Analysing vaccine efficacy evaluated in phase 3 clinical trials carried out during outbreaks.

In this paper we examine several definitions of vaccine efficacy (VE) that we found in the literature, for diseases that express themselves in outbreaks, that is, when the force of infection grows in time, reaches a maximum and then vanishes. The fact that the disease occurs in outbreaks results in several problems that we analyse. We propose a mathematical model that allows the calculation of VE for several scenarios. Vaccine trials usually needs a large number of volunteers that must be enrolled. Ideally, all volunteers should be enrolled in approximately the same time, but this is generally impossible for logistic reasons and they are enrolled in a fashion that can be replaced by a continuous density function (for example, a Gaussian function). The outbreak can also be replaced by a continuous density function, and the use of these density functions simplifies the calculations. Assuming, for example Gaussian functions, one of the problems one can immediately notice is that the peak of the two curves do not occur at the same time. The model allows us to conclude: First, the calculated vaccine efficacy decreases when the force of infection increases; Second, the calculated vaccine efficacy decreases when the gap between the peak in the force of infection and the peak in the enrollment rate increases; Third, different trial protocols can be simulated with this model; different vaccine efficacy definitions can be calculated and in our simulations, all result are approximately the same. The final, and perhaps most important conclusion of our model, is that vaccine efficacy calculated during outbreaks must be carefully examined and the best way we can suggest to overcome this problem is to stratify the enrolled volunteer's in a cohort-by-cohort basis and do the survival analysis for each cohort, or apply the Cox proportional hazards model for each cohort.

A Model of Linear Response with Progressive Decay Equally Suited for RiboGreen® Quantitation of RNA and Dissolution of Solid Oral Dosage Forms.

A new regression model is presented which offers flexibility, freedom from subjective determinations of linear range, and very wide applicability to measurement systems of industrial importance. This "progressive decay" model starts as a deceptively simple ordinary differential equation. We show here that its solution faithfully describes real but seemingly unconnected data from a plate-based assay for quantitation of RNA with RiboGreen® and dissolution data for a triple fixed-dose combination solid oral dosage form.

An Introduction to Plant Cell, Tissue, and Organ Culture: Current Status and Perspectives.

Plant cell, tissue, and organ cultures (PCTOC) have been used as experimental systems in basic research, allowing gene function demonstration through gene overexpression or repression and investigating the processes involved in embryogenesis and organogenesis or those related to the potential production of secondary metabolites, among others. On the other hand, PCTOC has also been applied at the commercial level for the vegetative multiplication (micropropagation) of diverse plant species, mainly ornamentals but also horticultural crops such as potato or fruit and tree species, and to produce high-quality disease-free plants. Moreover, PCTOC protocols are important auxiliary systems in crop breeding crops to generate pure lines (homozygous) to produce hybrids for the obtention of polyploid plants with higher yields or better performance. PCTOC has been utilized to preserve and conserve the germplasm of different crops or threatened species. Plant genetic improvement through genetic engineering and genome editing has been only possible thanks to the establishment of efficient in vitro plant regeneration protocols. Different companies currently focus on commercializing plant secondary metabolites with interesting biological activities using in vitro PCTOC. The impact of omics on PCTOC is discussed.

Correlation of rheology and oral tribology with sensory perception of commercial hazelnut and cocoa-based spreads.

