Population-based incidence of intussusception and a case-control study to examine the association of intussusception with natural rotavirus infection among indian children.

BACKGROUND: A rotavirus vaccine previously licensed in the United States was withdrawn because it caused intussusception. Data on background intussusception rates in developing countries are required to plan pre- and postlicensure safety studies for new rotavirus vaccines. Also, it is unclear whether natural rotavirus infection is associated with intussusception. METHODS: Passive surveillance for intussusception in a large, well-defined, poor, urban population in Delhi, India, was conducted in 2 phases. Intussusception was confirmed by ultrasonography or surgery. Fecal samples obtained from patients with intussusception at study hospitals (irrespective of their residence in study areas) and healthy control subjects were tested for rotavirus with use of enzyme immunoassay. If available, resected intestinal tissue samples were tested for rotavirus with use of immunohistochemistical analysis and reverse-transcription polymerase chain reaction. RESULTS: The incidence of intussusception requiring hospitalization was 17.7 cases per 100,000 infant-years of follow-up (95% confidence interval, 5.9-41.4 cases per 100,000 infant-years). Detection rates of rotavirus in stool samples did not differ significantly between case patients and control subjects (4 of 42 case patients vs 6 of 92 control subjects), and no evidence of rotavirus was detected in any of the 22 patients with intussusception for whom intestinal tissue samples were available. CONCLUSIONS: The incidence of intussusception among Indian infants appears to be lower than that reported in other middle- and high-income countries. Natural rotavirus infection does not appear to be a major cause of intussusception in Indian infants.

9th GCC closed forum: CAPA in regulated bioanalysis; method robustness, biosimilars, preclinical method validation, endogenous biomarkers, whole blood stability, regulatory audit experiences and electronic laboratory notebooks.

The 9th GCCClosed Forum was held just prior to the 2015 Workshop on Recent Issues in Bioanalysis (WRIB) in Miami, FL, USA on 13 April 2015. In attendance were 58 senior-level participants, from eight countries, representing 38 CRO companies offering bioanalytical services. The objective of this meeting was for CRO bioanalytical representatives to meet and discuss scientific and regulatory issues specific to bioanalysis. The issues selected at this year's closed forum include CAPA, biosimilars, preclinical method validation, endogenous biomarkers, whole blood stability, and ELNs. A summary of the industry's best practices and the conclusions from the discussion of these topics is included in this meeting report.

Calculations for Adjusting Endogenous Biomarker Levels During Analytical Recovery Assessments for Ligand-Binding Assay Bioanalytical Method Validation.

It is often necessary to adjust for detectable endogenous biomarker levels in spiked validation samples (VS) and in selectivity determinations during bioanalytical method validation for ligand-binding assays (LBA) with a matrix like normal human serum (NHS). Described herein are case studies of biomarker analyses using multiplex LBA which highlight the challenges associated with such adjustments when calculating percent analytical recovery (%AR). The LBA test methods were the Meso Scale Discovery V-PLEX® proinflammatory and cytokine panels with NHS as test matrix. The NHS matrix blank exhibited varied endogenous content of the 20 individual cytokines before spiking, ranging from undetectable to readily quantifiable. Addition and subtraction methods for adjusting endogenous cytokine levels in %AR calculations are both used in the bioanalytical field. The two methods were compared in %AR calculations following spiking and analysis of VS for cytokines having detectable endogenous levels in NHS. Calculations for %AR obtained by subtracting quantifiable endogenous biomarker concentrations from the respective total analytical VS values yielded reproducible and credible conclusions. The addition method, in contrast, yielded %AR conclusions that were frequently unreliable and discordant with values obtained with the subtraction adjustment method. It is shown that subtraction of assay signal attributable to matrix is a feasible alternative when endogenous biomarkers levels are below the limit of quantitation, but above the limit of detection. These analyses confirm that the subtraction method is preferable over that using addition to adjust for detectable endogenous biomarker levels when calculating %AR for biomarker LBA.

Incidence of severe rotavirus diarrhea in New Delhi, India, and G and P types of the infecting rotavirus strains.

A total of 62,475 children <5 years old from a defined population of approximately 500,000 children and adults from slums in New Delhi, India, were assessed for 1 year by means of passive surveillance, to identify children who were hospitalized for diarrhea. The incidence of severe rotavirus diarrhea was estimated, and the G and P types of the infecting rotavirus strains were determined and were correlated with the clinical severity of diarrhea. Of 584 children who were hospitalized with diarrhea, 137 (23.5%) had rotavirus detected in stool specimens (incidence of rotavirus diarrhea-associated hospitalizations, 337 hospitalizations/100,000 children <5 years of age). Most cases of diarrhea (98%) occurred during the first 2 years of life, peaking at 9-11 months of age. Rotavirus-associated diarrhea occurred year-round but was predominant in winter. Among the strains that could be G-typed, G1 was the most common serotype, followed by G9 and G2; 10% of cases of diarrhea were due to mixed G-type infections. Common strains identified in the present surveillance study were P[8]G1, P[4]G2, P[8]G9, P[6]G1, P[6]G9, and P[6]G3. Children infected with G1 strains had a greater risk of developing more-severe cases of diarrhea than did children infected with other rotavirus strains (odds ratio, 2.95; 95% confidence interval, 1.3-6.67).