Outbreak of acute hepatitis C following the use of anti-hepatitis C virus--screened intravenous immunoglobulin therapy

BACKGROUND and AIMS: Hepatitis C virus (HCV) infection has been associated with intravenous (IV) immunoglobulin (Ig), and plasma donations used to prepare IV Ig are now screened to prevent transmission. Thirty-six patients from the United Kingdom received infusions from a batch of anti-HCV antibody-screened intravenous Ig (Gammagard; Baxter Healthcare Ltd., Thetford, Norfolk, England) that was associated with reports of acute hepatitis C outbreak in Europe. The aim of this study was to document the epidemiology of this outbreak. METHODS: Forty-six patients from the United Kingdom treated with Gammagard (34 exposed and 12 unexposed to the batch) returned epidemiological questionnaires. RESULTS: Eighty-two percent of the exposed patients (28 of 34) became positive for HCV RNA. Eighteen percent of the patients (6 of 34) who had infusions with this batch tested negative for HCV RNA, but 2 of the patients had abnormal liver function and subsequently seroconverted to anti-HCV antibody positive. Twenty-seven percent of the patients (9 of 34) developed jaundice, and 79% (27 of 34) had abnormal liver transferase levels. Virus isolates (n=21), including an isolate from the implicated batch, were genotype 1a and virtually identical by sequence analysis of the NS5 region, consistent with transmission from a single source. CONCLUSIONS: Hepatitis C infection can be transmitted by anti-HCV-screened IV Ig. Careful documentation of IV Ig batch numbers and regular biochemical monitoring is recommended for all IV Ig recipients

Mapping of serotype-specific, immunodominant epitopes in the NS-4 region of hepatitis C virus (HCV):use of type-specific peptides to serologically differentiate infections with HCV types 1, 2, and 3

The effect of sequence variability between different types of hepatitis C virus (HCV) on the antigenicity of the NS-4 protein was investigated by epitope mapping and by enzyme-linked immunosorbent assay with branched oligopeptides. Epitope mapping of the region between amino acid residues 1679 and 1768 in the HCV polyprotein revealed two major antigenic regions (1961 to 1708 and 1710 to 1728) that were recognized by antibody elicited upon natural infection of HCV. The antigenic regions were highly variable between variants of HCV, with only 50 to 60% amino acid sequence similarity between types 1, 2, and 3. Although limited serological cross-reactivity between HCV types was detected between peptides, particularly in the first antigenic region of NS-4, type-specific reactivity formed the principal component of the natural humoral immune response to NS-4. Type-specific antibody to particular HCV types was detected in 89% of the samples from anti-HCV-positive blood donors and correlated almost exactly with genotypic analysis of HCV sequences amplified from the samples by polymerase chain reaction. Whereas almost all blood donors appeared to be infected with a single virus type (97%), a higher proportion of samples (40%) from hemophiliacs infected from transfusion of non-heat-inactivated clotting factor contained antibody to two or even all three HCV types, providing evidence that long-term exposure may lead to multiple infection with different variants of HCV

Artificial Immune System based on Hybrid and External Memory for Mathematical Function Optimization

Artificial immune system (AIS) is one of the natureinspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be further improved because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. Thus, a hybrid PSO-AIS and a new external memory CSA based scheme called EMCSA are proposed. In hybrid PSO-AIS, the good features of PSO and AIS are combined in order to reduce any limitation. Alternatively, EMCSA captures all the best antibodies into the memory in order to enhance global searching capability. In this preliminary study, the results show that the performance of hybrid PSO-AIS compares favourably with other algorithms while EMCSA produced moderate results in most of the simulations

Artificial Immune System Based Remainder Method for Multimodal Mathematical Function Optimization

Artificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. However, the CSA rate of convergence and accuracy can be further improved as the hypermutation in CSA itself cannot always guarantee a better solution. Conversely, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have an inclination to converge prematurely. In this work, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. Simulation results show that the proposed algorithm is able to enhance the performance of the conventional CSA in terms of accuracy and stability for single objective functions

Antibody Remainder Method Based Artificial Immune System for Mathematical Function Optimization

Artificial immune system (AIS) is one of the natureinspired algorithm for solving optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively,Genetic Algorithms (GAs) and Particle Swarm Optimization(PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solution for each exposure (iteration) namely Single Best Remainder (SBR) CSA. In this study, the results show that the performance of the proposed algorithm (SBR-CSA) compares favourably with other algorithms while Half Best Insertion (HBI) CSA produced moderate results in most of the simulations

Effectiveness and tolerability of pegylated interferon alfa-2b in combination with ribavirin for treatment of chronic hepatitis C: the PegIntrust Study

Background and study aims : Large international clinical trials conducted in the past 5 years rapidly improved the treatment of chronic hepatitis C; however, it is unclear whether the advances seen in clinical trials are being paralleled by similar improvements in routine clinical practice. PegIntrust is a Belgian community-based trial evaluating the sustained virological response. Patients and Methods : Observational study of 219 patients receiving pegylated interferon alfa-2b (1.5 mu g/kg/wk) and weight. based ribavirin (800-1200 mg/day) for 48 weeks. Primary study end point was sustained virological response (SVR), defined as undetectable HCV RNA 6 months after the completion of treatment. Results : In total, 108 patients (49.3 %) had undetectable HCV RNA at the end of therapy, 91(41.6%) attaining SVR. Of the 111 patients without an end-of-treatment response, 28 were non-responders, and 21 had virological breakthrough. In total, 134 patients attained early virological response (EVR); 88 (65.7%) of those patients attained SVR. In contrast, 82 (96.5 %) of the 85 patients who did not attain EVR also did not attain SVR. Age, fibrosis score and baseline viral load were identified as important predictors of treatment outcome. The most frequently reported serious adverse events resulting in treatment discontinuation were anemia (n = 10), fatigue/asthenia/malaise (n = 6) and fever (n = 3). Conclusion : Our data indicate that treatment of chronic hepatitis C with PEG-IFN alfa-2b plus weight-based ribavirin results in favourable treatment outcomes in a Belgian cohort of patients treated in community-based clinical practice. (Ada gastroenterol. belg., 2010, 73, 5-11)

BMI and Diabetes Risk in Singaporean Chinese

10.2337/dc08-1674Diabetes Care3261104-1106DICA

Grayscale Medical Image Compression Using Feedforward Neural Networks

In this paper, feedforward neural network train with backpropagation algorithm is propose to compress grayscale medical images. In this new method, a three hidden layer feedforward network (FFN) is applied directly as the main compression algorithm to compress an MRI image. After training with sufficient sample images, the compression process will be carried out on the target image. The coupling weights and activation values of each neuron in the hidden layer will be stored after training. Compression is then achieved by using smaller number of hidden neurons as compared to the number of image pixels due to lesser information being stored. Experimental results show that the FFN is able to achieve comparable compression ratio of 1:36 at PSNR 35.89 dB as compared to JPEG2000 with compression ratio of 1:20 at PSNR 40 dB.