This study examined the effects of spread formulation and the structural/lubricant properties of six different commercial hazelnut and cocoa spreads on sensory perception. Rheology, tribology, and quantitative descriptive analysis (QDA) was assessed by also evaluating the correlation coefficients between the quality descriptor and the rheological and textural parameters. The viscosity was evaluated at different temperatures to better simulate conditions before and after ingestion. Tribological analysis was executed at 37°C to mimic the human oral cavity. The effect of saliva presence and the number of runs on tribological behaviors was investigated. Moreover, textural, calorimetric, and particle size distribution measurements were performed to reinforce the correlation between structural/thermal parameters (e.g., firmness, stickiness, sugar melting point) and sensory aspects. "Visual viscosity," defined as a sensory attribute evaluated prior to consumption, negatively correlated with apparent viscosity measured at 20°C and 10 s-1, whereas "body," defined during oral processing and related to creaminess, positively correlated with apparent viscosity measured at 37°C and 50 s-1. These attributes were mainly influenced by particulate microstructure and solid volume fraction within the formulation. Textural stickiness positively correlated with sensory "adhesiveness" and was related to fat composition and milk powder addition, while "sweetness" was related to sucrose content and sugar melting enthalpy. Tribological data provided meaningful information related to particle-derived attributes, as well as after-coating perception (fattiness/oiliness), thus better predicting food evolution during oral consumption.

Modeling the transmission of antibiotic-resistant Enterobacterales in the community: A systematic review.

BACKGROUND: Antibiotic-resistant Enterobacterales (ARE) are a public health threat worldwide. Dissemination of these opportunistic pathogens has been largely studied in hospitals. Despite high prevalence of asymptomatic colonization in the community in some regions of the world, less is known about ARE acquisition and spread in this setting. As explaining the community ARE dynamics has not been straightforward, mathematical models can be key to explore underlying phenomena and further evaluate the impact of interventions to curb ARE circulation outside of hospitals. METHODS: We conducted a systematic review of mathematical modeling studies focusing on the transmission of AR-E in the community, excluding models only specific to hospitals. We extracted model features (population, setting), formalism (compartmental, individual-based), biological hypotheses (transmission, infection, antibiotic impact, resistant strain specificities) and main findings. We discussed additional mechanisms to be considered, open scientific questions, and most pressing data needs. RESULTS: We identified 18 modeling studies focusing on the human transmission of ARE in the community (n=11) or in both community and hospital (n=7). Models aimed at (i) understanding mechanisms driving resistance dynamics; (ii) identifying and quantifying transmission routes; or (iii) evaluating public health interventions to reduce resistance. To overcome the difficulty of reproducing observed ARE dynamics in the community using the classical two-strains competition model, studies proposed to include mechanisms such as within-host strain competition or a strong host population structure. Studies inferring model parameters from longitudinal carriage data were mostly based on models considering the ARE strain only. They showed differences in ARE carriage duration depending on the acquisition mode: returning travelers have a significantly shorter carriage duration than discharged hospitalized patient or healthy individuals. Interestingly, predictions across models regarding the success of public health interventions to reduce ARE rates depended on pathogens, settings, and antibiotic resistance mechanisms. For E. coli, reducing person-to-person transmission in the community had a stronger effect than reducing antibiotic use in the community. For Klebsiella pneumoniae, reducing antibiotic use in hospitals was more efficient than reducing community use. CONCLUSIONS: This study raises the limited number of modeling studies specifically addressing the transmission of ARE in the community. It highlights the need for model development and community-based data collection especially in low- and middle-income countries to better understand acquisition routes and their relative contribution to observed ARE levels. Such modeling will be critical to correctly design and evaluate public health interventions to control ARE transmission in the community and further reduce the associated infection burden.

Guidelines for Mechanistic Modeling and Analysis in Cardiovascular Research.

Computational, or in-silico, models are an effective, non-invasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in-silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data.

Deep brain stimulation pulse sequences to optimally modulate frequency-specific neural activity.

