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Retrieving information from memory enhances long-term retention. In this manuscript, we describe the dual-memory framework, which makes interval-scale predictions of the magnitude of this retrieval practice effect. After outlining the framework, we use data from our laboratory-both at the group level and at the distribution level-to fit the equations from the dual-memory framework. Overall, we successfully fitted the model predictions to the observed average data. In addition, we compared the predicted and the observed distributions of performance in the retrieval practice condition. More importantly, we introduce a useful approach to simulate empirical scenarios and test the relationship between individual-difference variables and the retrieval practice effect. We illustrate the application of this approach using data from a study that measured fluid intelligence. Future studies may benefit from contrasting different strength-based frameworks.

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Enterohepatic circulation (EHC) is a complex process where drugs undergo secretion and reabsorption from the intestinal lumen multiple times, resulting in pharmacokinetic profiles with multiple peaks. The impact of EHC on area under the curve (AUC) has been a topic of extensive debate, questioning the suitability of conventional AUC estimation methods. Moreover, a universal model for accurately estimating AUC in EHC scenarios is lacking. To address this gap, we conducted a simulation study evaluating five empirical models under various sampling strategies to assess their performance in AUC estimation. Our results identify the most suitable model for EHC scenarios and underscore the critical role of meal-based sampling strategies in accurate AUC estimation. Additionally, we demonstrate that while the trapezoidal method performs comparably to other models with a large number of samples, alternative models are essential when sample numbers are limited. These findings not only illuminate how EHC influences AUC but also pave the way for the application of empirical models in real-world drug studies.

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Chronic Myeloid Leukemia is a blood cancer for which standard therapy with Tyrosine-Kinase Inhibitors is successful in the majority of patients. After discontinuation of treatment half of the well-responding patients either present undetectable levels of tumor cells for a long time or exhibit sustained fluctuations of tumor load oscillating at very low levels. Motivated by the consequent question of whether the observed kinetics reflect periodic oscillations emerging from tumor-immune interactions, in this work, we analyze a system of ordinary differential equations describing the immune response to CML where both the functional response against leukemia and the immune recruitment exhibit optimal activation windows. Besides investigating the stability of the equilibrium points, we provide rigorous proofs that the model exhibits at least two types of bifurcations: a transcritical bifurcation around the tumor-free equilibrium point and a Hopf bifurcation around a biologically plausible equilibrium point, providing an affirmative answer to our initial question. Focusing our attention on the Hopf bifurcation, we examine the emergence of limit cycles and analyze their stability through the calculation of Lyapunov coefficients. Then we illustrate our theoretical results with numerical simulations based on clinically relevant parameters. Besides the mathematical interest, our results suggest that the fluctuating levels of low tumor load observed in CML patients may be a consequence of periodic orbits arising from predator-prey-like interactions.

Asunto(s)

Simulación por Computador , Leucemia Mielógena Crónica BCR-ABL Positiva , Conceptos Matemáticos , Leucemia Mielógena Crónica BCR-ABL Positiva/inmunología , Leucemia Mielógena Crónica BCR-ABL Positiva/tratamiento farmacológico , Leucemia Mielógena Crónica BCR-ABL Positiva/patología , Humanos , Modelos Inmunológicos , Inhibidores de Proteínas Quinasas/uso terapéutico , Inhibidores de Proteínas Quinasas/farmacología , Modelos Biológicos , Carga Tumoral/inmunología

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In this study, a model was developed to simulate the effect of temperature ( T $T$ ) and initial substrate concentration ( S 0 ${S}_{0}$ ) on the ethanol concentration limit ( P max ${P}_{\max }$ ) using the yeast Saccharomyces cerevisiae. To achieve this, regressions were performed using data provided by other authors for P max ${P}_{\max }$ to establish a model dependent on T $T$ and S 0 ${S}_{0}$ capable of predicting results with statistical significance. After constructing the model, a response surface was generated to determine the conditions where P max ${P}_{\max }$ reaches higher values: temperatures between 28°C and 32°C and an initial substrate concentration around 200 g/L. Thus, the proposed model is consistent with the observations that increasing temperatures decrease the ethanol concentration obtained, and substrate concentrations above 200 g/L lead to a reduction in ethanol concentration even at low temperatures such as 28°C.

