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CDK2 plays a pivotal role in controlling the progression of the cell cycle and is a target for anticancer drugs. The last 30 years of structural studies focused on CDK2 provided the basis for understanding its inhibition and furnished the data to develop machine-learning models to study intermolecular interactions. This review addresses the application of computational models to estimate the inhibition of CDK2. It focuses on machine-learning models developed to predict binding affinity against CDK2 using the program SAnDReS. A search of previously published articles on PubMed showed machine-learning models built to evaluate CDK2 inhibition. BindingDB information for CDK2 furnished the data to generate updated machine-learning models to predict the inhibition of this enzyme. The application of SAnDReS to model CDK2-inhibitor interactions showed that this approach can build machine-learning models with superior predictive performance compared with classical and deep-learning scoring functions. Also, the innovative DOME analysis of the predictive performance of machine learning and universal scoring function indicates that this method is adequate to select computational models to address protein-ligand interactions. The available structural and functional data about CDK2 is a rich source of information to build machine-learning models to predict the inhibition of this protein target. SAnDReS can build superior models to predict pKi and outperform universal scoring functions, including one developed using deep learning.
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OBJECTIVES: This in vitro study focused on verifying the influence of different ambient light conditions on the accuracy and precision of models obtained from digital scans. METHODOLOGY: To measure the tested illuminances: chair light/reflector; room light, and natural light at the time of scanning, a luxmeter was used. From the STL file, nine experimental groups were formed. RESULTS: Of the nine specific combinations between the three IOS and the three types of lighting, it was verified that for all of them, as well as the ICC, the accuracy was also excellent, in which the measured values were not significantly influenced by the IOS brand (p = 0.994) nor by the type of lighting (p = 0.996). For precision data, GLM indicated a statistically significant interaction between IOS and lighting type. Under LS, accuracy was significantly higher with 3Shape® than with CS 3600 CareStream®, which had significantly higher accuracy than Virtuo Vivo™ Straumann®. CONCLUSIONS: The models obtained with the three IOS evaluated exhibited excellent accuracy under the different illuminance tested and the 3Shape® under the three illuminance conditions was the device that presented the best precision, specifically when using LC and LS.
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Iluminação , Humanos , Técnicas In Vitro , Modelos Dentários , Luz , Reprodutibilidade dos TestesRESUMO
The muscle is the principal tissue that is capable to transform potential energy into kinetic energy. This process is due to the transformation of chemical energy into mechanical energy to enhance the movements and all the daily activities. However, muscular tissues can be affected by some pathologies associated with genetic alterations that affect the expression of proteins. As the muscle is a highly organized structure in which most of the signaling pathways and proteins are related to one another, pathologies may overlap. Duchenne muscular dystrophy (DMD) is one of the most severe muscle pathologies triggering degeneration and muscle necrosis. Several mathematical models have been developed to predict muscle response to different scenarios and pathologies. The aim of this review is to describe DMD and Becker muscular dystrophy in terms of cellular behavior and molecular disorders and to present an overview of the computational models implemented to understand muscle behavior with the aim of improving regenerative therapy.
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Distrofia Muscular de Duchenne , Distrofia Muscular de Duchenne/metabolismo , Distrofia Muscular de Duchenne/patologia , Humanos , Músculo Esquelético/metabolismo , Músculo Esquelético/patologia , Animais , Simulação por Computador , Modelos BiológicosRESUMO
BACKGROUND: Agave brittoniana subsp. brachypus is an endemic plant of Cuba, which contains different steroidal sapogenins with anti-inflammatory effects. This work aims to develop computational models which allow the identification of new chemical compounds with potential anti-inflammatory activity. METHODS: The in vivo anti-inflammatory activity was evaluated in two rat models: carrageenaninduced paw edema and cotton pellet-induced granuloma. In each study, we used 30 Sprague Dawley male rats divided into five groups containing six animals. The products isolated and administrated were fraction rich in yuccagenin and sapogenins crude. RESULTS: The obtained model, based on a classification tree, showed an accuracy value of 86.97% for the training set. Seven compounds (saponins and sapogenins) were identified as potential antiinflammatory agents in the virtual screening. According to in vivo studies, the yuccagenin-rich fraction was the greater inhibitor of the evaluated product from Agave. CONCLUSION: The evaluated metabolites of the Agave brittoniana subsp. Brachypus showed an interesting anti-inflammatory effect.
