BACKGROUND: The characterization and selection of heart failure (HF) patients for cardiac resynchronization therapy (CRT) remain challenging, with around 30% non-responder rate despite following current guidelines. This study aims to propose a novel hybrid approach, integrating machine-learning and personalized models, to identify explainable phenogroups of HF patients and predict their CRT response.
METHODS: The paper proposes the creation of a complete personalized model population based on preoperative CRT patient strain curves. Based on the parameters and features extracted from these personalized models, phenotypes of patients are identified thanks to a clustering algorithm and a random forest classification is provided.
RESULTS: A close match was observed between the 162 experimental and simulated myocardial strain curves, with a mean RMSE of 4.48% (±1.08) for the 162 patients. Five phenogroups of personalized models were identified from the clustering, with response rates ranging from 52% to 94%. The classification results show a mean area under the curves (AUC) of 0.86 ± 0.06 and provided a feature importance analysis with 22 features selected. Results show both regional myocardial contractility (from 22.5% to 33.0%), tissue viability and electrical activation delays importance on CRT response for each HF patient (from 55.8 ms to 88.4 ms).
CONCLUSIONS: The patient-specific model parameters\' analysis provides an explainable interpretation of HF patient phenogroups in relation to physiological mechanisms that seem predictive of the CRT response. These novel combined approaches appear as promising tools to improve understanding of LV mechanical dyssynchrony for HF patient characterization and CRT selection.

Background: Cardiac pacemaking remains an unsolved matter from many perspectives. Extensive experimental and computational studies have been performed to describe the sinoatrial physiology across different scales, from the molecular to clinical levels. Nevertheless, the mechanism by which a heartbeat is generated inside the sinoatrial node and propagated to the working myocardium is not fully understood at present. This work aims to provide quantitative information about this fascinating phenomenon, especially regarding the contributions of cellular heterogeneity and fibroblasts to sinoatrial node automaticity and atrial driving. Methods: We developed a bidimensional computational model of the human right atrial tissue, including the sinoatrial node. State-of-the-art knowledge of the anatomical and physiological aspects was adopted during the design of the baseline tissue model. The novelty of this study is the consideration of cellular heterogeneity and fibroblasts inside the sinoatrial node for investigating the manner by which they tune the robustness of stimulus formation and conduction under different conditions (baseline, ionic current blocks, autonomic modulation, and external high-frequency pacing). Results: The simulations show that both heterogeneity and fibroblasts significantly increase the safety factor for conduction by more than 10% in almost all the conditions tested and shorten the sinus node recovery time after overdrive suppression by up to 60%. In the human model, especially under challenging conditions, the fibroblasts help the heterogeneous myocytes to synchronise their rate (e.g. -82% in σ C L under 25 nM of acetylcholine administration) and capture the atrium (with 25% L-type calcium current block). However, the anatomical and gap junctional coupling aspects remain the most important model parameters that allow effective atrial excitations. Conclusion: Despite the limitations to the proposed model, this work suggests a quantitative explanation to the astonishing overall heterogeneity shown by the sinoatrial node.

BACKGROUND: Lyme disease continues to expand in Canada and the USA and no single intervention is likely to curb the epidemic.
METHODS: We propose a platform to quantitatively assess the effectiveness of a subset of Ixodes scapularis tick management approaches. The platform allows us to assess the impact of different control treatments, conducted either individually (single interventions) or in combination (combined efforts), with varying timings and durations. Interventions include three low environmental toxicity measures in differing combinations, namely reductions in white-tailed deer (Odocoileus virginianus) populations, broadcast area-application of the entomopathogenic fungus Metarhizium anisopliae, and fipronil-based rodent-targeted bait boxes. To assess the impact of these control efforts, we calibrated a process-based mathematical model to data collected from residential properties in the town of Redding, southwestern Connecticut, where an integrated tick management program to reduce I.xodes scapularis nymphs was conducted from 2013 through 2016. We estimated parameters mechanistically for each of the three treatments, simulated multiple combinations and timings of interventions, and computed the resulting percent reduction of the nymphal peak and of the area under the phenology curve.
RESULTS: Simulation outputs suggest that the three-treatment combination and the bait boxes-deer reduction combination had the overall highest impacts on suppressing I. scapularis nymphs. All (single or combined) interventions were more efficacious when implemented for a higher number of years. When implemented for at least 4 years, most interventions (except the single application of the entomopathogenic fungus) were predicted to strongly reduce the nymphal peak compared with the no intervention scenario. Finally, we determined the optimal period to apply the entomopathogenic fungus in residential yards, depending on the number of applications.
CONCLUSIONS: Computer simulation is a powerful tool to identify the optimal deployment of individual and combined tick management approaches, which can synergistically contribute to short-to-long-term, costeffective, and sustainable control of tick-borne diseases in integrated tick management (ITM) interventions.

