsero and Trumenba. Here we report the structural and functional characterization of two cooperative mAbs pairs isolated from Bexsero vaccines. The 3D electron microscopy structures of the human mAb-fHbp-mAb cooperative complexes indicate that the angle formed between the antigen binding fragments (fAbs) assume regular angle and that fHbp is able to bind simultaneously and stably the cooperative mAbs pairs and human factor H (fH) in vitro. These findings shed light on molecular basis of the antibody-based mechanism of protection driven by simultaneous recognition of the different epitopes of the fHbp and underline that cooperativity is crucial in vaccine efficacy

Specific Immunoassays Confirm Association of Mycobacterium avium Subsp. paratuberculosis with Type-1 but Not Type-2 Diabetes Mellitus

Mycobacterium avium subspecies paratuberculosis (MAP) is a versatile pathogen with a broad host range. Its association with type-1 diabetes mellitus (T1DM) has been recently proposed. Rapid identification of infectious agents such as MAP in diabetic patients at the level of clinics might be helpful in deciphering the role of chronic bacterial infection in the development of autoimmune diseases such as T1DM.We describe use of an ELISA method to identify live circulating MAP through the detection of a cell envelope protein, MptD by a specific M13 phage--fMptD. We also used another ELISA format to detect immune response to MptD peptide. Both the methods were tested with blood plasma obtained from T1DM, type-2 diabetes (T2DM) patients and non-diabetic controls. Our results demonstrate MptD and fMptD ELISA assays to be accurate and sensitive to detect MAP bacilli in a large fraction (47.3%) of T1DM patients as compared to non-diabetic controls (12.6%) and those with confirmed T2DM (7.7%). Comparative analysis of ELISA assays performed here with 3 other MAP antigen preparations, namely HbHA, Gsd and whole cell MAP lysates confirmed comparable sensitivity of the MptD peptide and the fMptD based ELISA assays. Moreover, we were successful in demonstrating positive bacterial culture in two of the clinical specimen derived from T1DM patients.The MptD peptide/fMptD based ELISA or similar tests could be suggested as rapid and specific field level diagnostic tests for the identification of MAP in diabetic patients and for finding the explanations towards the occurrence of type-1 or type-2 diabetes in the light of an active infectious trigger

Linking Chronic Infection and Autoimmune Diseases: Mycobacterium avium Subspecies paratuberculosis, SLC11A1 Polymorphisms and Type-1 Diabetes Mellitus

(MAP) has been reported to be a possible trigger in the development of T1DM. gene (274C/T) associated to type 1 diabetic patients and not to controls. The presence of MAP DNA was also significantly associated with T1DM patients and not with controls. alter the processing or presentation of MAP antigens triggering thereby an autoimmune response in T1DM patients

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Risk Factors for Hepatitis C Infection Among Sexually Transmitted Disease-Infected, Inner City Obstetric Patients

Objective: To test the hypothesis that our inner city obstetric patients who have been infected with sexually transmitted diseases (STDs) will have a higher prevalence of hepatitis C virus infection than the general population and to identify specific risk factors and high-risk groups. Methods: All patients in our prenatal clinic (July 1997–April 1999) who tested positive for one or more STDs were asked to return for hepatitis C antibody testing. Medical charts of all patients who returned for hepatitis C testing were reviewed. Results: A total of 106 patients with STDs were tested for hepatitis C. Positive screening tests for anti-hepatitis C antibody were found in 6.6% (7/106) of the patients (95% CI = 2.7–13.1%). This frequency is significantly higher than the hepatitis C prevalence (1.8%) in the general United States population (p = 0.006). Multiple logistic regression analysis confirmed only older age (p = 0.016) and positive HIV status (p = 0.023) to be significant predictors of hepatitis C infection. Conclusions: Inner city STD-infected obstetric patients are at high risk for hepatitis C infection compared with the general population. Increasing age and HIV-positive status are risk factors which are significantly associated with hepatitis C infection

Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models

Advanced Bayesian methods are employed in estimating dynamic stochastic general equilibrium (DSGE) models. Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be taken directly to the data and often yield weak prediction results. Hybrid models can deal with some of the DSGE model misspecifications. Major advances in Bayesian estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. A comparative evaluation of the out-of-sample predictive performance of many different specifications of estimated DSGE models and various classes of VAR models is performed, using datasets from the US economy. Simple and hybrid DSGE models are implemented, such as DSGE–VAR and Factor Augmented DSGEs and tested against standard, Bayesian and Factor Augmented VARs. Moreover, small scale models including the real gross domestic product, the harmonized consumer price index and the nominal short-term federal funds interest rate, are comparatively assessed against medium scale models featuring additionally sticky nominal prices, wage contracts, habit formation, variable capital utilization and investment adjustment costs. The investigated period spans 1960:Q4–2010:Q4 and forecasts are produced for the out-of-sample testing period 1997:Q1–2010:Q4. This comparative validation can be useful to monetary policy analysis and macro-forecasting with the use of advanced Bayesian methods.European Commission - Seventh Framework Programme (FP7)'Dote ricercatori': FSE, Regione Lombardi

On the predictability of time-varying VAR and DSGE models

Over the last few years, there has been a growing interest in DSGE modelling for predicting macroeconomic fluctuations and conducting quantitative policy analysis. Hybrid DSGE models have become popular for dealing with some of the DSGE misspecifications as they are able to solve the trade-off between theoretical coherence and empirical fit. However, these models are still linear and they do not consider time variation for parameters. The time-varying properties in VAR or DSGE models capture the inherent nonlinearities and the adaptive underlying structure of the economy in a robust manner. In this article, we present a state-space time-varying parameter VAR model. Moreover, we focus on the DSGE–VAR that combines a microfounded DSGE model with the flexibility of a VAR framework. All the aforementioned models as well simple DSGEs and Bayesian VARs are used in a comparative investigation of their out-of-sample predictive performance regarding the US economy. The results indicate that while in general the classical VAR and BVARs provide with good forecasting results, in many cases the TVP–VAR and the DSGE–VAR outperform the other models.European Commission - Seventh Framework Programme (FP7)Marie Curie Intra European Fellowship'Dote ricercatoriî: FSE, Regione Lombardia