Objective. Precise neuromodulation systems are needed to identify the role of neural oscillatory dynamics in brain function and to advance the development of brain stimulation therapies tailored to each patient's signature of brain dysfunction. Low-frequency, local field potentials (LFPs) are of increasing interest for the development of these systems because they can reflect the synaptic inputs to a recorded neuronal population and can be chronically recorded in humans. In this computational study, we aim to identify stimulation pulse patterns needed to optimally maximize the suppression or amplification of frequency-specific neural activity.Approach. We derived DBS pulse patterns to minimize or maximize the 2-norm of frequency-specific neural oscillations using a generalized mathematical model of spontaneous and stimulation-evoked LFP activity, and a subject-specific model of neural dynamics in the pallidum of a Parkinson's disease patient. We leveraged convex and mixed-integer optimization tools to identify these pulse patterns, and employed constraints on the pulse frequency and amplitude that are required to keep electrical stimulation within its safety envelope.Main results. Our analysis revealed that a combination of phase, amplitude, and frequency pulse modulation is needed to attain optimal suppression or amplification of the targeted oscillations. Phase modulation is sufficient to modulate oscillations with a constant amplitude envelope. To attain optimal modulation for oscillations with a time-varying envelope, a trade-off between frequency and amplitude pulse modulation is needed. The optimized pulse sequences were invariant to changes in the dynamics of stimulation-evoked neural activity, including changes in damping and natural frequency or complexity (i.e. generalized vs. patient-specific model).Significance. Our results provide insight into the structure of pulse patterns for future closed-loop brain stimulation strategies aimed at controlling neural activity precisely and in real-time.

Experimentally validated modeling of dynamic drug-hERG channel interactions reproducing the binding mechanisms and its importance in action potential duration.

BACKGROUND AND OBJECTIVE: Assessment of drug cardiotoxicity is critical in the development of new compounds and modeling of drug-binding dynamics to hERG can improve early cardiotoxicity assessment. We previously developed a methodology to generate Markovian models reproducing preferential state-dependent binding properties, trapping dynamics and the onset of IKr block using simple voltage clamp protocols. Here, we test this methodology with real IKr blockers and investigate the impact of drug dynamics on action potential prolongation. METHODS: Experiments were performed on HEK cells stably transfected with hERG and using the Nanion SyncroPatch 384i. Three protocols, P-80, P0 and P 40, were applied to obtain the experimental data from the drugs and the Markovian models were generated using our pipeline. The corresponding static models were also generated and a modified version of the O´Hara-Rudy action potential model was used to simulate the action potential duration. RESULTS: The experimental Hill plots and the onset of IKr block of ten compounds were obtained using our voltage clamp protocols and the models generated successfully mimicked these experimental data, unlike the CiPA dynamic models. Marked differences in APD prolongation were observed when drug effects were simulated using the dynamic models and the static models. CONCLUSIONS: These new dynamic models of ten well-known IKr blockers constitute a validation of our methodology to model dynamic drug-hERG channel interactions and highlight the importance of state-dependent binding, trapping dynamics and the time-course of IKr block to assess drug effects even at the steady-state.

Prediction and control of cholera outbreak: Study case of Cameroon.

This paper deals with the problem of the prediction and control of cholera outbreak using real data of Cameroon. We first develop and analyze a deterministic model with seasonality for the cholera, the novelty of which lies in the incorporation of undetected cases. We present the basic properties of the model and compute two explicit threshold parameters R¯0 and R_0 that bound the effective reproduction number R0, from below and above, that is R_0≤R0≤R¯0. We prove that cholera tends to disappear when R¯0≤1, while when R_0>1, cholera persists uniformly within the population. After, assuming that the cholera transmission rates and the proportions of newly symptomatic are unknown, we develop the EnKf approach to estimate unmeasurable state variables and these unknown parameters using real data of cholera from 2014 to 2022 in Cameroon. We use this result to estimate the upper and lower bound of the effective reproduction number and reconstructed active asymptomatic and symptomatic cholera cases in Cameroon, and give a short-term forecasts of cholera in Cameroon until 2024. Numerical simulations show that (i) the transmission rate from free Vibrio cholerae in the environment is more important than the human transmission and begin to be high few week after May and in October, (ii) 90% of newly cholera infected cases that present the symptoms of cholera are not diagnosed and (iii) 60.36% of asymptomatic are detected at 14% and 86% of them recover naturally. The future trends reveals that an outbreak appeared from July to November 2023 with the number of cases reported monthly peaked in October 2023. An impulsive control strategy is incorporated in the model with the aim to avoid or prevent the cholera outbreak. In the first year of monitoring, we observed a reduction of more than 75% of incidences and the disappearance of the peaks when no control are available in Cameroon. A second monitoring of control led to a further reduction of around 60% of incidences the following year, showing how impulse control could be an effective means of eradicating cholera.