BACKGROUND: Illicit opioid overdose continues to rise in North America and is a leading cause of death. Mathematical modeling is a valuable tool to investigate the epidemiology of this public health issue, as it can characterize key features of population outcomes and quantify the broader effect of structural and interventional changes on overdose mortality. The aim of this study is to quantify and predict the impact of key harm reduction strategies at differing levels of scale-up on fatal and nonfatal overdose among a population of people engaging in unregulated opioid use in Toronto.
METHODS: An individual-based model for opioid overdose was built featuring demographic and behavioural variation among members of the population. Key individual attributes known to scale the risk of fatal and nonfatal overdose were identified and incorporated into a dynamic modeling framework, wherein every member of the simulated population encompasses a set of distinct characteristics that govern demographics, intervention usage, and overdose incidence. The model was parametrized to fatal and nonfatal overdose events reported in Toronto in 2019. The interventions considered were opioid agonist therapy (OAT), supervised consumption sites (SCS), take-home naloxone (THN), drug-checking, and reducing fentanyl in the drug supply. Harm reduction scenarios were explored relative to a baseline model to examine the impact of each intervention being scaled from 0% use to 100% use on overdose events.
RESULTS: Model simulations resulted in 3690.6 nonfatal and 295.4 fatal overdoses, coinciding with 2019 data from Toronto. From this baseline, at full scale-up, 290 deaths were averted by THN, 248 from eliminating fentanyl from the drug supply, 124 from SCS use, 173 from OAT, and 100 by drug-checking services. Drug-checking and reducing fentanyl in the drug supply were the only harm reduction strategies that reduced the number of nonfatal overdoses.
CONCLUSIONS: Within a multi-faceted harm reduction approach, scaling up take-home naloxone, and reducing fentanyl in the drug supply led to the largest reduction in opioid overdose fatality in Toronto. Detailed model simulation studies provide an additional tool to assess and inform public health policy on harm reduction.

UNASSIGNED: The purpose of this study was to develop a simple flexible method for accurate estimation of stopping power ratio (SPR) and mean excitation energy (I) using relative electron density (ρ e).
UNASSIGNED: The model was formulated using empirical relationships between SPR, mean excitation energy I, and relative electron density. Some examples were implemented, and a comparison was carried out using other existing methods. The needed coefficients in the model were estimated using optimization tools. Basis vector method (BVM) and Hunemohr and Saito (H-S) method were applied to estimate the ρ e used in the application section. 80 kVp and 150 kVpSn were used as low and high energy, respectively, for the implementation of dual-energy methods.
UNASSIGNED: All the examples of the proposed method considered have modeling error that is ≤0.32% and testing root mean square error (RMSE) ≤0.92% for SPR with a mean error close to 0.00%. The method was able to achieve modeling RMSE of 2.12% for mean excitation energy with room for improvement. Similar or better results were achieved in application to BVM.
UNASSIGNED: The method showed robustness in application by achieving lower testing error than other presented methods in most cases. It achieved accurate estimation which can be improved using the machine learning algorithm since it is flexible to implement in terms of the function (model) degree and tissue classification.

This study introduces an SIRS compartmental mathematical model encompassing vaccination and variable immunity periods for infectious diseases. I derive a basic reproduction number formula and assess the local and global stability of disease-free and the local stability of the endemic equilibria. I demonstrate that the basic reproduction number in the presence of a vaccine is highly sensitive to the rate of immunity loss, and even a slight reduction in this rate can significantly contribute to disease control. Additionally, I have derived a formula to calculate the critical efficacy period required for a vaccine to effectively manage and control the disease.The analysis conducted for the model suggests that increasing the vaccine\'s immunity duration (efficacy) decelerates disease dynamics, leading to reduced rates of reinfection and less severe disease outcomes. Furthermore, this delay contributes to a decrease in the basic reproduction number ( R 0 ), thus facilitating more rapid disease control efforts.

We model interactions between cancer cells and viruses during oncolytic viral therapy. One of our primary goals is to identify parameter regions that yield treatment failure or success. We show that the tumor size under therapy at a particular time is less than the size without therapy. Our analysis demonstrates two thresholds for the horizontal transmission rate: a \"failure threshold\" below which treatment fails, and a \"success threshold\" above which infection prevalence reaches 100% and the tumor shrinks to its smallest size. Moreover, we explain how changes in the virulence of the virus alter the success threshold and the minimum tumor size. Our study suggests that the optimal virulence of an oncolytic virus depends on the timescale of virus dynamics. We identify a threshold for the virulence of the virus and show how this threshold depends on the timescale of virus dynamics. Our results suggest that when the timescale of virus dynamics is fast, administering a more virulent virus leads to a greater reduction in the tumor size. Conversely, when the viral timescale is slow, higher virulence can induce oscillations with high amplitude in the tumor size. Furthermore, we introduce the concept of a \"Hopf bifurcation Island\" in the parameter space, an idea that has applications far beyond the results of this paper and is applicable to many mathematical models. We elucidate what a Hopf bifurcation Island is, and we prove that small Islands can imply very slowly growing oscillatory solutions.

