Mathematical Model of Viral Kinetics In Vitro Estimates the Number of E2-CD81 Complexes Necessary for Hepatitis C Virus Entry

Interaction between the hepatitis C virus (HCV) envelope protein E2 and the host receptor CD81 is essential for HCV entry into target cells. The number of E2-CD81 complexes necessary for HCV entry has remained difficult to estimate experimentally. Using the recently developed cell culture systems that allow persistent HCV infection in vitro, the dependence of HCV entry and kinetics on CD81 expression has been measured. We reasoned that analysis of the latter experiments using a mathematical model of viral kinetics may yield estimates of the number of E2-CD81 complexes necessary for HCV entry. Here, we constructed a mathematical model of HCV viral kinetics in vitro, in which we accounted explicitly for the dependence of HCV entry on CD81 expression. Model predictions of viral kinetics are in quantitative agreement with experimental observations. Specifically, our model predicts triphasic viral kinetics in vitro, where the first phase is characterized by cell proliferation, the second by the infection of susceptible cells and the third by the growth of cells refractory to infection. By fitting model predictions to the above data, we were able to estimate the threshold number of E2-CD81 complexes necessary for HCV entry into human hepatoma-derived cells. We found that depending on the E2-CD81 binding affinity, between 1 and 13 E2-CD81 complexes are necessary for HCV entry. With this estimate, our model captured data from independent experiments that employed different HCV clones and cells with distinct CD81 expression levels, indicating that the estimate is robust. Our study thus quantifies the molecular requirements of HCV entry and suggests guidelines for intervention strategies that target the E2-CD81 interaction. Further, our model presents a framework for quantitative analyses of cell culture studies now extensively employed to investigate HCV infection

Copula based prediction models: an application to an aortic regurgitation study

<p>Abstract</p> <p>Background:</p> <p>An important issue in prediction modeling of multivariate data is the measure of dependence structure. The use of Pearson's correlation as a dependence measure has several pitfalls and hence application of regression prediction models based on this correlation may not be an appropriate methodology. As an alternative, a copula based methodology for prediction modeling and an algorithm to simulate data are proposed.</p> <p>Methods:</p> <p>The method consists of introducing copulas as an alternative to the correlation coefficient commonly used as a measure of dependence. An algorithm based on the marginal distributions of random variables is applied to construct the <it>Archimedean </it>copulas. Monte Carlo simulations are carried out to replicate datasets, estimate prediction model parameters and validate them using Lin's concordance measure.</p> <p>Results:</p> <p>We have carried out a correlation-based regression analysis on data from 20 patients aged 17–82 years on pre-operative and post-operative ejection fractions after surgery and estimated the prediction model: Post-operative ejection fraction = - 0.0658 + 0.8403 (Pre-operative ejection fraction); p = 0.0008; 95% confidence interval of the slope coefficient (0.3998, 1.2808). From the exploratory data analysis, it is noted that both the pre-operative and post-operative ejection fractions measurements have slight departures from symmetry and are skewed to the left. It is also noted that the measurements tend to be widely spread and have shorter tails compared to normal distribution. Therefore predictions made from the correlation-based model corresponding to the pre-operative ejection fraction measurements in the lower range may not be accurate. Further it is found that the best approximated marginal distributions of pre-operative and post-operative ejection fractions (using q-q plots) are gamma distributions. The copula based prediction model is estimated as: Post -operative ejection fraction = - 0.0933 + 0.8907 × (Pre-operative ejection fraction); p = 0.00008 ; 95% confidence interval for slope coefficient (0.4810, 1.3003). For both models differences in the predicted post-operative ejection fractions in the lower range of pre-operative ejection measurements are considerably different and prediction errors due to copula model are smaller. To validate the copula methodology we have re-sampled with replacement fifty independent bootstrap samples and have estimated concordance statistics 0.7722 (p = 0.0224) for the copula model and 0.7237 (p = 0.0604) for the correlation model. The predicted and observed measurements are concordant for both models. The estimates of accuracy components are 0.9233 and 0.8654 for copula and correlation models respectively.</p> <p>Conclusion:</p> <p>Copula-based prediction modeling is demonstrated to be an appropriate alternative to the conventional correlation-based prediction modeling since the correlation-based prediction models are not appropriate to model the dependence in populations with asymmetrical tails. Proposed copula-based prediction model has been validated using the independent bootstrap samples.</p

Understanding the factors influencing pharmacokinetics of tacrolimus

Tacrolimus, a potent calcineurin inhibitor integral to immunosuppressive regimens, exhibits complex pharmacokinetics influenced by diverse factors and understanding these factors is crucial for safety, efficacy and dose optimisation. Genetic variations, particularly in CYP3A enzyme systems and P- Glycoprotein, contribute significantly to inter-individual variability in tacrolimus metabolism. Polymorphisms in these systems alter drug bioavailability, impacting clinical outcomes. Ethnicity further compounds this variability, with distinct genetic profiles leading to differential drug responses. Notably, black patients, often characterized by CYP3A5 expressor status, may have higher drug clearance. Age-related changes in tacrolimus clearance highlights the discrepancies in elderly and paediatric populations. On the other hand, prediction of gender-specific differences is difficult due to lack of evidence. Body composition, specifically variations in fat and muscle mass, significantly impacts drug distribution and clearance. Obesity, associated with altered CYP3A activity, results in decreased drug clearance, emphasizing the importance of accounting for body composition in dosing calculations. Pregnancy -induced physiological changes affect tacrolimus absorption, distribution, metabolism, and excretion, necessitating careful monitoring and dose adjustments in pregnant individuals. Dietary factors and drug interactions, particularly with CYP3A4 and P-glycoprotein, further contribute to the intricate web of variables influencing tacrolimus pharmacokinetics. In conclusion, this review sheds light on the multifaceted factors influencing tacrolimus pharmacokinetics, providing essential insights for clinicians to tailor individualized dosing regimens and enhance therapeutic efficacy while minimizing the risk of adverse events

