Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis. Patients sometimes have moderate or no symptoms of liver illness for decades before developing cirrhosis. Cirrhosis typically worsens to the point of liver failure. Patients with cirrhosis may also experience brain and nerve system damage, as well as gastrointestinal hemorrhage. Treatment for cirrhosis focuses on preventing further progression of the disease. Detecting cirrhosis earlier is therefore crucial for avoiding complications. Machine learning (ML) has been shown to be effective at providing precise and accurate information for use in diagnosing several diseases. Despite this, no studies have so far used ML to detect cirrhosis in patients with hepatitis C. This study obtained a dataset consisting of 28 attributes of 2038 Egyptian patients from the ML Repository of the University of California at Irvine. Four ML algorithms were trained on the dataset to diagnose cirrhosis in hepatitis C patients: a Random Forest, a Gradient Boosting Machine, an Extreme
Polymorphism has been observed in viral capsid assembly, demonstrating the ability of identical protein dimers to adopt multiple geometries under the same solution conditions. A well-studied example is the hepatitis B virus (HBV), which forms two stable capsid morphologies both in vivo and in vitro. These capsids differ in diameter, containing either 90 or 120 protein dimers. Experiments have shown that their relative prevalence depends on the ionic conditions of the solution during assembly. We developed a model that incorporates salt effects by altering the intermolecular binding free energy between capsid proteins, thereby shifting the relative thermodynamic stability of the two morphologies. This model reproduces experimental results on the prevalence ratios of the large and small HBV capsids. We also constructed a kinetic model that captures the time-dependent ratio of the two morphologies under subcritical capsid concentrations, consistent with experimental data.
Decision-makers frequently must choose a single action from a finite set of alternatives -- for example, physicians selecting a treatment, investors choosing a portfolio risk level, or judges determining sentences. To improve outcomes, policymakers often issue policy rules or guidelines to inform such choices. In this paper, I show how to generally derive policy rules from observational data in a multi-action framework under relatively weak assumptions about the underlying structure of the heterogeneous sampled population. Conditional average treatment effects (CATEs) are consistently estimated via a weighted K-means algorithm, assuming the outcome model is correctly specified within each homogeneous subgroup. Feasible policy rules are then implemented via a standard decision tree, allowing for both perfect and imperfect adherence to treatment. The methodology is applied to treatment options for Hepatitis C (HCV) among patients co-infected with human immunodeficiency virus (HIV), a setting in which no uniform guideline exists for modern pharmaceutical therapies. The results identify a subgroup of patients with approximately an 80% probability of spontaneous HCV clearance without tr
Introduction: In Thailand, Hepatitis B is still endemic despite a strong program to eliminate the disease. A higher prevalence is reported in the border region and among migrants due to physical, financial and cultural barriers. Policies and programs targeting the border region and migrant communities have been suggested. Models can be used to understand and quantify the impact of these policies, given they can capture the heterogeneity within the population. Methods: In this study, we developed an Agent-based model that captures the differences between the Thai and migrant populations living in this region, notably the higher level of mobility, lower access to healthcare, and the higher prevalence of Hepatitis B among migrants, by modelling the origin of each individual explicitly. We used the model to estimate future trends of Hepatitis B prevalence in Thailand near the border with Myanmar under different scenarios of intervention. Results: Our study shows that although the current intervention level is effective in the Thai population, it is insufficient to reach national elimination targets due to high prevalence in migrants. Improving access to healthcare for migrants and the
The entry inhibitor Bulevirtide (BLV) was recently approved in Europe for treatment of chronic hepatitis D virus (HDV) infection, which is considered the most severe viral hepatitis infection. Theory indicates that models that account for free virus and infected cells, but do not include target cell dynamics (historically called the two-equation model) are limited to predicting a monophasic viral decline for antiviral agents that act only to block viral entry/infection. We investigated herein a recently published two-equation type model against clinical data obtained from patients with HDV treated with BLV monotherapy for up to 96 weeks using non-linear mixed effects modelling (NLME). We found that (i) although the model parameters had a relative standard error (RSE) <50\% suggesting that they were 'precisely estimated', the fits failed to reproduce the non-monophasic HDV kinetic patterns observed in most patients leading to incorrect predictions of the duration of treatment needed to reach a theoretical cure boundary, defined as less than 1 virion in the entire patient extracellular body fluid. (ii) The model cannot explain viral breakthrough, and (iii) the model wrongly predic
Chronic infection with hepatitis B virus (HBV) can lead to formation of abnormal nodular structures within the liver. To address how changes in liver anatomy affect overall virus-host dynamics, we developed within-host ordinary differential equation models of two-patch hepatitis B infection, one that assumes irreversible and one that assumes reversible movement between nodular structures. We investigated the models analytically and numerically, and determined the contribution of patch susceptibility, immune responses, and virus movement on within-patch and whole-liver virus dynamics. We explored the structural and practical identifiability of the models by implementing a differential algebra approach and the Monte Carlo approach for a specific HBV data set. We determined conditions for viral clearance, viral localization, and systemic viral infection. Our study suggests that cell susceptibility to infection within modular structures, the movement rate between patches, and the immune-mediated infected cell killing have the most influence on HBV dynamics. Our results can help inform intervention strategies.
