Determinants of self-reported smoking and misclassification during pregnancy, and analysis of optimal cut-off points for urinary cotinine: a cross-sectional study

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Malaria Vaccine Adjuvants: Latest Update and Challenges in Preclinical and Clinical Research

There is no malaria vaccine currently available, and the most advanced candidate has recently reported a modest 30% efficacy against clinical malaria. Although many efforts have been dedicated to achieve this goal, the research was mainly directed to identify antigenic targets. Nevertheless, the latest progresses on understanding how immune system works and the data recovered from vaccination studies have conferred to the vaccine formulation its deserved relevance. Additionally to the antigen nature, the manner in which it is presented (delivery adjuvants) as well as the immunostimulatory effect of the formulation components (immunostimulants) modulates the immune response elicited. Protective immunity against malaria requires the induction of humoral, antibody-dependent cellular inhibition (ADCI) and effector and memory cell responses. This review summarizes the status of adjuvants that have been or are being employed in the malaria vaccine development, focusing on the pharmaceutical and immunological aspects, as well as on their immunization outcomings at clinical and preclinical stages.

On the optimal usage of labelled examples in semi-supervised multi-class classification problems

In recent years, the performance of semi-supervised learning has been theoretically investigated. However, most of this theoretical development has focussed on binary classification problems. In this paper, we take it a step further by extending the work of Castelli and Cover [1] [2] to the multi-class paradigm. Particularly, we consider the key problem in semi-supervised learning of classifying an unseen instance x into one of K different classes, using a training dataset sampled from a mixture density distribution and composed of l labelled records and u unlabelled examples. Even under the assumption of identifiability of the mixture and having infinite unlabelled examples, labelled records are needed to determine the K decision regions. Therefore, in this paper, we first investigate the minimum number of labelled examples needed to accomplish that task. Then, we propose an optimal multi-class learning algorithm which is a generalisation of the optimal procedure proposed in the literature for binary problems. Finally, we make use of this generalisation to study the probability of error when the binary class constraint is relaxed.

Progressive Decrease in the Potential Usefulness of Meningococcal Serogroup B Vaccine (4CMenB, Bexsero (R)) in Gipuzkoa, Northern Spain

The effectiveness of a vaccine is determined not only by the immunogenicity of its components, but especially by how widely it covers the disease-causing strains circulating in a given region. Because vaccine coverage varies over time, this study aimed to detect possible changes that could affect vaccine protection during a specific period in a southern European region. The 4CMenB vaccine is licensed for use in Europe, Canada, and Australia and is mainly directed against Neisseria meningitidis serogroup B. This vaccine contains four main immunogenic components: three recombinant proteins, FHbp, Nhba and NadA, and an outer membrane vesicle [PorA P1.4]. The allelic distribution of FHbp, Nhba, NadA, and PorA antigens in 82 invasive isolates (B and non-B serogroups) isolated from January 2008 to December 2013 were analyzed. 4CMenB was likely protective against 61.8% and 50% of serogroup B and non-B meningococci, respectively, in the entire period, but between 2012 and 2013, the predicted protection fell below 45% (42.1% for serogroup B isolates). The observed decreasing trend in the predicted protection during the 6 years of the study (X-2 for trend = 4.68, p=0.03) coincided with a progressive decrease of several clonal complexes (e. g., cc11, cc32 and cc41/44), which had one or more antigens against which the vaccine would offer protection.

Distribution of Healthcare resources II.Study and Comparison of a Linear Programming Problem and its Dual. Resolution and Evaluation of a Practical Case and its Programming using Excel and Win QSB.

[EN]This research had as primary objective to model different types of problems using linear programming and apply different methods so as to find an adequate solution to them. To achieve this objective, a linear programming problem and its dual were studied and compared. For that, linear programming techniques were provided and an introduction of the duality theory was given, analyzing the dual problem and the duality theorems. Then, a general economic interpretation was given and different optimal dual variables like shadow prices were studied through the next practical case: An aesthetic surgery hospital wanted to organize its monthly waiting list of four types of surgeries to maximize its daily income. To solve this practical case, we modelled the linear programming problem following the relationships between the primal problem and its dual. Additionally, we solved the dual problem graphically, and then we found the optimal solution of the practical case posed through its dual, following the different theorems of the duality theory. Moreover, how Complementary Slackness can help to solve linear programming problems was studied. To facilitate the solution Solver application of Excel and Win QSB programme were used.