Asunto(s)

Etanol , Modelos Biológicos , Saccharomyces cerevisiae , Temperatura , Saccharomyces cerevisiae/metabolismo , Etanol/metabolismo , Fermentación

RESUMEN

Pterocaulon polystachyum is a species of pharmacological interest for providing volatile and non-volatile extracts with antifungal and amebicidal properties. The biological activities of non-volatile extracts may be related to the presence of coumarins, a promising group of secondary metabolites. In the present study, leaves and inflorescences previously used for the extraction of essential oils instead of being disposed of were subjected to extraction with supercritical CO2 after pretreatment with microwaves. An experimental design was followed to seek the best extraction condition with the objective function being the maximum total extract. Pressure and temperature were statistically significant factors, and the optimal extraction condition was 240 bar, 60 °C, and pretreatment at 30 °C. The applied mathematical models showed good adherence to the experimental data. The extracts obtained by supercritical CO2 were analyzed and the presence of coumarins was confirmed. The extract investigated for cytotoxicity against bladder tumor cells (T24) exhibited significant reduction in cell viability at concentrations between 6 and 12 µg/mL. The introduction of green technology, supercritical extraction, in the exploration of P. polystachyum as a source of coumarins represents a paradigm shift with regard to previous studies carried out with this species, which used organic solvents. Furthermore, the concept of circular bioeconomy was applied, i.e., the raw material used was the residue of a steam-distillation process. Therefore, the approach used here is in line with the sustainable exploitation of native plants to obtain extracts rich in coumarins with cytotoxic potential against cancer cells.

Asunto(s)

Dióxido de Carbono , Cromatografía con Fluido Supercrítico , Cumarinas , Extractos Vegetales , Cumarinas/química , Cumarinas/aislamiento & purificación , Cumarinas/farmacología , Dióxido de Carbono/química , Extractos Vegetales/química , Extractos Vegetales/farmacología , Extractos Vegetales/aislamiento & purificación , Humanos , Cromatografía con Fluido Supercrítico/métodos , Componentes Aéreos de las Plantas/química , Línea Celular Tumoral , Supervivencia Celular/efectos de los fármacos , Antineoplásicos Fitogénicos/farmacología , Antineoplásicos Fitogénicos/química , Antineoplásicos Fitogénicos/aislamiento & purificación

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Generally, medicinal plants are harvested with high amount of water, so it is essential to subject the product to drying as soon as possible to prevent degradation before application. Most compounds from medicinal plants are sensitive to drying processes, so it is important to adjust the drying conditions. The objective of this study was to describe the drying of Rue (Ruta chalepensis L.) leaves, select the models that best fit each drying condition, determine the activation energy and thermodynamic properties of the leaves, and evaluate their quality after drying. Leaves were harvested with moisture content of 3.55 ± 0.05 kg water kg-1dry matter and subjected to drying at temperatures of 40, 50, 60 and 70 °C. Valcam model showed the best fit to represent the drying kinetics of Rue leaves at temperatures of 40 and 70 °C, and Midilli model proved to be better for the temperatures of 50 and 60 °C. Effective diffusion coefficient increased linearly with the increase in drying air temperature, and the activation energy was 60.58 kJ mol-1. Enthalpy, entropy and Gibbs free energy values ranged from 57.973 to 57.723 kJ mol-1, from - 0.28538 to - 0.28614 kJ mol-1 K-1 and from 147.34 to 155.91 kJ mol-1, respectively, for the temperature range of 40-70 °C. Drying air temperature promoted darkening or tendency to loss of green color; increase in drying air temperature leads to greater discoloration, as well as a higher concentration of total phenolic compounds (about 221.10 mg GAE mL-1 g-1 dm), with a peak at temperature of 60 °C.

