Comparative mathematical modeling reveals the differential effects of high-fat diet and ketogenic diet on the PI3K-Akt signaling pathway in heart.

BACKGROUND: Obesity is a global health concern associated with increased risk of diseases like cardiovascular conditions including ischemic heart disease, a leading cause of mortality. The ketogenic diet (KD) has potential therapeutic applications in managing obesity and related disorders. However, the intricate effects of KD on diverse physiological conditions remain incompletely understood. The PI3K-Akt signaling pathway is critical for heart health, and its dysregulation implicates numerous cardiac diseases. METHODS: We developed comprehensive mathematical models of the PI3K-Akt signaling pathway under high-fat diet (HFD) and KD conditions to elucidate their differential impacts and quantify apoptosis. Simulations and sensitivity analysis were performed. RESULTS: Simulations demonstrate that KD can reduce the activation of key molecules like Erk and Trp53 to mitigate apoptosis compared to HFD. Findings align with experimental data, highlighting the potential cardiac benefits of KD. Sensitivity analysis identifies regulators like Trp53 and Bcl2l1 that critically influence apoptosis under HFD. CONCLUSIONS: Mathematical modeling provides quantitative insights into the contrasting effects of HFD and KD on cardiac PI3K-Akt signaling and apoptosis. Findings have implications for precision nutrition and developing novel therapeutic strategies to address obesity-related cardiovascular diseases.

Assessing the Role of Patient Generation Techniques in Virtual Clinical Trial Outcomes.

Virtual clinical trials (VCTs) are growing in popularity as a tool for quantitatively predicting heterogeneous treatment responses across a population. In the context of a VCT, a plausible patient is an instance of a mathematical model with parameter (or attribute) values chosen to reflect features of the disease and response to treatment for that particular patient. A number of techniques have been introduced to determine the set of model parametrizations to include in a virtual patient cohort. These methodologies generally start with a prior distribution for each model parameter and utilize some criteria to determine whether a parameter set sampled from the priors should be included or excluded from the plausible population. No standard technique exists, however, for generating these prior distributions and choosing the inclusion/exclusion criteria. In this work, we rigorously quantify the impact that VCT design choices have on VCT predictions. Rather than use real data and a complex mathematical model, a spatial model of radiotherapy is used to generate simulated patient data and the mathematical model used to describe the patient data is a two-parameter ordinary differential equations model. This controlled setup allows us to isolate the impact of both the prior distribution and the inclusion/exclusion criteria on both the heterogeneity of plausible populations and on predicted treatment response. We find that the prior distribution, rather than the inclusion/exclusion criteria, has a larger impact on the heterogeneity of the plausible population. Yet, the percent of treatment responders in the plausible population was more sensitive to the inclusion/exclusion criteria utilized. This foundational understanding of the role of virtual clinical trial design should help inform the development of future VCTs that use more complex models and real data.

Real-time assessment of mitochondrial DNA heteroplasmy dynamics at the single-cell level.

Mitochondrial DNA (mtDNA) is present in multiple copies within cells and is required for mitochondrial ATP generation. Even within individual cells, mtDNA copies can differ in their sequence, a state known as heteroplasmy. The principles underlying dynamic changes in the degree of heteroplasmy remain incompletely understood, due to the inability to monitor this phenomenon in real time. Here, we employ mtDNA-based fluorescent markers, microfluidics, and automated cell tracking, to follow mtDNA variants in live heteroplasmic yeast populations at the single-cell level. This approach, in combination with direct mtDNA tracking and data-driven mathematical modeling reveals asymmetric partitioning of mtDNA copies during cell division, as well as limited mitochondrial fusion and fission frequencies, as critical driving forces for mtDNA variant segregation. Given that our approach also facilitates assessment of segregation between intact and mutant mtDNA, we anticipate that it will be instrumental in elucidating the mechanisms underlying the purifying selection of mtDNA.

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer.

BACKGROUND: Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. Therapeutic targeting of miR-155 through its antagonist, anti-miR-155, has proven challenging due to its dual molecular effects. METHODS: We developed a multiscale mechanistic model, calibrated with in vivo data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs. RESULTS: Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimens to prevent antagonistic effects. CONCLUSIONS: This work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.

Modeling the interaction between tubuloglomerular feedback and myogenic mechanisms in the control of glomerular mechanics.