Utilization of diffusion-weighted derived mathematical models to predict prognostic factors of resectable rectal cancer.

PURPOSE: This study explored models of monoexponential diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), stretched exponential (SEM), fractional-order calculus (FROC), and continuous-time random-walk (CTRW) as diagnostic tools for assessing pathological prognostic factors in patients with resectable rectal cancer (RRC). METHODS: RRC patients who underwent radical surgery were included. The apparent diffusion coefficient (ADC), the mean kurtosis (MK) and mean diffusion (MD) from the DKI model, the distributed diffusion coefficient (DDC) and α from the SEM model, D, ß and u from the FROC model, and D, α and ß from the CTRW model were assessed. RESULTS: There were a total of 181 patients. The area under the receiver operating characteristic (ROC) curve (AUC) of CTRW-α for predicting histology type was significantly higher than that of FROC-u (0.780 vs. 0.671, p = 0.043). The AUC of CTRW-α for predicting pT stage was significantly higher than that of FROC-u and ADC (0.786 vs.0.683, p = 0.043; 0.786 vs. 0.682, p = 0.030), the difference in predictive efficacy of FROC-u between ADC and MK was not statistically significant [0.683 vs. 0.682, p = 0.981; 0.683 vs. 0.703, p = 0.720]; the difference between the predictive efficacy of MK and ADC was not statistically significant (p = 0.696). The AUC of CTRW (α + ß) (0.781) was significantly higher than that of FROC-u (0.781 vs. 0.625, p = 0.003) in predicting pN stage but not significantly different from that of MK (p = 0.108). CONCLUSION: The CTRW and DKI models may serve as imaging biomarkers to predict pathological prognostic factors in RRC patients before surgery.

Mosquito species identity matters: unraveling the complex interplay in vector-borne diseases.

BACKGROUND: Research on vector-borne diseases has traditionally centred on a limited number of vertebrate hosts and their associated pathogens, often neglecting the broader array of vectors within communities. Mosquitoes, with their vast species diversity, hold a central role in disease transmission, yet their capacity to transmit specific pathogens varies considerably among species. Quantitative modelling of mosquito-borne diseases is essential for understanding transmission dynamics and requires the necessity of incorporating the identity of vector species into these models. Consequently, understanding the role of different species of mosquitoes in modelling vector-borne diseases is crucial for comprehending pathogen amplification and spill-over into humans. This comprehensive overview highlights the importance of considering mosquito identity and emphasises the essential need for targeted research efforts to gain a complete understanding of vector-pathogen specificity. METHODS: Leveraging the recently published book, 'Mosquitoes of the World', I identified 19 target mosquito species in Europe, highlighting the diverse transmission patterns exhibited by different vector species and the presence of 135 medically important pathogens. RESULTS: The review delves into the complexities of vector-pathogen interactions, with a focus on specialist and generalist strategies. Furthermore, I discuss the importance of using appropriate diversity indices and the challenges associated with the identification of correct indices. CONCLUSIONS: Given that the diversity and relative abundance of key species within a community significantly impact disease risk, comprehending the implications of mosquito diversity in pathogen transmission at a fine scale is crucial for advancing the management and surveillance of mosquito-borne diseases.

Clinical implications of a mathematical-geometrical model to predict changes in the anterior arch dimension.