Background and Aims: Pegylated interferon (PEG-IFN) combined with oral antiviral agents is currently the most widely used and highly effective treatment regimen for chronic hepatitis B virus (HBV) infection. While effectively suppressing HBV replication, its impact on liver histopathological fibrosis and inflammation remains a critical concern for clinicians and patients. Methods : A total of 625 patients who completed 48 weeks of PEG-IFN combined with oral antiviral therapy were enrolled in this real-world study. Based on their virological response at 48 weeks, patients were categorized into Clearance group and Non-clearance group. Changes in liver biochemistry, fibrosis, and renal function were compared between groups and before/after treatment. Results: No significant differences were observed in baseline blood tests, liver biochemical markers, or histopathological features between the Clearance group and Non-clearance group. Similarly, baseline renal function showed no significant variation. Further analysis revealed that the Clearance group exhibited significant aggravation of liver fibrosis after 48 weeks of treatment, which correlated strongly with alterations in liver enzym
The best way to treat chronic hepatitis B is with pegylated interferon alone or with oral antiviral drugs. There is limited research comparing the renal safety of entecavir and tenofovir when used with pegylated interferon. This study will compare changes in renal function in chronic hepatitis B patients treated with pegylated interferon and either entecavir or tenofovir. The study included a cohort of 836 patients with chronic hepatitis B (CHB) who received treatment with pegylated interferon (IFN) either alone or in combination with entecavir (ETV) and tenofovir (TDF) between the years 2018 and 2021. Of these patients, 713 were included in a matched analysis comparing outcomes between those who were cured and those who were uncured, while 123 patients received IFN alone as a control group for comparison with the ETV and TDF treatment groups. The primary outcome measured was the change in renal function, specifically estimated glomerular filtration rate (eGFR), cystatin C (CysC), and inorganic phosphorus (IPHOS). Patients were categorized into stage 1 or stage 2 based on a baseline eGFR of less than 90 ml/min/m^2 Results: 125 CHB patients were matched 1:1 in both the combined trea
Multiscale mathematical models of hepatitis C infection have been instrumental in our understanding of direct acting antivirals. These models include the mechanisms driving intracellular viral production and explicitly model the intracellular concentration of viral RNA. Incorporating proliferation of infected hepatocytes in these models can be subtle, as infected daughter cells inherit viral RNA from the proliferating mother cell. In this note, we show how to incorporate this inheritance within a multiscale model of HCV infection. As in typical multiscale models of HCV infection, we show that this model is mathematically equivalent to a system of ordinary differential equations and perform bifurcation analysis of the resulting ODE that demonstrates that proliferation of infected hepatocytes can lead to infection persistence even if the basic repoductive number is less than one.