Introduction: Mechanical stresses and strains exerted on the glomerular cells have emerged as potentially influential factors in the progression of glomerular disease. Renal autoregulation, the feedback process by which the afferent arteriole changes in diameter in response to changes in blood pressure, is assumed to control glomerular mechanical stresses exerted on the glomerular capillaries. However, it is unclear how the two major mechanisms of renal autoregulation, the afferent arteriole myogenic mechanism and tubuloglomerular feedback (TGF), each contribute to the maintenance of glomerular mechanical homeostasis. Methods: In this study, we made a mathematical model of renal autoregulation and combined this model with an anatomically accurate model of glomerular blood flow and filtration, developed previously by us. We parameterized the renal autoregulation model based on data from previous literature, and we found evidence for an increased myogenic mechanism sensitivity when TGF is operant, as has been reported previously. We examined the mechanical effects of each autoregulatory mechanism (the myogenic, TGF and modified myogenic) by simulating blood flow through the glomerular capillary network with and without each mechanism operant. Results: Our model results indicate that the myogenic mechanism plays a central role in maintaining glomerular mechanical homeostasis, by providing the most protection to the glomerular capillaries. However, at higher perfusion pressures, the modulation of the myogenic mechanism sensitivity by TGF is crucial for the maintenance of glomerular mechanical homeostasis. Overall, a loss of renal autoregulation increases mechanical strain by up to twofold in the capillaries branching off the afferent arteriole. This further corroborates our previous simulation studies, that have identified glomerular capillaries nearest to the afferent arteriole as the most prone to mechanical injury in cases of disturbed glomerular hemodynamics. Discussion: Renal autoregulation is a complex process by which multiple feedback mechanisms interact to control blood flow and filtration in the glomerulus. Importantly, our study indicates that another function of renal autoregulation is control of the mechanical stresses on the glomerular cells, which indicates that loss or inhibition of renal autoregulation may have a mechanical effect that may contribute to glomerular injury in diseases such as hypertension or diabetes. This study highlights the utility of mathematical models in integrating data from previous experimental studies, estimating variables that are difficult to measure experimentally (i.e. mechanical stresses in microvascular networks) and testing hypotheses that are historically difficult or impossible to measure.

New mathematical model from dynamic dissolution rate tests.

In vitro dissolution experiments are becoming increasingly complex attempting to replicate in vivo behavior. The objective of these new methods is to explore the behavior of low-solubility drugs. This is more relevant for drugs classified in subclasses 2a, 2b and 2c of the BCS, considering their pH-dependent solubility and dissolution characteristics. A novel mathematical approach using a modified double Weibull equation is proposed to model the dissolution and precipitation kinetics observed in two-stage and transfer dissolution experiments (dumping test). This approach demonstrates a high level of accuracy in fitting experimental data for two drugs BCS class 2a and two BCS class 2b, providing valuable insights into their dissolution behavior under different conditions. The results highlight the versatility of the proposed model in capturing complex dissolution phenomena, including rapid dissolution, precipitation, and redissolution. The ease of implementation in Excel, using the Solver tool, makes it a practical and accessible tool for pharmaceutical researchers. Overall, the study contributes to the development of more effective in vitro dissolution testing methods, facilitating the formulation and optimization of pharmaceutical products and potentially aiding in the establishment of in vitro-in vivo correlations (IVIVC).

Assessment of bidirectional impact of stigmatization induced self-medication on COVID-19 and malaria transmissions using mathematical modeling: Nigeria as a case study.

The continual social and economic impact of infectious diseases on nations has maintained sustained attention on their control and treatment, of which self-medication has been one of the means employed by some individuals. Self-medication complicates the attempt of their control and treatment as it conflicts with some of the measures implemented by health authorities. Added to these complications is the stigmatization of individuals with some diseases in some jurisdictions. This study investigates the co-infection of COVID-19 and malaria and its related deaths and further highlights how self-medication and stigmatization add to the complexities of the fight against these two diseases using Nigeria as a study case. Using a mathematical model on COVID-19 and malaria co-infection, we address the question: to what degree does the impact of the interaction between COVID-19 and malaria amplify infections and deaths induced by both diseases via self-medication and stigmatization? We demonstrate that COVID-19 related self-medication due to misdiagnoses contributes substantially to the prevalence of disease. The control reproduction numbers for these diseases and quantification of model parameters uncertainties and sensitivities are presented.