PURPOSE: Anterior arch length (AL) and the alterations in its dimension following incisor movements were shown to be predictable for an individual patient using a mathematical-geometrical model based on a third-degree parabola. Although the model has been validated previously, it is hard to apply in daily orthodontic routine. Thus, the aim of this study was to modify the model using different approaches to allow its establishment in daily routine. METHODS: This retrospective study was based on a study collective, which was described previously and consisted of 50 randomly chosen dental casts and lateral cephalograms taken before (T0) and after (T1) orthodontic treatment with fixed appliances. A JAVA computer program (Oracle, Austin, TX, USA) was developed to predict AL changes following therapeutic changes of arch width, depth or incisor inclination/position, taking the type of tooth movement into account. Performing exemplary AL calculations with the computer program, general rules and nomograms were set up, followed by multiple linear regression analyses to establish easy-to-use regression equations. RESULTS: The JAVA computer program is available for download. Sagittal changes showed more effect on AL than transverse modifications. Protruding incisors increased AL, but also reduced overbite. The extent of alteration in AL depended on the initial depth, width, incisor inclination, tooth movement type and distance between the incisal edge and the centre of rotation. CONCLUSIONS: The computer program precisely predicts individual changes in AL but is time-consuming. The presented regression equations and nomograms, considering metric variables, are easier to apply clinically and the differences compared to the AL calculated by the computer program are negligible.

Mathematical modeling of brain metastases growth and response to therapies: A review.

Brain metastases (BMs) are the most common intracranial tumor type and a significant health concern, affecting approximately 10% to 30% of all oncological patients. Although significant progress is being made, many aspects of the metastatic process to the brain and the growth of the resulting lesions are still not well understood. There is a need for an improved understanding of the growth dynamics and the response to treatment of these tumors. Mathematical models have been proven valuable for drawing inferences and making predictions in different fields of cancer research, but few mathematical works have considered BMs. This comprehensive review aims to establish a unified platform and contribute to fostering emerging efforts dedicated to enhancing our mathematical understanding of this intricate and challenging disease. We focus on the progress made in the initial stages of mathematical modeling research regarding BMs and the significant insights gained from such studies. We also explore the vital role of mathematical modeling in predicting treatment outcomes and enhancing the quality of clinical decision-making for patients facing BMs.

Relationship between body weight and hip width in dairy buffaloes (Bubalus bubalis).

The objective of the present study was to evaluate the relationship between body weight (BW) and hip width (HW) in dairy buffaloes (Bubalus bubalis). HW was measured in 215 Murrah buffaloes with a BW of 341 ± 161.6 kg, aged between three months and five years, and raised in southeastern Mexico. Linear and non-linear regressions were used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). Additionally, the developed models were evaluated through internal and external cross-validation (k-folds) using independent data. The ability of the fitted models to predict the observed values was assessed based on the root mean square error of prediction (RMSEP), R2, and mean absolute error (MAE). The relationship between BW and HW showed a high correlation coefficient (r = 0.96, P < 0.001). The chosen fitted model to predict BW was: -176.33 (± 40.83***) + 8.74 (± 1.79***) × HW + 0.04 (± 0.01*) × HW2, because it presented the lowest MSE, RMSE, and AIC values, which were 1228.64, 35.05 and 1532.41, respectively. Therefore, with reasonable accuracy, the quadratic model using hip width may be suitable for predicting body weight in buffaloes.

Mathematical models and analysis tools for risk assessment of unnatural epidemics: a scoping review.

Predicting, issuing early warnings, and assessing risks associated with unnatural epidemics (UEs) present significant challenges. These tasks also represent key areas of focus within the field of prevention and control research for UEs. A scoping review was conducted using databases such as PubMed, Web of Science, Scopus, and Embase, from inception to 31 December 2023. Sixty-six studies met the inclusion criteria. Two types of models (data-driven and mechanistic-based models) and a class of analysis tools for risk assessment of UEs were identified. The validation part of models involved calibration, improvement, and comparison. Three surveillance systems (event-based, indicator-based, and hybrid) were reported for monitoring UEs. In the current study, mathematical models and analysis tools suggest a distinction between natural epidemics and UEs in selecting model parameters and warning thresholds. Future research should consider combining a mechanistic-based model with a data-driven model and learning to pursue time-varying, high-precision risk assessment capabilities.

Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions?

Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets.

A computational model of Alzheimer's disease at the nano, micro, and macroscales.

Introduction: Mathematical models play a crucial role in investigating complex biological systems, enabling a comprehensive understanding of interactions among various components and facilitating in silico testing of intervention strategies. Alzheimer's disease (AD) is characterized by multifactorial causes and intricate interactions among biological entities, necessitating a personalized approach due to the lack of effective treatments. Therefore, mathematical models offer promise as indispensable tools in combating AD. However, existing models in this emerging field often suffer from limitations such as inadequate validation or a narrow focus on single proteins or pathways. Methods: In this paper, we present a multiscale mathematical model that describes the progression of AD through a system of 19 ordinary differential equations. The equations describe the evolution of proteins (nanoscale), cell populations (microscale), and organ-level structures (macroscale) over a 50-year lifespan, as they relate to amyloid and tau accumulation, inflammation, and neuronal death. Results: Distinguishing our model is a robust foundation in biological principles, ensuring improved justification for the included equations, and rigorous parameter justification derived from published experimental literature. Conclusion: This model represents an essential initial step toward constructing a predictive framework, which holds significant potential for identifying effective therapeutic targets in the fight against AD.

Smart Algorithms for Efficient Insulin Therapy Initiation in Individuals With Type 2 Diabetes: An in Silico Study.

BACKGROUND: Insulin-naive subjects with type 2 diabetes (T2D) start basal insulin titration from a low initial insulin dose (IID), which is adjusted weekly or twice per week based on fasting plasma glucose (FPG) measurement as recommended by the American Diabetes Association (ADA). The procedure to reach the optimal insulin dose (OID) is time-consuming, especially in subjects with high insulin needs (HIN). The aim of this study is to provide a fast and effective, but still safe, insulin titration algorithm in insulin-naive T2D subjects with HIN. METHOD: To do that, we in silico cloned 300 subjects, matching a real population of insulin-naive T2D and used a logistic regression model to classify them as subjects with HIN or subjects with low insulin needs (LIN). Then, we applied to the subjects with HIN both a more aggressive insulin dose initiation (SMART-IID) and two newly developed titration algorithms (continuous glucose monitoring [CGM]-BASED and SMART-CGM-BASED) in which CGM was used to guide the decision-making process. RESULTS: The new titration algorithm applied to HIN-classified individuals guaranteed a faster reaching of OID, with significant improvements in time in range (TIR) and reduction in time above range (TAR) in the first months of the trial, without any clinically significant increase in the risk of hypoglycemia. CONCLUSIONS: Smart basal insulin titration algorithms enable insulin-naive T2D individuals to achieve OID and improve their glycemic control faster than standard guidelines, without jeopardizing patient safety.

Predictive Models of Life Span in Old Female Mice Based on Immune, Redox, and Behavioral Parameters.

The development of mathematical models capable of predicting the lifespan of animals is growing. However, there are no studies that compare the predictive power of different sets of parameters depending on the age of the animals. The aim of the present study is to test whether mathematical models for life span prediction developed in adult female mice based on immune, redox, and behavioral parameters can predict life span in old animals and to develop new models in old mice. For this purpose, 29 variables, including parameters of immune function, redox state, and behavioral ones, were evaluated in old female Swiss mice (80 ± 4 weeks). Life span was registered when they died naturally. Firstly, we observed that the models developed in adults were not able to accurately predict the life span of old mice. Therefore, the immunity (adjusted R2 = 73.6%), redox (adjusted R2 = 46.5%), immunity-redox (adjusted R2 = 96.4%), and behavioral (adjusted R2 = 67.9%) models were developed in old age. Finally, the models were validated in another batch of mice. The developed models in old mice show certain similarities to those in adults but include different immune, redox, and behavioral markers, which highlights the importance of age in the prediction of life span.