Improved hygiene and infant vaccinations have led to age specific variations in hepatitis A antibody prevalence in Korea, with lower rates among individuals in their 20s to 40s. Given that the fatality rate of hepatitis A increases for those aged 50 and older, the low immunity level among younger adults indicates a future risk of increased deaths in older age groups without additional preventive measures.We developed an age structured transmission model to assess the impact of adult vaccination, assuming it begins in 2025. The 20s age group was modeled with an additional compartment to account for hepatitis A vaccination administered to military recruits. Vaccination strategies targeting the 20s to 30s and 40s to 50s age groups were compared, considering antibody testing costs for the latter in Korea and focusing on projected deaths over approximately 50 years. When the total vaccination cost is fixed, targeting the 40s to 50s group covers 20% fewer individuals than the 20s to 30s group but yields a 1.3 to 1.5 fold greater reduction in deaths. When the total vaccine supply is fixed, targeting the 40s to 50s group is 1.2 times more expensive but yields a 1.7 to 1.8 fold greater redu
Hepatitis B virus (HBV) is considered as etiological agent of the lethal liver disease hepatitis B. Globally, hepatitis B is recognized as one of the prevailing infectious diseases with a significant impact on human health. In spite of being non-infectious in nature, sub-viral particles (SVPs) , composed with mainly viral surface proteins, play critical roles in the persistence and progression of the infection. Although the understanding on the functions of these non-infectious SVPs remains limited and incomplete. In this study, a mathematical model is proposed for the first time by incorporating the roles of SVPs and including the effects of capsids recycling. The impacts of spatial mobility of capsids, viruses, SVPs and antibodies are also taken into account in this model. Overall, this model carry unique characteristics in the context of this viral infection. This study investigates the changes in the dynamics of infection considering both single-point as well as multi-point infection initial condition. As a result, it is observed that SVPs can significantly enhance intracellular viral replication and gene expression by reducing the neutralization of virus particles by antibodie
Hepatitis A virus (HAV) infection has been greatly reduced in most developed countries through the use of vaccine and improved hygienic conditions. However, the magnitude of the problem is underestimated and there are no well-established Hepatitis A virus prevention and control strategies in Nigeria. The aim of this study was to determine the prevalence of Hepatitis A virus infection among children aged 2 to 9 years in Rumuewhor, Emuoha LGA, Rivers State, Nigeria. Blood samples were collected from the 89 children enrolled in this study, and analyzed for the presence of HAV IgG antibodies using ELISA techniques. Of the 89 participants, 22 (24.7%) tested positive for HAV IgG antibodies, while 67 (75.3%) were negative. The children within the ages of 4 to 6 years had the highest seropositivity rate (33.3%) while those less than 4 years had the least seropositivity rate (22.4%). The prevalence rate ratio of the males to females was 1:1.3. There was no significant difference (p between IgG seropositivity and age groups and gender. However, there was a statistical association of IgG seropositivity rates with respect to immunization. The seroprevalence rate recorded in this study was sign
Cost-effectiveness analyses, based on decision-analytic models of disease progression and treatment, are routinely used to assess the economic value of a new intervention and consequently inform reimbursement decisions for the intervention. Many decision-analytic models developed to assess the economic value of highly effective directly acting antiviral (DAA) treatments for the hepatitis C virus (HCV) infection do not incorporate the transmission dynamics of HCV, accounting for which is required to estimate the number of downstream infections prevented by curing an infection. In this study, we develop and validate a comprehensive agent-based simulation (ABS) model of HCV transmission dynamics in the Indian context and use it to: (a) quantify the extent to which the cost-effectiveness of a DAA is underestimated - as a function of its uptake rate - if disease transmission dynamics are not considered in a cost-effectiveness analysis model; and (b) quantify the impact of the frequency and timing of treatment with DAAs, also as a function of their uptake rate, within a disease surveillance period on its cost-effectiveness.
Background: It is not established whether clinical notes provided on pathology request forms are useful as decision support data when assessing Hepatitis B and C viral infection status. Objective: To determine sensitivity, specificity, and predictive value of clinical notes for identifying infection status of Hepatitis B and C. Methods: The study comprises 179 cases and 166 cases tested for HBsAg and anti-HCV serological markers, respectively, and accompanied by a written description (clinical note) provided on pathology request forms by the clinician on duty. The clinical note sensitivity, specificity, positive (PPV) and negative (NPV) predictive values were calculated using serological HBsAg and anti-HCV tests as gold standards. Results: The sensitivity and specificity of clinical notes for Hepatitis B infection status were 90 percent and 56 percent, respectively. The sensitivity and specificity of clinical notes for Hepatitis C infection status were 86 percent and 21 percent, respectively. Conclusions: Clinical note information identifies moderate-to-high sensitivity with regards to Hepatitis B and C viral infection status, however, given low specificity in both groups, the clin
Proliferation of uninfected as well as infected hepatocytes and recycling of DNA-containing capsids are two major mechanisms playing significant roles in the clearance of hepatitis B virus (HBV) infection. In this study, the temporal dynamics of this infection are investigated through two in silico bio-mathematical models considering both proliferation of hepatocytes and the recycling of capsids. Both models are formulated on the basis of a key finding in the existing literature: mitosis of infected yields in two uninfected progenies. In the first model, we examine regular proliferation (occurs continuously), while the second model deals with the irregular proliferation (happens when the total number of liver cells decreases to less than 70% of its initial volume). The models are calibrated with the experimental data obtained from an adult chimpanzee. Results of this study suggest that when both hepatocytes proliferate with equal rate, proliferation aids the individual in a rapid recovery from the acute infection whereas in the case of chronic infection, the severity of the infection increases if the proliferation occurs frequently. On the other hand, if the infected cells prolifer
In this paper, we investigate the dynamics of hepatitis B virus infection taking into account the implementation of combination therapy through mathematical modeling. This model is established considering the interplay between uninfected cells, infected cells, capsids, and viruses. Three drugs are considered for specific roles (i) pegylated interferon (PEG IFN) for immune modulation, (ii) lamivudine (LMV) as a reverse-transcriptase inhibitor, and (iii) entecavir (ETV) to block capsid recycling. Using these drugs, three combination therapies are introduced, specifically CT PEG IFN plus LMV, CT PEG IFN plus ETV, and CT PEG IFN plus LMV plus ETV. As a result, when LMV is used in combination therapy with PEG IFN and ETV, the impacts of ETV become insignificant. In conclusion, if the appropriate drug effectively inhibits reverse transcription, there is no need for an additional inhibitor to block capsid recycling.
