Liver Progenitor Cell Line HepaRG Differentiated in a Bioartificial Liver Effectively Supplies Liver Support to Rats with Acute Liver Failure

A major roadblock to the application of bioartificial livers is the need for a human liver cell line that displays a high and broad level of hepatic functionality. The human bipotent liver progenitor cell line HepaRG is a promising candidate in this respect, for its potential to differentiate into hepatocytes and bile duct cells. Metabolism and synthesis of HepaRG monolayer cultures is relatively high and their drug metabolism can be enhanced upon treatment with 2% dimethyl sulfoxide (DMSO). However, their potential for bioartificial liver application has not been assessed so far. Therefore, HepaRG cells were cultured in the Academic Medical Center bioartificial liver (AMC-BAL) with and without DMSO and assessed for their hepatic functionality in vitro and in a rat model of acute liver failure. HepaRG-AMC-BALs cultured without DMSO eliminated ammonia and lactate, and produced apolipoprotein A-1 at rates comparable to freshly isolated hepatocytes. Cytochrome P450 3A4 transcript levels and activity were high with 88% and 37%, respectively, of the level of hepatocytes. DMSO treatment of HepaRG-AMC-BALs reduced the cell population and the abovementioned functions drastically. Therefore, solely HepaRG-AMC-BALs cultured without DMSO were tested for efficacy in rats with acute liver failure (n = 6). HepaRG-AMC-BAL treatment increased survival time of acute liver failure rats ∼50% compared to acellular-BAL treatment. Moreover, HepaRG-AMC-BAL treatment decreased the progression of hepatic encephalopathy, kidney failure, and ammonia accumulation. These results demonstrate that the HepaRG-AMC-BAL is a promising bioartificial liver for clinical application

Impact of H1N1 on Socially Disadvantaged Populations: Systematic Review

The burden of H1N1 among socially disadvantaged populations is unclear. We aimed to synthesize hospitalization, severe illness, and mortality data associated with pandemic A/H1N1/2009 among socially disadvantaged populations.Studies were identified through searching MEDLINE, EMBASE, scanning reference lists, and contacting experts. Studies reporting hospitalization, severe illness, and mortality attributable to laboratory-confirmed 2009 H1N1 pandemic among socially disadvantaged populations (e.g., ethnic minorities, low-income or lower-middle-income economy countries [LIC/LMIC]) were included. Two independent reviewers conducted screening, data abstraction, and quality appraisal (Newcastle Ottawa Scale). Random effects meta-analysis was conducted using SAS and Review Manager.Sixty-two studies including 44,777 patients were included after screening 787 citations and 164 full-text articles. The prevalence of hospitalization for H1N1 ranged from 17-87% in high-income economy countries (HIC) and 11-45% in LIC/LMIC. Of those hospitalized, the prevalence of intensive care unit (ICU) admission and mortality was 6-76% and 1-25% in HIC; and 30% and 8-15%, in LIC/LMIC, respectively. There were significantly more hospitalizations among ethnic minorities versus non-ethnic minorities in two studies conducted in North America (1,313 patients, OR 2.26 [95% CI: 1.53-3.32]). There were no differences in ICU admissions (n = 8 studies, 15,352 patients, OR 0.84 [0.69-1.02]) or deaths (n = 6 studies, 14,757 patients, OR 0.85 [95% CI: 0.73-1.01]) among hospitalized patients in HIC. Sub-group analysis indicated that the meta-analysis results were not likely affected by confounding. Overall, the prevalence of hospitalization, severe illness, and mortality due to H1N1 was high for ethnic minorities in HIC and individuals from LIC/LMIC. However, our results suggest that there were little differences in the proportion of hospitalization, severe illness, and mortality between ethnic minorities and non-ethnic minorities living in HIC

Real-time numerical forecast of global epidemic spreading: Case study of 2009 A/H1N1pdm

Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. Methods We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Results Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Conclusions Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models

Phase I clinical trial with the AMC-bioartificial liver.

Recently a bio-artificial liver (BAL) system has been developed at the Academic Medical Center (AMC) of Amsterdam to bridge patients with acute liver failure (ALF) to orthotopic liver transplantation (OLT). After successful testing of the AMC-BAL in rodents and pigs with ALF, a phase I study in ALF patients waiting for (OLT) was started in Italy. We present the safety outcome of the first 7 patients aged 21-56 years with coma grade III or IV The total AMC-BAL treatment time ranged from 8 to 35 hours. Three patients received 2 treatments with two different BAL's within three days. Six of the 7 patients were successfully bridged to OLT. One patient showed improved liver function after two treatments and did not need OLT. No severe adverse events of the BAL treatment were noted. CONCLUSION: Treatment of ALF patients with the AMC-BAL is a safe and feasible technique to bridge the waiting time for an adequate liver-graft