Background: Hepatitis B virus (HBV) genotypes distribution varies worldwide. Genotype determination is important in virus epidemiology and to predict progress of disease. Certain genotypes are reported to be associated with more severe disease and more frequent development of hepatocellular carcinoma. More data is required on the local HBV genotypes. This study aimed to determine the HBV genotypes among chronic hepatitis B patients who were on treatment in Universiti Kebangsaan Malaysia Medical Centre (UKMMC), and the association with demographics, laboratory and clinical parameters.Methods and materials: Cross-sectional study done at the Molecular Biology Unit, Department of Laboratory Diagnostics, UKMMC from January 2016 to March 2019. Plasma samples (n = 1760) received for HBV viral load were screened for volume adequacy and viral load of at least 1000 IU/mL (n = 61). Records were traced to include patients who were on treatment, no coinfection and no hepatocellular carcinoma. Real-time polymerase chain reaction with two-step melting curve analysis was done. Genotypes were determined for 50 samples. From the remaining 11 samples which the genotype could not be determined, 3 samples with good amplification curves were then sequenced, using the platform of ABI 3730 XL (Applied Biosystems). Data on patient demographics, viral load, alanine transaminase (ALT), HBeAg, anti-HBs, alpha-fetoprotein and cirrhosis were recorded. The association between genotypes and the parameters were then analysed.Results: A total of 53 samples were successfully genotyped while eight samples were non-typable (13.0%). Thirty-three were identified as genotype B (54.1%), 14 were genotype C (23.0%), 5 were genotype A (8.2%) and 1 was genotype D (1.6%). Thirty-seven patients were male (60.7%) and majority of patients were between age 40 and 64 (49.2%). Fifty patients were non-cirrhotic (82.0%), while nine were cirrhotic (14.8%). Of these, 2 were genotype B, 2 were genotype C and 1 was genotype A. ALT were within normal limits for 42 patients (68.9%). Anti-HBs were reactive in 2 patients (genotype B and genotype C each). There was no statistically significant association found between genotypes and patient demographics, viral load, ALT, HBeAg, anti-HBs, alpha-fetoprotein and cirrhosis.Conclusion: Genotype B is the most predominant genotype among the chronic hepatitis B patients who were on treatment, followed by genotype C.
Serum is commonly used as a specimen in immunoassays but the presence of heterophilic antibodies can potentially interfere with the test results. Previously, we have developed a microfluidic device called: 3D Stack for enzyme-linked immunosorbent assay (ELISA). However, its evaluation was limited to detection from a single protein solution. Here, we investigated the sensitivity of the 3D Stack in detecting a severe dengue biomarker—soluble CD163 (sCD163)—within the serum matrix. To determine potential interactions with serum matrix, a spike-and-recovery assay was performed, using 3D Stacks with and without surface modification by an EDC–NHS (N-ethyl-N′-(3-(dimethylamino)propyl)carbodiimide/N-hydroxysuccinimide) coupling. Without surface modification, a reduced analyte recovery in proportion to serum concentration was observed because of the Vroman effect, which resulted in competitive displacement of coated capture antibodies by serum proteins with stronger binding affinities. However, EDC–NHS coupling prevented antibody desorption and improved the sensitivity. Subsequent comparison of sCD163 detection using a 3D Stack with EDC–NHS coupling and conventional ELISA in dengue patients’ sera revealed a high correlation (R = 0.9298, p < 0.0001) between the two detection platforms. Bland–Altman analysis further revealed insignificant systematic error between the mean differences of the two methods. These data suggest the potentials of the 3D Stack for further development as a detection platform.
Background: Influenza is intrinsically characterized by the unpredictable predominance of one or co-circulation of several influenza A subtypes and/or influenza type B lineages in any influenza season. The past decades two antigenically distinct lineages of influenza B viruses have circulated globally since 1985 and have co-circulated since 2001, lineage Victoria and lineage Yamagata.Our aim was to estimate the distribution of the two lineages circulating in Greece and any possible mismatching with vaccine influenza B strains.Methods and materials: We studied 490 laboratory-confirmed influenza B non-severe acute respiratory infection (non-SARI) clinical cases diagnosed in the two National Influenza Reference Laboratories by reverse trancscriptase-PCR from 1/7/2005 to 30/6/2015 and 100 influenza B SARI cases diagnosed from 1/7/2011 to 30/6/2015. The distribution of influenza B lineages Victoria and Yamagata was described and mismatch between circulating lineages and vaccine strains was estimated.Results: Median matching between the circulating influenza B lineages and the vaccine influenza B strains was 19.2% (range: 0-100%) for non-SARI cases during 2005-2015 and 67.6% (range: 41.2-94.1%) for SARI cases during 2011-2015. In two influenza seasons (2005-2006 and 2006-2007) complete mismatch between influenza B non-SARI cases and influenza B vaccine strains was found. We estimated that 5, 12 or 16 laboratory-confirmed SARI cases could have been prevented by quadrivalent influenza vaccine during the 2011-2012 season and 1, 3, or 4 SARI cases during the 2014-2015 season, with a vaccination coverage rate of 70% and a vaccine effectiveness of 20%, 50% or 70%, respectively.Conclusion: Significant co-circulation of lineage Victoria and Yamagata influenza B strains and mismatching with vaccine influenza B strains was found during 2005-2015 in Greece. The wide use of a quadrivalent influenza vaccine instead of a trivalent influenza vaccine will confer additional immunity and therefore protection against influenza B and it is expected to prevent several SARI cases annually. Our findings strongly support the recommendations for using quadrivalent influenza vaccine.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.