This study presents an improved mathematical model for Hepatitis B Virus (HBV) transmission dynamics by investigating autonomous and nonautonomous cases. The novel model incorporates the effects of medical treatment, allowing for a more comprehensive understanding of HBV transmission and potential control measures. Our analysis involves verifying unique solutions' existence, ensuring solutions' positivity over time, and conducting a stability analysis at the equilibrium points. Both local and global stability are discussed; for local stability, we use the Jacobian matrix and the basic reproduction number, $R_0$. For global stability, we construct a Lyapunov function and derive necessary and sufficient conditions for stability in our models, establishing a connection between these conditions and $R_0$. Numerical simulations substantiate our analytical findings, offering valuable insights into HBV transmission dynamics and the effectiveness of different interventions. This study advances our understanding of Hepatitis B Virus (HBV) transmission dynamics by presenting an enhanced mathematical model that considers both autonomous and nonautonomous cases.
This study develops and analyzes a stochastic differential equation (SDE) model for the dynamics of hepatitis B virus (HBV) infection. While deterministic frameworks have yielded important insights into viral behavior, they cannot adequately describe the intrinsic randomness and fluctuations present in biological processes. To address this limitation, we construct a stochastic model incorporating multiplicative environmental noise to account for variability in infection rates, cellular mortality, and viral replication. We establish a rigorous theoretical foundation by proving the existence, uniqueness, and global positivity of solutions for all biologically relevant initial conditions. Stability properties are investigated in detail, including stability in probability and almost sure exponential stability, with particular emphasis on conditions under which random perturbations stabilize the infection-free state. Furthermore, we demonstrate the existence of a unique ergodic stationary distribution and derive convergence properties of the uninfected hepatocyte population. Numerical simulations, performed via the Euler-Maruyama method with sufficiently small time steps to ensure posit
Viral hepatitis is the regularly found health problem throughout the world among other easily transmitted diseases, such as tuberculosis, human immune virus, malaria and so on. Among all hepatitis viruses, the uppermost numbers of deaths are result from the long-lasting hepatitis C infection or long-lasting hepatitis B. In order to develop this system, the knowledge is acquired using both structured and semi-structured interviews from internists of St.Paul Hospital. Once the knowledge is acquired, it is modeled and represented using rule based reasoning techniques. Both forward and backward chaining is used to infer the rules and provide appropriate advices in the developed expert system. For the purpose of developing the prototype expert system SWI-prolog editor also used. The proposed system has the ability to adapt with dynamic knowledge by generalizing rules and discover new rules through learning the newly arrived knowledge from domain experts adaptively without any help from the knowledge engineer. Keywords: Expert System, Diagnosis and Management of Viral Hepatitis, Adaptive Learning, Discovery and Generalization Mechanism
We investigate the dynamics of the hepatitis B virus by integrating variable-order calculus and discrete analysis. Specifically, we utilize the Caputo variable-order difference operator in this study. To establish the existence and uniqueness results of the model, we employ a fixed-point technique. Furthermore, we prove that the model exhibits bounded and positive solutions. Additionally, we explore the local stability of the proposed model by determining the basic reproduction number. Finally, we present several numerical simulations to illustrate the richness of our results.