Creep Failure Characteristics and Mathematical Modeling of High-Density Polyethylene Geomembranes under High Stress Levels.

To explore the creep characteristics of geomembrane under different tensile stresses, a series of creep tests were carried out on high-density polyethylene (HDPE) geomembrane specimens. For the interpretation and fitting of the experimental data, refined approximation functions were proposed. Particular attention was paid to the creep failure behavior under high tensile stresses, i.e., 70%, 80%, and 90% of maximum peak stress. To investigate the effects of size on the mechanical response, experiments with two different membrane thicknesses were conducted. The results obtained under high stress levels were compared with creep tests at medium and low stress levels. Depending on load level, different creep characteristics can be distinguished. In the secondary creep state, the creep velocity is higher for higher load levels. In contrast to the medium and low load levels, the geomembrane under high stresses underwent the tertiary creep stage after instantaneous deformation and primary and secondary creep stages. In some tests, it was observed that under very high stress levels, creep velocity does not necessarily follow the expected trend and creep rupture can occur within a short time. For numerical simulation, an improved mathematical model was proposed to reproduce in a unified manner the experimental data of the whole non-linear evolution of creep elongation under different stress levels.

Mechanical and biochemical feedback combine to generate complex contractile oscillations in cytokinesis.

The actomyosin cortex is an active material that generates force to drive shape changes via cytoskeletal remodeling. Cytokinesis is the essential cell division event during which a cortical actomyosin ring closes to separate two daughter cells. Our active gel theory predicted that actomyosin systems controlled by a biochemical oscillator and experiencing mechanical strain would exhibit complex spatiotemporal behavior. To test whether active materials in vivo exhibit spatiotemporally complex kinetics, we imaged the C. elegans embryo with unprecedented temporal resolution and discovered that sections of the cytokinetic cortex undergo periodic phases of acceleration and deceleration. Contractile oscillations exhibited a range of periodicities, including those much longer periods than the timescale of RhoA pulses, which was shorter in cytokinesis than in any other biological context. Modifying mechanical feedback in vivo or in silico revealed that the period of contractile oscillation is prolonged as a function of the intensity of mechanical feedback. Fast local ring ingression occurs where speed oscillations have long periods, likely due to increased local stresses and, therefore, mechanical feedback. Fast ingression also occurs where material turnover is high, in vivo and in silico. We propose that downstream of initiation by pulsed RhoA activity, mechanical feedback, including but not limited to material advection, extends the timescale of contractility beyond that of biochemical input and, therefore, makes it robust to fluctuations in activation. Circumferential propagation of contractility likely allows for sustained contractility despite cytoskeletal remodeling necessary to recover from compaction. Thus, like biochemical feedback, mechanical feedback affords active materials responsiveness and robustness.

Improving photovoltaic cell parameter calculations through a puffer fish inspired optimization technique.

The precise estimation of solar PV cell parameters has become increasingly important as solar energy deployment expands. Due to the intricate and nonlinear characteristics of solar PV cells, meta-heuristic algorithms show greater promise than traditional ones for parameter estimation. This study utilizes the Puffer Fish (PF) meta-heuristic optimization method, inspired by male puffer fish's circular structures, to estimate parameters of a modified four-diode PV cell. The PF algorithm's performance is assessed against ten benchmark test functions, with results presented as mean and standard deviation for validation. Comparative analysis with Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Rat Search Algorithm (RAT), Heap Based Optimizer (HBO), and Cuckoo Search (CS) algorithms highlights PF's superior performance, achieving optimal solutions with minimal error of 7.8947E-08. Statistical tests, including Friedman Ranking (1st) and Wilcoxon's rank sum (3.8108E-07), confirm PF's superiority. The circular structures of male puffer fish serve as an effective model for optimization algorithms, enhancing parameter estimation. Benchmark tests and statistical analysis consistently underscore PF's superiority over other meta-heuristic algorithms. Future research should explore PF's potential applications in solar energy and beyond.

Individualized, dynamic, and full-course vancomycin dosing prediction: a study on the customized dose model.