Asunto(s)

Desecación , Hojas de la Planta , Termodinámica , Hojas de la Planta/química , Cinética , Desecación/métodos , Temperatura , Agua/química , Plantas Medicinales/química

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In this paper, we presented a mathematical model for tuberculosis with treatment for latent tuberculosis cases and incorporated social implementations based on the impact they will have on tuberculosis incidence, cure, and recovery. We incorporated two variables containing the accumulated deaths and active cases into the model in order to study the incidence and mortality rate per year with the data reported by the model. Our objective is to study the impact of social program implementations and therapies on latent tuberculosis in particular the use of once-weekly isoniazid-rifapentine for 12 weeks (3HP). The computational experimentation was performed with data from Brazil and for model calibration, we used the Markov Chain Monte Carlo method (MCMC) with a Bayesian approach. We studied the effect of increasing the coverage of social programs, the Bolsa Familia Programme (BFP) and the Family Health Strategy (FHS) and the implementation of the 3HP as a substitution therapy for two rates of diagnosis and treatment of latent at 1% and 5%. Based of the data obtained by the model in the period 2023-2035, the FHS reported better results than BFP in the case of social implementations and 3HP with a higher rate of diagnosis and treatment of latent in the reduction of incidence and mortality rate and in cases and deaths avoided. With the objective of linking the social and biomedical implementations, we constructed two different scenarios with the rate of diagnosis and treatment. We verified with results reported by the model that with the social implementations studied and the 3HP with the highest rate of diagnosis and treatment of latent, the best results were obtained in comparison with the other independent and joint implementations. A reduction of the incidence by 36.54% with respect to the model with the current strategies and coverage was achieved, and a greater number of cases and deaths from tuberculosis was avoided.

Asunto(s)

Antituberculosos , Teorema de Bayes , Isoniazida , Tuberculosis Latente , Cadenas de Markov , Conceptos Matemáticos , Método de Montecarlo , Rifampin , Humanos , Brasil/epidemiología , Incidencia , Isoniazida/administración & dosificación , Antituberculosos/administración & dosificación , Rifampin/administración & dosificación , Rifampin/análogos & derivados , Rifampin/uso terapéutico , Tuberculosis Latente/epidemiología , Tuberculosis Latente/tratamiento farmacológico , Tuberculosis Latente/mortalidad , Modelos Biológicos , Tuberculosis/mortalidad , Tuberculosis/epidemiología , Tuberculosis/tratamiento farmacológico , Simulación por Computador

RESUMEN

BACKGROUND AND OBJECTIVES: Laser ablation is increasingly used to treat atrial fibrillation (AF). However, atrioesophageal injury remains a potentially serious complication. While proactive esophageal cooling (PEC) reduces esophageal injury during radiofrequency ablation, the effects of PEC during laser ablation have not previously been determined. We aimed to evaluate the protective effects of PEC during laser ablation of AF by means of a theoretical study based on computer modeling. METHODS: Three-dimensional mathematical models were built for 20 different cases including a fragment of atrial wall (myocardium), epicardial fat (adipose tissue), connective tissue, and esophageal wall. The esophagus was considered with and without PEC. Laser-tissue interaction was modeled using Beer-Lambert's law, Pennes' Bioheat equation was used to compute the resultant heating, and the Arrhenius equation was used to estimate the fraction of tissue damage (FOD), assuming a threshold of 63% to assess induced necrosis. We modeled laser irradiation power of 8.5 W over 20 s. Thermal simulations extended up to 250 s to account for thermal latency. RESULTS: PEC significantly altered the temperature distribution around the cooling device, resulting in lower temperatures (around 22°C less in the esophagus and 9°C in the atrial wall) compared to the case without PEC. This thermal reduction translated into the absence of transmural lesions in the esophagus. The esophagus was thermally damaged only in the cases without PEC and with a distance equal to or shorter than 3.5 mm between the esophagus and endocardium (inner boundary of the atrial wall). Furthermore, PEC demonstrated minimal impact on the lesion created across the atrial wall, either in terms of maximum temperature or FOD. CONCLUSIONS: PEC reduces the potential for esophageal injury without degrading the intended cardiac lesions for a variety of different tissue thicknesses. Thermal latency may influence lesion formation during laser ablation and may play a part in any collateral damage.