Controlled release nanoparticulate matter delivery system

A controlled release nanoparticulate matter delivery system includes a plurality of thermoresponsive modules containing a respective nanoparticulate matter. Each thermoresponsive module is selectively operable in at least one of a heating mode that releases the nanoparticulate matter and a cooling mode that inhibits release of the nanoparticulate matter. A control module is in electrical communication with the plurality of thermoresponsive modules. The control module is configured to determine a temperature of each thermoresponsive module and to select the at least one heating mode and cooling mode based on the temperature. The heating and cooling mode may be selected in response to a desired dosing profile and/or a biometric condition.Board of Regents, University of Texas Syste

Binary black hole merger gravitational waves and recoil in the large mass ratio limit

Spectacular breakthroughs in numerical relativity now make it possible to compute spacetime dynamics in almost complete generality, allowing us to model the coalescence and merger of binary black holes with essentially no approximations. The primary limitation of these calculations is now computational. In particular, it is difficult to model systems with large mass ratio and large spins, since one must accurately resolve the multiple lengthscales which play a role in such systems. Perturbation theory can play an important role in extending the reach of computational modeling for binary systems. In this paper, we present first results of a code which allows us to model the gravitational waves generated by the inspiral, merger, and ringdown of a binary system in which one member of the binary is much more massive than the other. This allows us to accurately calibrate binary dynamics in the large mass ratio regime. We focus in this analysis on the recoil imparted to the merged remnant by these waves. We closely examine the "antikick", an anti-phase cancellation of the recoil arising from the plunge and ringdown waves, described in detail by Schnittman et al. We find that, for orbits aligned with the black hole spin, the antikick grows as a function of spin. The total recoil is smallest for prograde coalescence into a rapidly rotating black hole, and largest for retrograde coalescence. Amusingly, this completely reverses the predicted trend for kick versus spin from analyses that only include inspiral information.Comment: 15 pages, 5 figures. Submitted to Phys. Rev.

Studies of inherent lubricity coatings for low surface roughness galvanised steel for automotive applications

Surface lubricity on TiO2-coated galvanised steels can be controlled by solution depositing perfluorooctanoic (C8), lauric (C12) or stearic (C18) acids to avoid lubricating oils/emulsions or substrate pre-etching to remove surface oxide that add cost and waste. Water contact angles reveal increased surface hydrophobicity on coated samples that correlate with linear friction testing, suggesting water contact angle can be used to screen lubricity compounds. Linear friction testing shows that C12 and C18 lower the coefficient of friction (μ) by 50–60% compared with uncoated substrates whilst C8 drops μ from 0.31 to 0.22. Surfaces have been characterised by X-ray photoelectron spectroscopy, scanning electron microscopy and atomic force microscopy, whilst infrared confirms that as-deposited coatings contain physisorbed and deprotonated acids chemisorbed through esters and thermal gravimetric analysis confirms increasing loadings from C8 to C12 to C18. Surface washing removes physisorbed material and lowers μ by increasing surface organisation and alkyl chain packing that enhances frictional energy dissipation through steric quenching

Families\u27 healthcare experiences for children with inherited metabolic diseases: Protocol for a mixed methods cohort study

Introduction Children with inherited metabolic diseases (IMDs) often have complex and intensive healthcare needs and their families face challenges in receiving high-quality, family centred health services. Improvement in care requires complex interventions involving multiple components and stakeholders, customised to specific care contexts. This study aims to comprehensively understand the healthcare experiences of children with IMDs and their families across Canada. Methods and analysis A two-stage explanatory sequential mixed methods design will be used. Stage 1: quantitative data on healthcare networks and encounter experiences will be collected from 100 parent/guardians through a care map, 2 baseline questionnaires and 17 weekly diaries over 5-7 months. Care networks will be analysed using social network analysis. Relationships between demographic or clinical variables and ratings of healthcare experiences across a range of family centred care dimensions will be analysed using generalised linear regression. Other quantitative data related to family experiences and healthcare experiences will be summarised descriptively. Ongoing analysis of quantitative data and purposive, maximum variation sampling will inform sample selection for stage 2: a subset of stage 1 participants will participate in one-on-one videoconference interviews to elaborate on the quantitative data regarding care networks and healthcare experiences. Interview data will be analysed thematically. Qualitative and quantitative data will be merged during analysis to arrive at an enhanced understanding of care experiences. Quantitative and qualitative data will be combined and presented narratively using a weaving approach (jointly on a theme-by-theme basis) and visually in a side-by-side joint display. Ethics and dissemination The study protocol and procedures were approved by the Children\u27s Hospital of Eastern Ontario\u27s Research Ethics Board, the University of Ottawa Research Ethics Board and the research ethics boards of each participating study centre. Findings will be published in peer-reviewed journals and presented at scientific conferences