Effects of the Share 35 Rule on Waitlist and Liver Transplantation Outcomes for Patients with Hepatocellular Carcinoma.

Several studies have investigated the effects following the implementation of the "Share 35" policy; however none have investigated what effect this policy change has had on waitlist and liver transplantation (LT) outcomes for hepatocellular carcinoma(HCC).Data were obtained from the UNOS database and a comparison of the 2 years post-Share 35 with data from the 2 years pre-Share 35 was performed.In the pre-Share35 era, 23% of LT were performed for HCC exceptions compared to 22% of LT in the post-Share35 era (p = 0.21). No difference in wait-time for HCC patients was seen in any of the UNOS regions between the 2 eras. Competing risk analysis demonstrated that HCC candidates in post-Share 35 era were more likely to die or be delisted for "too sick" while waiting (7.2% vs. 5.3%; p = 0.005) within 15 months. A higher proportion of ECD (p<0.001) and DCD (p<0.001) livers were used for patients transplanted for HCC, while lower DRI organs were used for those patients transplanted with a MELD≥35 between the 2 eras (p = 0.007).No significant change to wait-time for patients listed for HCC was seen following implementation of "Share 35". Transplant program behavior has changed resulting use of higher proportion of ECD and DCD liver grafts for patients with HCC. A higher rate of wait list mortality was observed in patients with HCC in the post-Share 35 era

Autoimmune Hepatitis: A Diagnostic and Therapeutic Overview

Autoimmune hepatitis is an immune-mediated inflammatory condition of the liver of undetermined cause that affects both sexes, all ages, races, and ethnicities. Its clinical presentation can be very broad, from having an asymptomatic and silent course to presenting as acute hepatitis, cirrhosis, and acute liver failure potentially requiring liver transplantation. The diagnosis is based on histological abnormalities (interface hepatitis), characteristic clinical and laboratory findings (increased aspartate aminotransferase, alanine aminotransferase, and serum IgG concentration), and the presence of one or more characteristic autoantibodies. The large heterogeneity of these clinical, biochemical, and histological findings can sometimes make a timely and proper diagnosis a difficult task. Treatment seeks to achieve remission of the disease and prevent further progression of liver disease. First-line therapy includes high-dose corticosteroids, which are later tapered to decrease side effects, and azathioprine. In the presence of azathioprine intolerance or a poor response to the standard of care, second-line therapy needs to be considered, including mycophenolate mofetil. AIH remains a diagnostic and therapeutic challenge, and a further understanding of the pathophysiological pathways of the disease and the implementation of randomized controlled trials are needed

Forecasting the power of Higher Order Weak Lensing Statistics with automatically differentiable simulations

International audienceWe present the Differentiable Lensing Lightcone (DLL), a fully differentiable physical model designed for being used as a forward model in Bayesian inference algorithms requiring access to derivatives of lensing observables with respect to cosmological parameters. We extend the public FlowPM N-body code, a particle-mesh N-body solver, simulating lensing lightcones and implementing the Born approximation in the Tensorflow framework. Furthermore, DLL is aimed at achieving high accuracy with low computational costs. As such, it integrates a novel Hybrid Physical-Neural parameterisation able to compensate for the small-scale approximations resulting from particle-mesh schemes for cosmological N-body simulations. We validate our simulations in an LSST setting against high-resolution $\kappa$TNG simulations by comparing both the lensing angular power spectrum and multiscale peak counts. We demonstrate an ability to recover lensing $C_\ell$ up to a 10% accuracy at $\ell=1000$ for sources at redshift 1, with as few as $\sim 0.6$ particles per Mpc/h. As a first use case, we use this tool to investigate the relative constraining power of the angular power spectrum and peak counts statistic in an LSST setting. Such comparisons are typically very costly as they require a large number of simulations, and do not scale well with the increasing number of cosmological parameters. As opposed to forecasts based on finite differences, these statistics can be analytically differentiated with respect to cosmology, or any systematics included in the simulations at the same computational cost of the forward simulation. We find that the peak counts outperform the power spectrum on the cold dark matter parameter $\Omega_c$, on the amplitude of density fluctuations $\sigma_8$, and on the amplitude of the intrinsic alignment signal $A_{IA}$

Forecasting the power of Higher Order Weak Lensing Statistics with automatically differentiable simulations

International audienceWe present the Differentiable Lensing Lightcone (DLL), a fully differentiable physical model designed for being used as a forward model in Bayesian inference algorithms requiring access to derivatives of lensing observables with respect to cosmological parameters. We extend the public FlowPM N-body code, a particle-mesh N-body solver, simulating lensing lightcones and implementing the Born approximation in the Tensorflow framework. Furthermore, DLL is aimed at achieving high accuracy with low computational costs. As such, it integrates a novel Hybrid Physical-Neural parameterisation able to compensate for the small-scale approximations resulting from particle-mesh schemes for cosmological N-body simulations. We validate our simulations in an LSST setting against high-resolution $\kappa$TNG simulations by comparing both the lensing angular power spectrum and multiscale peak counts. We demonstrate an ability to recover lensing $C_\ell$ up to a 10% accuracy at $\ell=1000$ for sources at redshift 1, with as few as $\sim 0.6$ particles per Mpc/h. As a first use case, we use this tool to investigate the relative constraining power of the angular power spectrum and peak counts statistic in an LSST setting. Such comparisons are typically very costly as they require a large number of simulations, and do not scale well with the increasing number of cosmological parameters. As opposed to forecasts based on finite differences, these statistics can be analytically differentiated with respect to cosmology, or any systematics included in the simulations at the same computational cost of the forward simulation. We find that the peak counts outperform the power spectrum on the cold dark matter parameter $\Omega_c$, on the amplitude of density fluctuations $\sigma_8$, and on the amplitude of the intrinsic alignment signal $A_{IA}$