Asunto(s)

Fibrilación Atrial , Ablación por Catéter , Terapia por Láser , Humanos , Esófago/cirugía , Esófago/lesiones , Esófago/patología , Atrios Cardíacos/cirugía , Fibrilación Atrial/cirugía , Rayos Láser , Computadores , Ablación por Catéter/métodos

Asunto(s)

Criptococosis/clasificación , Micosis/tratamiento farmacológico , Micosis/complicaciones

RESUMEN

The monkeypox virus (MPXV) has caused an unusual epidemiological scenario-an epidemic within a pandemic (COVID-19). Despite the inherent evolutionary and adaptive capacity of poxviruses, one of the potential triggers for the emergence of this epidemic was the change in the status of orthopoxvirus vaccination and eradication programs. This epidemic outbreak of HMPX spread worldwide, with a notable frequency in Europe, North America, and South America. Due to these particularities, the objective of the present study was to assess and compare cases of HMPX in these geographical regions through logistic and Gompertz mathematical modeling over one year since its inception. We estimated the highest contagion rates (people per day) of 690, 230, 278, and 206 for the world, Europe, North America, and South America, respectively, in the logistic model. The equivalent values for the Gompertz model were 696, 268, 308, and 202 for the highest contagion rates. The Kruskal-Wallis Test indicated different means among the geographical regions affected by HMPX regarding case velocity, and the Wilcoxon pairwise test indicated the absence of significant differences between the case velocity means between Europe and South America. The coefficient of determination (R2) values in the logistic model varied from 0.8720 to 0.9023, and in the Gompertz model, they ranged from 0.9881 to 0.9988, indicating a better fit to the actual data when using the Gompertz model. The estimated basic reproduction numbers (R0) were more consistent in the logistic model, varying from 1.71 to 1.94 in the graphical method and from 1.75 to 1.95 in the analytical method. The comparative assessment of these mathematical modeling approaches permitted the establishment of the Gompertz model as the better-fitting model for the data and the logistic model for the R0. However, both models successfully represented the actual HMPX case data. The present study estimated relevant epidemiological data to understand better the geographic similarities and differences in the dynamics of HMPX.

RESUMEN

This article presents an improved mathematical model and numerical simulation for weathering of large areas with complex topography. It uses the equations of momentum, temperature, and humidity in turbulent air and for heat and water infiltration into soils. A mathematical model is also presented to calculate the soil porosity fraction produced by physical rock weathering in areas where soil is produced from intrusive rocks (batholiths). An algorithm based on air velocity, humidity (rainfall), temperature variation, and soil topography was developed to quantify soil erosion and change of relief at each point and time step in air, at the ground surface, and within the soil. This results in a complete air-soil model based on conservation laws that have not previously been applied to large areas of the earth's surface. The mathematical model is solved using large-scale numerical simulations applied to an area of 6.6 km2 in the Sierra Nevada batholith of California, USA. The results show that the wind velocity and resulting erosion is greater in areas with steeper slopes and that moisture accumulates mainly in low and flat areas; therefore, erosion is not uniform throughout the study area. In addition, computer simulations localized calculations to discrete grid cells within the porous (saprolite) fraction of the soil produced by freezing and thawing of water in rock. Results indicate that this physical mechanism is the primary contributor to weathering of rock at the study area.