Purpose: The single-point trough-based therapeutic drug monitoring (TDM) and Bayesian forecasting approaches are still limited in individualized and dynamic vancomycin delivery. Until recently, there has not yet been enough focus on the direct integration of pharmacokinetic/pharmacodynamic (PK/PD) and TDM to construct a customized dose model (CDM) for vancomycin to achieve individualized, dynamic, and full-course dose prediction from empirical to follow-up treatment. This study sought to establish CDM for vancomycin, test its performance and superiority in clinical efficacy prediction, formulate a CDM-driven full-course dosage prediction strategy to overcome the above challenge, and predict the empirical vancomycin dosages for six Staphylococci populations and four strains in patients with various creatinine clearance rates (CLcr). Methods: The PK/PD and concentration models derived from our earlier research were used to establish CDM. The receiver operating characteristic (ROC) curve, with the area under ROC curve (AUCR) as the primary endpoint, for 21 retrospective cases was applied to test the performance and superiority of CDM in clinical efficacy prediction by comparison to the current frequently-used dose model (FDM). A model with an AUCR of at least 0.8 was considered acceptable. Based on the availability of TDM, the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for vancomycin therapy was formulated. Based on the CDM, Monte Carlo simulation was used to predict the empirical vancomycin dosages for the target populations and bacteria. Results: Four CDMs and the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for vancomycin therapy from empirical to follow-up treatment were constructed. Compared with FDM, CDM showed a greater AUCR value (0.807 vs. 0.688) in clinical efficacy prediction. The empirical vancomycin dosages for six Staphylococci populations and four strains in patients with various CLcr were predicted. Conclusion: CDM is a competitive individualized dose model. It compensates for the drawbacks of the existing TDM technology and Bayesian forecasting and offers a straightforward and useful supplemental approach for individualized and dynamic vancomycin delivery. Through mathematical modeling of the vancomycin dosage, this study achieved the goal of predicting doses individually, dynamically, and throughout, thus promoting "mathematical knowledge transfer and application" and also providing reference for quantitative and personalized research on similar drugs.

Accounting for cellular-level variation in lysis: implications for virus-host dynamics.

Viral impacts on microbial populations depend on interaction phenotypes-including viral traits spanning the adsorption rate, latent period, and burst size. The latent period is a key viral trait in lytic infections. Defined as the time from viral adsorption to viral progeny release, the latent period of bacteriophage is conventionally inferred via one-step growth curves in which the accumulation of free virus is measured over time in a population of infected cells. Developed more than 80 years ago, one-step growth curves do not account for cellular-level variability in the timing of lysis, potentially biasing inference of viral traits. Here, we use nonlinear dynamical models to understand how individual-level variation of the latent period impacts virus-host dynamics. Our modeling approach shows that inference of the latent period via one-step growth curves is systematically biased-generating estimates of shorter latent periods than the underlying population-level mean. The bias arises because variability in lysis timing at the cellular level leads to a fraction of early burst events, which are interpreted, artefactually, as an earlier mean time of viral release. We develop a computational framework to estimate latent period variability from joint measurements of host and free virus populations. Our computational framework recovers both the mean and variance of the latent period within simulated infections including realistic measurement noise. This work suggests that reframing the latent period as a distribution to account for variability in the population will improve the study of viral traits and their role in shaping microbial populations.IMPORTANCEQuantifying viral traits-including the adsorption rate, burst size, and latent period-is critical to characterize viral infection dynamics and develop predictive models of viral impacts across scales from cells to ecosystems. Here, we revisit the gold standard of viral trait estimation-the one-step growth curve-to assess the extent to which assumptions at the core of viral infection dynamics lead to ongoing and systematic biases in inferences of viral traits. We show that latent period estimates obtained via one-step growth curves systematically underestimate the mean latent period and, in turn, overestimate the rate of viral killing at population scales. By explicitly incorporating trait variability into a dynamical inference framework that leverages both virus and host time series, we provide a practical route to improve estimates of the mean and variance of viral traits across diverse virus-microbe systems.

Precision control and parameter optimization in screw extrusion 3D printing of polypropylene materials.

