Effect of vaccination against pneumococcal infection and influenza in children with asthma

Assessment of clinical course of asthma and IgG response in children with asthma immunized with pneumococcal polysaccharide vaccine (Pneumo 23) and influenza vaccine (Vaxigrip). 78 children aged 4 - 17 years old were allocated to two groups. Children from the 1st group were immunized against pneumococcal infection and influenza; children from 2nd group were immunized against pneumococcal infection only. Rate of asthma exacerbations in the 1st group of children decreased by 1.7 times compared with the period before vaccination, whereas the same rate in the 2nd group of children decreased by 1.5 times. It was accompanied by the increase of IgG level to antigens of pneumococcalvaccine in blood, which was observed in both groups. Vaccination did not result in increase of IgE levels. Immunization of children with asthma against pneumococcal infection with polysaccharide vaccine or combined immunization against pneumococcal infection and influenza reduced rate of asthma exacerbations and led to formation of immunity to vaccine strains of Streptococcus pneumoniae. Vaccination did not lead to sensitization of children.     

Adsorbed influenza chemical vaccine in the immunization of schoolchildren

When used for the immunization of healthy children aged 7-14 year, adsorbed chemical influenza vaccine produced at the Mechnikov Research Institute for Vaccines and Sera in Ufa has proved to be safe, faintly reactogenic and antigenically very potent. The optimum doses for immunizing children aged 7-10 and 11-14 years have been selected.     

A model for influenza with vaccination and antiviral treatment

Compartmental models for influenza that include control by vaccination and antiviral treatment are formulated. Analytic expressions for the basic reproduction number, control reproduction number and the final size of the epidemic are derived for this general class of disease transmission models. Sensitivity and uncertainty analyses of the dependence of the control reproduction number on the parameters of the model give a comparison of the various intervention strategies. Numerical computations of the deterministic models are compared with those of recent stochastic simulation influenza models. Predictions of the deterministic compartmental models are in general agreement with those of the stochastic simulation models.     

Envisioning future strategies for vaccination against tuberculosis

The design of tuberculosis vaccines has entered a new era. Although several new vaccine candidates will pass Phase I clinical trials within the next year, I believe that the most effective vaccination strategy will be to combine different vaccine candidates and to use a prime-boost approach. This strategy, however, would require several years of iterative vaccine trials, unless the process is expedited by the identification of reliable biomarkers for assessing vaccine efficacy. In this Essay, I briefly summarize past and present attempts to develop a vaccine against tuberculosis, and I describe, using imagined scenarios, the tuberculosis vaccination schemes that might become available from a large repertoire of candidate schemes in the near and distant future.     

Finding optimal vaccination strategies for pandemic influenza using genetic algorithms

In the event of pandemic influenza, only limited supplies of vaccine may be available. We use stochastic epidemic simulations, genetic algorithms (GA), and random mutation hill climbing (RMHC) to find optimal vaccine distributions to minimize the number of illnesses or deaths in the population, given limited quantities of vaccine. Due to the non-linearity, complexity and stochasticity of the epidemic process, it is not possible to solve for optimal vaccine distributions mathematically. However, we use GA and RMHC to find near optimal vaccine distributions. We model an influenza pandemic that has age-specific illness attack rates similar to the Asian pandemic in 1957-1958 caused by influenza A(H2N2), as well as a distribution similar to the Hong Kong pandemic in 1968-1969 caused by influenza A(H3N2). We find the optimal vaccine distributions given that the number of doses is limited over the range of 10-90% of the population. While GA and RMHC work well in finding optimal vaccine distributions, GA is significantly more efficient than RMHC. We show that the optimal vaccine distribution found by GA and RMHC is up to 84% more effective than random mass vaccination in the mid range of vaccine availability. GA is generalizable to the optimization of stochastic model parameters for other infectious diseases and population structures.     

HLA class II-restricted CD4+ T cell responses directed against influenza viral antigens postinfluenza vaccination

The memory T cell response is polyclonal, with the magnitude and specificity of the response controlled in part by the burst size of T cells expanded from effector/memory precursors. Sensitive assays using HLA class II multimers were used to detect low-frequency Ag-specific T cells directed against influenza viral Ags in subjects immunized with the influenza vaccine. Direct ex vivo tetramer staining of PBMC from five individuals identified frequencies of hemagglutinin (HA) 306-318 tetramer binding CD4(+) T cells in the peripheral blood ranging from 1 in 600 to 1 in 30,000 CD4(+) T cells. These frequencies were validated by counting CFSE(low), tetramer-positive T cells after in vitro expansion. Low frequency of T cells directed to other influenza epitopes, including DRA1*0101/DRB1*0401-restricted matrix protein 60-73, DRA1*0101/DRB1*0101-restricted matrix protein 18-29, DRA1*0101/DRB1*0701-restricted HA 232-244 and DRA1*0101/DRB1*0101-restricted nucleoprotein 206-217 were also determined. T cells which occurred at a frequency as low as 1 in 350,000 could be ascertained by in vitro expansion of precursors. Peripheral HA(306-318)-responsive T cells expanded 2- to 5-fold following influenza vaccination. Examination of phenotypic markers of the HA(306-318)-responsive T cells in the peripheral blood indicated that the majority were CD45RA(-), CD27(+), CD25(-), CD28(+), and CD62L(-), while T cell clones derived from this population were CD45RA(-), CD27(-), CD25(+), CD28(+), and CD62L(-).