RESUMEN

In the present work, the inhibitory effect of the peptide fractions, obtained through enzymatic hydrolysis of bovine plasma was evaluated, on the enzyme used in the reaction (Alcalase 2.4 L). In this sense, Ultra-filtered peptide fractions of different molecular sizes (A: Fraction>10; B: Fraction 10-3 kDa; and C: Fraction <3 kDa), were used to verify the impact on the total hydrolysis rate. The Fractions between 3 and 10 kDa were refined to fit a conceptual kinetic model which considers inhibition by product and substrate. Additionally, the inactivation of the enzyme through the reaction time was evaluated and its effects incorporated into the model. It was shown that some peptides released in the successive stages of the reaction can in turn inhibit the activity of the hydrolyzing enzyme. The model evaluated suggests a time-varying expression of inhibition parameters as a function of the initial substrate concentration in the reaction. This is based on the kinetic changes of the product profiles for each reaction time in the evaluated operating conditions (S0 variable). A greater inhibitory effect due to the products is evidenced when the reaction occurs with a higher load of the initial substrate (S0 = 20 g/L).

RESUMEN

Sharka is a disease affecting stone fruit trees. It is caused by the Plum pox virus (PPV), with Myzus persicae being one of the most efficient aphid species in transmitting it within and among Prunus orchards. Other agricultural management strategies are also responsible for the spread of disease among trees, such as grafting and pruning. We present a mathematical model of impulsive differential equations to represent the dynamics of Sharka disease in the tree and vector population. We consider three transmission routes: grafting, pruning, and through aphid vectors. Grafting, pruning, and vector control occur as pulses at specific instants. Within the model, human risk perception towards disease influences these agricultural management strategies. Model results show that grafting with infected biological material has a significant impact on the spread of the disease. In addition, detecting infectious symptomatic and asymptomatic trees in the short term is critical to reduce disease spread. Furthermore, vector control to prevent aphid movement between trees is crucial for disease mitigation, as well as implementing awareness campaigns for Sharka disease in agricultural communities that provide a long-term impact on responsible pruning, grafting, and vector control.

RESUMEN

The incidence of cancer has been constantly growing worldwide, placing pressure on health systems and increasing the costs associated with the treatment of cancer. In particular, low- and middle-income countries are expected to face serious challenges related to caring for the majority of the world's new cancer cases in the next 10 years. In this study, we propose a mathematical model that allows for the simulation of different strategies focused on public policies by combining spending and epidemiological indicators. In this way, strategies aimed at efficient spending management with better epidemiological indicators can be determined. For validation and calibration of the model, we use data from Colombia-which, according to the World Bank, is an upper-middle-income country. The results of the simulations using the proposed model, calibrated and validated for Colombia, indicate that the most effective strategy for reducing mortality and financial burden consists of a combination of early detection and greater efficiency of treatment in the early stages of cancer. This approach is found to present a 38% reduction in mortality rate and a 20% reduction in costs (% GDP) when compared to the baseline scenario. Hence, Colombia should prioritize comprehensive care models that focus on patient-centered care, prevention, and early detection.

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Crispness is a textural characteristic that influences consumer choices, requiring a comprehensive understanding for product customization. Previous studies employing neural networks focused on acquiring audio through mechanical crushing of crispy samples. This research investigates the representation of crispy sound in time intervals and frequency domains, identifying key parameters to distinguish different foods. Two machine learning architectures, multi-layer perceptron (MLP) and residual neural network (ResNet), were used to analyze mel frequency cepstral coefficients (MFCC) and discrete Fourier transform (DFT) data, respectively. The models achieved over 95% accuracy "in-sample" successfully classifying fried chicken, potato chips, and toast using randomly extracted audio from ASMR videos. The MLP (MFCC) model demonstrated superior robustness compared to ResNet and predicted external inputs, such as freshly toasted bread acquired by a microphone or ASMR audio of toast in milk. In contrast, the ResNet model proved to be more responsive to variations in DFT spectrum and unable to predict the similarity of external audio sources, making it useful for classifying pretrained "in-samples". These findings are useful for classifying crispness among individual food sources. Additionally, the study explores the promising utilization of ASMR audio from Internet platforms to pretrain artificial neural network models, expanding the dataset for investigating the texture of crispy foods.