Fused Deposition Modeling (FDM), a widely-utilized additive manufacturing (AM) technology, has found significant favor among automotive manufacturers. Polypropylene (PP) compound is extensively employed in the production of automotive parts due to its superior mechanical properties and formability. However, aiming at the problem of poor dimensional accuracy of pure PP parts, the quality of products can be enhanced by optimizing the combination of processing parameters. In this paper, the dimensional accuracy of 3D-printed components made from pure PP material is investigated. Key influencing factors such as infill percentage, infill pattern, layer thickness, and extrusion temperature are considered. To gain a deeper understanding, fluid simulation is conducted, and mathematical models are established to correlate processing parameters with dimensional accuracy. Furthermore, the Taguchi's experiments are designed and the experimental data are subjected to rigorous Signal-to-Noise ratio and ANOVA analyses. Within the experimental range, the lower extrusion temperature, infill percentage and layer thickness yield the best dimensional accuracy. Considering the influence factors of X, Y and Z directions, the optimal processing parameters for PP prints using screw extrusion 3D printers are determined as follows: an extrusion temperature of 210 °C, an infill percentage of 40 %, a layer thickness of 0.3 mm, and a concentric circle infill pattern. This study provides reference value for the subsequent improvement of the dimensional accuracy of the printed parts.

Model simplification and sensitivity analysis of chemical species for oxidation behavior in multi route complex chemical mechanism.

This study employs a Model Reduction Technique (MRT) to simplify the four-step catalytic carbon monoxide (CO) oxidation reaction. The C-matrix method identifies key elements, key/non key components, and key reactions, while the Intrinsic Low-Dimensional Manifold (ILDM) pinpoints a Slow-Invariant Manifold (SIM) important for understanding key species behavior. Sensitivity analysis can be considered for measuring the efficiency of the chemical species in detailed mechanism. This systematic approach contributes to optimizing and controlling complex reactions offering broad application potential. In addition to the mathematical proof, the validation of the given chemical model is rectified. The comparison between the slow invariant manifold of both reaction routes is reported and the computational based results performed in this study are obtained through MATLAB.

External validation of a clinical mathematical model estimating post-operative urine output following cardiac surgery in children.

BACKGROUND: This study aims to externally validate a clinical mathematical model designed to predict urine output (UOP) during the initial post-operative period in pediatric patients who underwent cardiac surgery with cardiopulmonary bypass (CPB). METHODS: Children aged 0-18 years admitted to the pediatric cardiac intensive care unit at Cleveland Clinic Children's from April 2018 to April 2023, who underwent cardiac surgery with CPB were included. Patients were excluded if they had pre-operative kidney failure requiring kidney replacement therapy (KRT), re-operation or extracorporeal membrane oxygenation or KRT requirement within the first 32 post-operative hours or had indwelling urinary catheter for fewer than the initial 32 post-operative hours, or had vasoactive-inotrope score of 0, or those with missing data in the electronic health records. RESULTS: A total of 213 encounters were analyzed; median age (days): 172 (IQR 25-75th%: 51-1655), weight (kg): 6.1 (IQR 25-75th%: 3.8-15.5), median UOP ml/kg/hr in the first 32 post-operative hours: 2.59 (IQR 25-75th%: 1.93-3.26) and post-operative 30-day mortality: 1, (0.4%). The mathematical model achieved the following metrics in the entire dataset: mean absolute error (95th% Confidence Interval (CI)): 0.70 (0.67-0.73), median absolute error (95th% CI): 0.54 (0.52-0.56), mean squared error (95th% CI): 0.97 (0.89-1.05), root mean squared error (95th% CI): 0.99 (0.95-1.03) and R2 Score (95th% CI): 0.29 (0.24-0.34). CONCLUSIONS: This study provides encouraging external validation results of a mathematical model predicting post-operative UOP in pediatric cardiac surgery patients. Further multicenter studies must explore its broader applicability.

Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets.

Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.

Mathematical and sensitivity analysis for chemical species in multistep dynamical system: A computational study.

This study presents a significant contribution to the field of chemical kinetics by providing a detailed analysis of a multi-step chemical kinetic process using ordinary differential equations (ODEs). The aim is to describe complex chemical processes' kinetics and the steady-state behavior of chemical species. The research employs reduction techniques to simplify the model by separating fast and slow processes based on their time scales, with a focus on a two-step reversible reaction mechanism. Special consideration is given to the phase flow of solution trajectories near equilibrium points, providing a clear depiction of system behavior. MATLAB simulations demonstrate the physical properties of observed data, while sensitivity analysis reveals parameters' impact on species behavior. Overall, this study enhances our understanding of chemical kinetics and offers insights into modeling complex reaction processes, with implications for various applications in chemistry and related fields.

Impact of dosing schedules on performance of rotavirus vaccines in Ghana.

