In recent years, the within-host viral dynamics of dengue infections have been increasingly characterized, and the relationship between aspects of these dynamics and the manifestation of severe disease has been increasingly probed. Despite this progress, there are few mathematical models of within-host dengue dynamics, and the ones that exist focus primarily on the general role of immune cells in the clearance of infected cells, while neglecting other components of the immune response in limiting viraemia. Here, by considering a suite of mathematical within-host dengue models of increasing complexity, we aim to isolate the critical components of the innate and the adaptive immune response that suffice in the reproduction of several well-characterized features of primary and secondary dengue infections. By building up from a simple target cell limited model, we show that only the innate immune response is needed to recover the characteristic features of a primary symptomatic dengue infection, while a higher rate of viral infectivity (indicative of antibody-dependent enhancement) and infected cell clearance by T cells are further needed to recover the characteristic features of a secondary dengue infection. We show that these minimal models can reproduce the increased risk of disease associated with secondary heterologous infections that arises as a result of a cytokine storm, and, further, that they are consistent with virological indicators that predict the onset of severe disease, such as the magnitude of peak viraemia, time to peak viral load, and viral clearance rate. Finally, we show that the effectiveness of these virological indicators to predict the onset of severe disease depends on the contribution of T cells in fuelling the cytokine storm.

PurposeThe American College of Obstetricians and Gynecologists (ACOG) proposed seven criteria for expanded carrier screening (ECS) panel design. To ensure that screening for a condition is sufficiently sensitive to identify carriers and reduce residual risk of noncarriers, one criterion requires a per-condition carrier rate greater than 1 in 100. However, it is unestablished whether this threshold corresponds with a loss in clinical detection. The impact of the proposed panel design criteria on at-risk couple detection warrants data-driven evaluation.MethodsCarrier rates and at-risk couple rates were calculated in 56,281 patients who underwent a 176-condition ECS and were evaluated for panels satisfying various criteria. Condition-specific clinical detection rates were estimated via simulation.ResultsDifferent interpretations of the 1-in-100 criterion have variable impact: a compliant panel would include between 3 and 38 conditions, identify 11–81% fewer at-risk couples, and detect 36–79% fewer carriers than a 176-condition panel. If the carrier rate threshold must be exceeded in all ethnicities, ECS panels would lack prevalent conditions like cystic fibrosis. Simulations suggest that the clinical detection rate remains >84% for conditions with carrier rates as low as 1 in 1000.ConclusionThe 1-in-100 criterion limits at-risk couple detection and should be reconsidered.

BackgroundAcetaminophen (N-acetyl-para-aminophenol) is the most widely used over-the-counter or prescription painkiller in the world. Acetaminophen is metabolized in the liver where a toxic byproduct is produced that can be removed by conjugation with glutathione. Acetaminophen overdoses, either accidental or intentional, are the leading cause of acute liver failure in the United States, accounting for 56,000 emergency room visits per year. The standard treatment for overdose is N-acetyl-cysteine (NAC), which is given to stimulate the production of glutathione.MethodsWe have created a mathematical model for acetaminophen transport and metabolism including the following compartments: gut, plasma, liver, tissue, urine. In the liver compartment the metabolism of acetaminophen includes sulfation, glucoronidation, conjugation with glutathione, production of the toxic metabolite, and liver damage, taking biochemical parameters from the literature whenever possible. This model is then connected to a previously constructed model of glutathione metabolism.ResultsWe show that our model accurately reproduces published clinical and experimental data on the dose-dependent time course of acetaminophen in the plasma, the accumulation of acetaminophen and its metabolites in the urine, and the depletion of glutathione caused by conjugation with the toxic product. We use the model to study the extent of liver damage caused by overdoses or by chronic use of therapeutic doses, and the effects of polymorphisms in glucoronidation enzymes. We use the model to study the depletion of glutathione and the effect of the size and timing of N-acetyl-cysteine doses given as an antidote. Our model accurately predicts patient death or recovery depending on size of APAP overdose and time of treatment.ConclusionsThe mathematical model provides a new tool for studying the effects of various doses of acetaminophen on the liver metabolism of acetaminophen and glutathione. It can be used to study how the metabolism of acetaminophen depends on the expression level of liver enzymes. Finally, it can be used to predict patient metabolic and physiological responses to APAP doses and different NAC dosing strategies.

Dengue is a vector-borne viral disease of humans that endemically circulates in many tropical and subtropical regions worldwide. Infection with dengue can result in a range of disease outcomes. A considerable amount of research has sought to improve our understanding of this variation in disease outcomes and to identify predictors of severe disease. Contributing to this research, patterns of viral load in dengue infected patients have been quantified, with analyses indicating that peak viral load levels, rates of viral load decline, and time to peak viremia are useful predictors of severe disease. Here, we take a complementary approach to understanding patterns of clinical manifestation and inter-individual variation in viral load dynamics. Specifically, we statistically fit mathematical within-host models of dengue to individual-level viral load data to test virological and immunological hypotheses explaining inter-individual variation in dengue viral load. We choose between alternative models using model selection criteria to determine which hypotheses are best supported by the data. We first show that the cellular immune response plays an important role in regulating viral load in secondary dengue infections. We then provide statistical support for the process of antibody-dependent enhancement (but not original antigenic sin) in the development of severe disease in secondary dengue infections. Finally, we show statistical support for serotype-specific differences in viral infectivity rates, with infectivity rates of dengue serotypes 2 and 3 exceeding those of serotype 1. These results contribute to our understanding of dengue viral load patterns and their relationship to the development of severe dengue disease. They further have implications for understanding how dengue transmissibility may depend on the immune status of infected individuals and the identity of the infecting serotype.

An extensive body of theory addresses the topic of pathogen virulence evolution, yet few studies have empirically demonstrated the presence of fitness trade-offs that would select for intermediate virulence. Here we show the presence of transmission-clearance trade-offs in dengue virus using viremia measurements. By fitting a within-host model to these data, we further find that the interaction between dengue and the host immune response can account for the observed trade-offs. Finally, we consider dengue virulence evolution when selection acts on the virus’s production rate. By combining within-host model simulations with empirical findings on how host viral load affects human-to-mosquito transmission success, we show that the virus’s transmission potential is maximized at production rates associated with intermediate virulence and that the optimal production rate critically depends on dengue’s epidemiological context. These results indicate that long-term changes in dengue’s global distribution impact the invasion and spread of virulent dengue virus genotypes.