The closed-loop control of pathological brain activity is a challenging task. In this study, we investigated the sensitivity of continuous epileptiform short discharge generation to electrical stimulation applied at different phases between the discharges using an in vitro 4-AP-based model of epilepsy in rat hippocampal slices. As a measure of stimulation effectiveness, we introduced a sensitivity function, which we then measured in experiments and analyzed with different biophysical and abstract mathematical models, namely, (i) the two-order subsystem of our previous Epileptor-2 model, describing short discharge generation governed by synaptic resource dynamics; (ii) a similar model governed by shunting conductance dynamics (Epileptor-2B); (iii) the stochastic leaky integrate-and-fire (LIF)-like model applied for the network; (iv) the LIF model with potassium M-channels (LIF+KM), belonging to Class II of excitability; and (v) the Epileptor-2B model with after-spike depolarization. A semi-analytic method was proposed for calculating the interspike interval (ISI) distribution and the sensitivity function in LIF and LIF+KM models, which provided parametric analysis. Sensitivity was found to increase with phase for all models except the last one. The Epileptor-2B model is favored over other models for subthreshold oscillations in the presence of large noise, based on the comparison of ISI statistics and sensitivity functions with experimental data. This study also emphasizes the stochastic nature of epileptiform discharge generation and the greater effectiveness of closed-loop stimulation in later phases of ISIs.

Bladder cancer poses a significant global health burden with high incidence and recurrence rates. This study addresses the therapeutic challenges in advanced bladder cancer, focusing on the competitive mechanisms of ligand or drug binding to receptors. We developed a refined mathematical model that integrates the dynamics of tumor cells and immune responses, particularly targeting fibroblast growth factor receptor 3 (FGFR3) and immune checkpoint inhibitors (ICIs). This study contributes to understanding combination therapies by elucidating the competitive binding dynamics and quantifying the synergistic effects. The findings highlight the importance of personalized immunotherapeutic strategies, considering factors such as drug dosage, dosing schedules, and patient-specific parameters. Our model further reveals that ligand-independent activated-state receptors are the most essential drivers of tumor proliferation. Moreover, we found that PD-L1 expression rate was more important than PD-1 in driving the dynamic evolution of tumor and immune cells. The proposed mathematical model provides a comprehensive framework for unraveling the complexities of combination therapies in advanced bladder cancer. As research progresses, this multidisciplinary approach contributes valuable insights toward optimizing therapeutic strategies and advancing cancer treatment paradigms.

BACKGROUND: This study aimed to examine the transmission dynamics of Neisseria gonorrhoeae (NG) in heterosexual sex work networks (HSWNs) and the impact of variation in sexual behavior and interventions on NG epidemiology.
METHODS: The study employed an individual-based mathematical model to simulate NG transmission dynamics in sexual networks involving female sex workers (FSWs) and their clients, primarily focusing on the Middle East and North Africa region. A deterministic model was also used to describe NG transmission from clients to their spouses.
RESULTS: NG epidemiology in HSWNs displays two distinct patterns. In the common low-partner-number HSWNs, a significant proportion of NG incidence occurs among FSWs, with NG prevalence 13 times higher among FSWs than clients, and three times higher among clients than their spouses. Interventions substantially reduce incidence. Increasing condom use from 10 % to 50 % lowers NG prevalence among FSWs, clients, and their spouses from 12.2 % to 6.4 %, 1.2 % to 0.5 %, and 0.4 % to 0.2 %, respectively. Increasing symptomatic treatment coverage among FSWs from 0 % to 100 % decreases prevalence from 10.6 % to 4.5 %, 0.8 % to 0.4 %, and 0.3 % to 0.1 %, respectively. Increasing asymptomatic treatment coverage among FSWs from 0 % to 50 % decreases prevalence from 8.2 % to 0.4 %, 0.6 % to 0.1 %, and 0.2 % to 0.0 %, respectively, with very low prevalence when coverage exceeds 50 %. In high-partner-number HSWNs, prevalence among FSWs saturates at a high level, and the vast majority of incidence occurs among clients and their spouses, with a limited impact of incremental increases in interventions.
CONCLUSIONS: NG epidemiology in HSWNs is typically a \"fragile epidemiology\" that is responsive to a range of interventions even if the interventions are incremental, partially efficacious, and only applied to FSWs.