Background: Available live-oral rotavirus vaccines are associated with low to moderate performance in low- and middle-income settings. There is limited evidence relating to how the vaccine dosing schedule might be adjusted to improve vaccine performance in these settings. Methods: We used mathematical models fitted to rotavirus surveillance data for children <5 years of age from three different hospitals in Ghana (Korle-Bu Teaching Hospital in Accra, Komfo Anokye Teaching Hospital in Kumasi and War Memorial Hospital in Navrongo) to project the impact of rotavirus vaccination over a 10-year period (April 2012-March 2022). We quantified and compared the impact of the previous vaccination program in Ghana to the model-predicted impact for other vaccine dosing schedules across the three hospitals and the entire country, under different assumptions about vaccine protection. To project the rotavirus vaccine impact over Ghana, we sampled from the range of model parameters for Accra and Navrongo, assuming that these two settings represent the "extremes" of rotavirus epidemiology within Ghana. Results: For the previously implemented 6/10-week monovalent Rotarix vaccine (RV1) schedule, the model-estimated average annual incidence of moderate-to-severe rotavirus-associated gastroenteritis (RVGE) ranged between 1,151 and 3,002 per 100,000 people per year over the 10-year period for the three sites. Compared to no vaccination, the model-estimated median percentage reductions in RVGE ranged from 28-85% and 12-71% among children <1 year and <5 years of age respectively, with the highest and lowest percentage reductions predicted using model parameters estimated for Accra and Navrongo, respectively. The median predicted reductions in RVGE for the whole country ranged from 57-66% and 35-45% among children <1 year and <5 years of age, respectively. The 1/6/10- and 6/10/14-week schedules provided the best and comparable reductions in RVGE compared to the original 6/10-week schedule, whereas there was no improvement in impact for the 10/14-week schedule. Conclusions: We found that administering an additional dose of RV1 might be an effective strategy to improve rotavirus vaccine impact, particularly in settings with low vaccine effectiveness. The results could be extrapolated to other countries using a 2-dose vaccine schedule with low to moderate vaccine performance.

Assessing the impact of autologous virus neutralizing antibodies on viral rebound time in postnatally SHIV-infected ART-treated infant rhesus macaques.

While the benefits of early antiretroviral therapy (ART) initiation in perinatally infected infants are well documented, early initiation is not always possible in postnatal pediatric HIV infections. The timing of ART initiation is likely to affect the size of the latent viral reservoir established, as well as the development of adaptive immune responses, such as the generation of neutralizing antibody responses against the virus. How these parameters impact the ability of infants to control viremia and the time to viral rebound after ART interruption is unclear and has never been modeled in infants. To investigate this question we used an infant nonhuman primate Simian/Human Immunodeficiency Virus (SHIV) infection model. Infant Rhesus macaques (RMs) were orally challenged with SHIV.C.CH505 375H dCT and either given ART at 4-7 days post-infection (early ART condition), at 2 weeks post-infection (intermediate ART condition), or at 8 weeks post-infection (late ART condition). These infants were then monitored for up to 60 months post-infection with serial viral load and immune measurements. To gain insight into early after analytic treatment interruption (ATI), we constructed mathematical models to investigate the effect of time of ART initiation in delaying viral rebound when treatment is interrupted, focusing on the relative contributions of latent reservoir size and autologous virus neutralizing antibody responses. We developed a stochastic mathematical model to investigate the joint effect of latent reservoir size, the autologous neutralizing antibody potency, and CD4+ T cell levels on the time to viral rebound for RMs rebounding up to 60 days post-ATI. We find that the latent reservoir size is an important determinant in explaining time to viral rebound in infant macaques by affecting the growth rate of the virus. The presence of neutralizing antibodies can also delay rebound, but we find this effect for high potency antibody responses only. Finally, we discuss the therapeutic implications of our findings.

Building up a model family for inflammations.

The paper presents an approach for overcoming modeling problems of typical life science applications with partly unknown mechanisms and lacking quantitative data: A model family of reaction-diffusion equations is built up on a mesoscopic scale and uses classes of feasible functions for reaction and taxis terms. The classes are found by translating biological knowledge into mathematical conditions and the analysis of the models further constrains the classes. Numerical simulations allow comparing single models out of the model family with available qualitative information on the solutions from observations. The method provides insight into a hierarchical order of the mechanisms. The method is applied to the clinics for liver inflammation such as metabolic dysfunction-associated steatohepatitis or viral hepatitis where reasons for the chronification of disease are still unclear and time- and space-dependent data is unavailable.