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Pinsky, B.

Publications and source records attributed to Pinsky, B..

2 recordsLinked to original sources

Despite egg-adaptive mutations, the 2012-13 H3N2 influenza vaccine induced comparable antibody titers to the intended strain

BackgroundInfluenza vaccination aims to prevent infection by influenza virus and reduce associated morbidity and mortality; however, vaccine effectiveness (VE) can be modest, especially for subtype A/H3N2. Failure to achieve consistently high VE has been attributed both to mismatches between the vaccine and circulating influenza strains and to the vaccine's elicitation of protective immunity in only a subset of the population. The low H3N2 VE in 2012-13 was attributed to egg-adaptive mutations that created antigenic mismatch between the intended (A/Victoria/361/2011) and actual vaccine strain (IVR-165).\n\nMethodsWe investigate the basis of the low VE in 2012-2013 by evaluating whether vaccinated and unvaccinated individuals were infected by different viral strains and assessing the serologic responses to A/Victoria/361/2011 and the IVR-165 vaccine strain in an adult cohort before and after vaccination.\n\nResultsWe found no significant genetic differences between the strains that infected vaccinated and unvaccinated individuals. Vaccination increased titers to A/Victoria/361/2011 as much as to IVR-165. These results are consistent with the hypothesis that vaccination served merely to boost preexisting cross-reactive immune responses, which provided limited protection against infection with the circulating influenza strains.\n\nConclusionsIn contrast to suggestive analyses based on ferret antisera, low H3N2 VE in 2012-13 does not appear to be due to the failure of the egg-adapted strain to induce a response to the intended vaccine strain. Instead, low VE might have been caused by the emergence of anti-genically novel influenza strains and low vaccine immunogenicity in a subset of the population.

epidemiology

Metagenomic DNA sequencing for the diagnosis of intraocular infections

PurposeTo compare the performance of unbiased high-throughput sequencing with pathogen directed PCR using DNA isolated from archived ocular fluid, approaches that are compatible with the current sample handling practice of ophthalmologists.\n\nDesignRetrospective molecular study of banked vitreous samples.\n\nMethodsWe evaluated a metagenomic DNA sequencing-based approach (DNA-seq) using archived positive (n = 31) and negative (n=36) vitreous specimens as determined by reference pathogen-specific PCR assays (herpes simplex virus 1 and 2, cytomegalovirus, varicella-zoster virus, and Toxoplasma gondii). Pathogens were identified using a rapid computational pipeline to analyze the non-host sequences obtained from DNA-seq. Clinical samples were de-identified and laboratory personnel handling the samples and interpreting the data were masked.\n\nResultsMetagenomic DNA sequencing detected 87% of positive reference samples. In the presumed negative reference samples, DNA-seq detected an additional 6 different pathogens in 8 samples (22% of negative samples) that were either not detected or not targeted with pathogen-specific PCR assays. Infectious agents identified only with DNA-seq were Candida dubliniensis, Klebsiella pneumoniae, human herpesvirus 6 (HHV-6), and human T-cell leukemia virus type 1 (HTLV-1). Discordant samples were independently verified in CLIA-certified laboratories. CMV sequences were compared against the antiviral mutation database and 3 of the samples were found to have mutations conferring ganciclovir resistance.\n\nConclusionsMetagenomic DNA sequencing was highly concordant with pathogen-directed PCRs. The unbiased nature of metagenomics DNA sequencing allowed an expanded scope of pathogen detection, including bacteria, fungal species, and viruses, resolving 22% of cases that had previously escaped detection by routine pathogen-specific PCRs. The detection of drug resistance mutations highlights the potential for unbiased sequencing to provide clinically actionable information beyond pathogen species detection.

genomics