Safety of anti-TNF agents in patients with compensated cirrhosis: a case-control study

Background: There is limited data on the use of anti-TNF agents in patients with concomitant cirrhosis. The aim of this study is to assess the safety of anti-TNF agents in patients with compensated cirrhosis who used these medications for the treatment of an underlying rheumatologic condition or IBD. Methods: Multicenter, retrospective, matched, case-control study. A one to three case-control match was performed. Adults who received anti-TNF therapy were matched to three adults with cirrhosis who did not receive anti-TNF therapy. Patients were matched for etiology of cirrhosis, MELD-Na and age. Primary outcome was the development of hepatic decompensation. Secondary outcomes included development of infectious complications, hepatocellular carcinoma (HCC), extra-hepatic malignancy, and mortality. Results: Eighty patients with cirrhosis who received anti-TNF agents were matched with 240 controls. Median age was 57.2 years. Median MELD-Na for the anti-TNF cohort was seven and median MELD-Na for the controls was eight. The most common etiology of cirrhosis was NAFLD. Anti-TNF therapy did not increase risk of decompensation (HR: 0.91, 95% CI: 0.64–1.30, p = 0.61) nor influence the time to development of a decompensating event. Anti-TNF therapy did not increase the risk of hepatic mortality or need for liver transplantation (HR: 1.18, 95% CI: 0.55–2.53, p = 0.67). Anti-TNF therapy was not associated with an increased risk of serious infection (HR: 1.21, 95% CI: 0.68–2.17, p = 0.52), HCC (OR: 0.45, 95% CI: 0.13–1.57, p = 0.21), or extra-hepatic malignancy (OR: 0.82, 95% CI: 0.29–2.30, p = 0.71). Conclusions: Anti-TNF agents in patients with compensated cirrhosis does not influence the risk of decompensation, serious infections, transplant free survival, or malignancy

TMNT: Dynamic Models of Cancer and HIV

Differential equations are used to build dynamic mathematical models for systems and nonlinear phenomena, which dynamically change with time. Ordinary differential equations describe a relation that contains functions of only one independent variable, and one or more of their derivatives with respect to that variable. Applications of those models are found in biological systems. In one study, homogeneous mathematical models are used to describe the interactions between cancerous cells and the immune system. Modeling using differential equations will allow better understanding of the behavior and spreading of those malignant cells. The models will investigate the dynamics of populations of cancer cells, the mechanism of immune surveillance, whereby the immune system identifies and kills foreign cells, the interactions between cancer cells, immune cells, and other type of cells or signaling proteins and the interacting components of the tumor microenvironment. These mathematical models of differential equations will provide a simpler framework within which to explore the interactions among tumor cells and the different types of immune and healthy tissue cells. Another application of models is in HIV dynamics, which have aided significantly in AIDS research. Deterministic dynamic models are used to study the viral dynamic process for understanding the pathogenesis of HIV Type 1 infections as well as antiviral treatment strategies. This study estimates the parameters of a long-term HIV dynamic model containing constant and time varying parameters by using HIV viral load and CD4 + T cell counts