Asunto(s)

Aprendizaje Automático , Redes Neurales de la Computación , Pan , Sonido

RESUMEN

Double strand break (DSB) repair is critical to maintaining the integrity of the genome. DSB repair deficiency underlies multiple pathologies, including cancer, chromosome instability syndromes, and, potentially, neurodevelopmental defects. DSB repair is mainly handled by two pathways: highly accurate homologous recombination (HR), which requires a sister chromatid for template-based repair, limited to S/G2 phases of the cell cycle, and canonical non-homologous end joining (c-NHEJ), available throughout the cell cycle in which minimum homology is sufficient for highly efficient yet error-prone repair. Some circumstances, such as cancer, require alternative highly mutagenic DSB repair pathways like microhomology-mediated end-joining (MMEJ) and single-strand annealing (SSA), which are triggered to attend to DNA damage. These non-canonical repair alternatives are emerging as prominent drivers of resistance in drug-based tumor therapies. Multiple DSB repair options require tight inter-pathway regulation to prevent unscheduled activities. In addition to this complexity, epigenetic modifications of the histones surrounding the DSB region are emerging as critical regulators of the DSB repair pathway choice. Modeling approaches to understanding DSBs repair pathway choice are advantageous to perform simulations and generate predictions on previously uncharacterized aspects of DSBs response. In this work, we present a Boolean network model of the DSB repair pathway choice that incorporates the knowledge, into a dynamic system, of the inter-pathways regulation involved in DSB repair, i.e., HR, c-NHEJ, SSA, and MMEJ. Our model recapitulates the well-characterized HR activity observed in wild-type cells in response to DSBs. It also recovers clinically relevant behaviors of BRCA1/FANCS mutants, and their corresponding drug resistance mechanisms ascribed to DNA repair gain-of-function pathogenic variants. Since epigenetic modifiers are dynamic and possible druggable targets, we incorporated them into our model to better characterize their involvement in DSB repair. Our model predicted that loss of the TIP60 complex and its corresponding histone acetylation activity leads to activation of SSA in response to DSBs. Our experimental validation showed that TIP60 effectively prevents activation of RAD52, a key SSA executor, and confirms the suitable use of Boolean network modeling for understanding DNA DSB repair.

Asunto(s)

Daño del ADN , Reparación del ADN , Ciclo Celular , Mutagénesis , División Celular

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Electrochemotherapy (ECT) and Irreversible electroporation (IRE) are cancer treatments based on electric field distribution in tissues. Solanum tuberosum (potato tissue) phantom is known to mimic changes in the electrical conductivity that occur in animal tissues during electroporation (EP). Electric field distribution is assessed through enzymatic staining. However, the 24-h wait for this assessment could slow agile response scenarios. We developed and validated the Musa acuminata (cavendish banana) conductivity model, which quickly evaluates EP by tissue staining. We investigated the frequency response of the tissue using impedance spectroscopy analysis, conductivity changes, and enzymatic staining. We optimized three usual EP models: adapted Gompertz, smoothed Heaviside, and the sigmoid or logistic function. We found dielectric parameters in banana tissue similar to those in potato (electrical conductivity of 0.035 S/m and relative permittivity of 4.1×104). The coefficients of determination R2 were 99.94% (Gompertz), 99.85% (Heaviside), and 99.58% (sigmoid). The sigmoid and Heaviside functions described the calibration and validation electric currents with 95% confidence. We observed the electroporated areas in bananas 3h30m after EP. Staining was significant after 450 V/cm. The conductivity model of Musa acuminata suits treatment planning, hardware development, and training scenarios. Banana phantom supports the 3Rs practice and is a reliable alternative for potato in EP studies.

Asunto(s)

Electroquimioterapia , Musa , Animales , Terapia de Electroporación , Electroporación , Conductividad Eléctrica

RESUMEN

RESUMEN INTRODUCCIÓN Los modelos matemáticos de la transmisión de enfermedades infecciosas permiten estudiar distintos mecanismos que afectan su comportamiento temporal. Este trabajo analizó el efecto sobre la dinámica de la influenza y el virus sincitial respiratorio (VSR) de la disminución de la transmisibilidad debida a las medidas de cuidado adoptadas para reducir la circulación de COVID-19. MÉTODOS Se empleó un modelo determinista tipo SIRS (susceptible-infectado-recuperado-susceptible) con modulación estacional para representar la influenza y el VSR, en ambos casos con inmunidad de corta duración y ciclo anual. Los cambios en la transmisibilidad de la enfermedad se modelaron reduciéndola durante dos años y planteando distintos escenarios. RESULTADOS En el modelo planteado, la reducción en la transmisibilidad genera cambios que se sostienen en los años siguientes: eventos epidémicos muy pronunciados con alargamiento del intervalo interbrote. Este efecto resulta dominante respecto del comportamiento estacional. El escenario de una reducción inicial de la transmisibilidad del 40% resulta compatible con el comportamiento de influenza y VSR reportados actualmente para Argentina. DISCUSIÓN El modelo general propuesto, en condiciones de disminución transitoria en la transmisibilidad, exhibe una epidemiología compatible con la observada recientemente en Argentina para ambas enfermedades e ilustra el modelado como herramienta útil en la comprensión de efectos no intuitivos.

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ABSTRACT INTRODUCTION Mathematical models of infectious diseases transmission allow to study different mechanisms which affect their temporal behavior. This work analyzed the impact of the decrease in transmissibility, as a result of measures of personal care adopted to reduce circulation of COVID-19, on the dynamics of influenza and respiratory syncytial virus (RSV). METHODS A deterministic SIRS (susceptible-infected-recovered-susceptible) model with seasonal modulation was used to represent two diseases with short-term immunity and annual cycle: influenza and RSV. Changes in disease transmissibility were modeled by reducing it for two years and analyzing different scenarios. RESULTS In the proposed model, transmissibility reduction brings changes which sustain in the following years: very pronounced epidemic events with lengthening of the inter-outbreak interval. This effect prevails over the seasonal behavior. The scenario of 40% initial reduction in transmissibility is compatible with the behavior of influenza and RSV currently reported in Argentina. DISCUSSION The general model proposed here, under conditions of temporary reduced transmissibility, shows an epidemiology compatible with recently reported data of influenza and RSV in Argentina. This result illustrates modeling as a useful tool to understand non-intuitive effects.

RESUMEN

Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is generally neglected when the number of experimental observations is restricted, obtaining multiple solutions or results without physiological justification. This work proposes a fitting and validation strategy for physiological models with many parameters under various populations, stimuli, and experimental conditions. A cardiorespiratory system model is used as a case study, and the strategy, model, computational implementation, and data analysis are described. Using optimized parameter values, model simulations are compared to those obtained using nominal values, with experimental data as a reference. Overall, a reduction in prediction error is achieved compared to that reported for model building. Furthermore, the behavior and accuracy of all the predictions in the steady state were improved. The results validate the fitted model and provide evidence of the proposed strategy's usefulness.

RESUMEN

BACKGROUND: In Brazil, institutional foodservices are required to meet the recommendations of the Workers? Food Program (WFP), a national public policy used to plan collective menus. The current study aimed to propose a mathematical model to generate a one-month menu that meets the nutritional recommendations of the WFP, with low cost and good quality. METHODS: We considered aspects related to the eating habits of the Brazilian population, spacing of repetitions between the dishes, texture combination, and monotonicity of colors of the dishes served. A mixed integer programming model was built to formulate daily menus for an institutional foodservice for one month. The menu consisted of a base dish, a base dish option, salads (2 options), a protein dish, a protein dish option, a side dish, and a dessert. RESULTS: The model ensured compliance with the recommendations proposed by the WFP and the provision of healthy and nutritionally balanced meals. The menu generated met the recommendations of the WFP, with an average of 716.97 kcal/meal, including on average 58.28% carbohydrates, 17.89% proteins, and 24.88% total fats/meal. CONCLUSION: The model used can help in the menu elaboration dynamics of institutional foodservices, optimizing the work of the nutritionist in charge.