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Agave , Sapogeninas , Saponinas , Ratos , Animais , Sapogeninas/farmacologia , Agave/química , Ratos Sprague-Dawley , Saponinas/química , Saponinas/farmacologia , Anti-Inflamatórios/farmacologia , Anti-Inflamatórios/uso terapêutico , Extratos Vegetais/farmacologia , Extratos Vegetais/químicaRESUMO
Autoassociative neural networks provide a simple model of how memories can be stored through Hebbian synaptic plasticity as retrievable patterns of neural activity. Although progress has been made along the last decades in understanding the biological implementation of autoassociative networks, their modest theoretical storage capacity has remained a major constraint. While most previous approaches utilize randomly connected networks, here we explore the possibility of optimizing network performance by selective connectivity between neurons, that could be implemented in the brain through creation and pruning of synaptic connections. We show through numerical simulations that a reconfiguration of the connectivity matrix can improve the storage capacity of autoassociative networks up to one order of magnitude compared to randomly connected networks, either by reducing the noise or by making it reinforce the signal. Our results indicate that the signal-reinforcement scenario is not only the best performing but also the most adequate for brain-like highly diluted connectivity. In this scenario, the optimized network tends to select synapses characterized by a high consensus across stored patterns. We also introduced an online algorithm in which the network modifies its connectivity while learning new patterns. We observed that, similarly to what happens in the human brain, creation of connections dominated in an initial stage, followed by a stage characterized by pruning, leading to an equilibrium state that was independent of the initial connectivity of the network. Our results suggest that selective connectivity could be a key component to make attractor networks in the brain viable in terms of storage capacity.
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We propose herein a mathematical model to predict the COVID-19 evolution and evaluate the impact of governmental decisions on this evolution, attempting to explain the long duration of the pandemic in the 26 Brazilian states and their capitals well as in the Federative Unit. The prediction was performed based on the growth rate of new cases in a stable period, and the graphics plotted with the significant governmental decisions to evaluate the impact on the epidemic curve in each Brazilian state and city. Analysis of the predicted new cases was correlated with the total number of hospitalizations and deaths related to COVID-19. Because Brazil is a vast country, with high heterogeneity and complexity of the regional/local characteristics and governmental authorities among Brazilian states and cities, we individually predicted the epidemic curve based on a specific stable period with reduced or minimal interference on the growth rate of new cases. We found good accuracy, mainly in a short period (weeks). The most critical governmental decisions had a significant temporal impact on pandemic curve growth. A good relationship was found between the predicted number of new cases and the total number of inpatients and deaths related to COVID-19. In summary, we demonstrated that interventional and preventive measures directly and significantly impact the COVID-19 pandemic using a simple mathematical model. This model can easily be applied, helping, and directing health and governmental authorities to make further decisions to combat the pandemic.
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Atherosclerotic plaque rupture and erosion are the most important mechanisms underlying the sudden plaque growth, responsible for acute coronary syndromes and even fatal cardiac events. Advances in the understanding of the culprit plaque structure and composition are already reported in the literature, however, there is still much work to be done toward in-vivo plaque visualization and mechanical characterization to assess plaque stability, patient risk, diagnosis and treatment prognosis. In this work, a methodology for the mechanical characterization of the vessel wall plaque and tissues is proposed based on the combination of intravascular ultrasound (IVUS) imaging processing, data assimilation and continuum mechanics models within a high performance computing (HPC) environment. Initially, the IVUS study is gated to obtain volumes of image sequences corresponding to the vessel of interest at different cardiac phases. These sequences are registered against the sequence of the end-diastolic phase to remove transversal and longitudinal rigid motions prescribed by the moving environment due to the heartbeat. Then, optical flow between the image sequences is computed to obtain the displacement fields of the vessel (each associated to a certain pressure level). The obtained displacement fields are regarded as observations within a data assimilation paradigm, which aims to estimate the material parameters of the tissues within the vessel wall. Specifically, a reduced order unscented Kalman filter is employed, endowed with a forward operator which amounts to address the solution of a hyperelastic solid mechanics model in the finite strain regime taking into account the axially stretched state of the vessel, as well as the effect of internal and external forces acting on the arterial wall. Due to the computational burden, a HPC approach is mandatory. Hence, the data assimilation and computational solid mechanics computations are parallelized at three levels: (i) a Kalman filter level; (ii) a cardiac phase level; and (iii) a mesh partitioning level. To illustrate the capabilities of this novel methodology toward the in-vivo analysis of patient-specific vessel constituents, mechanical material parameters are estimated using in-silico and in-vivo data retrieved from IVUS studies. Limitations and potentials of this approach are exposed and discussed.
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Resumen Objetivo Tras las evidencias acumuladas mediante el uso de técnicas de angioplastia con stents, surge la polémica sobre los factores que inciden en la respuesta final, ya que hay estudios que reportan reestenosis de la luz en el 30-40% alrededor de 6 meses luego de ser implantados, vinculándose como una de las causas al diseño del dispositivo. Este artículo propone la caracterización funcional de stents endovasculares, analizando su influencia mecánica en el sistema vascular y prediciendo los factores de trauma implícitos en el lecho de los vasos. Métodos Utilizando modelos computacionales de prótesis endovasculares tipo stents, mediante técnicas Finite Elements Analysis, se procedió al análisis estructural de dichos dispositivos con el fin de predecir el comportamiento mecánico y el trauma vascular. Para ello, las prótesis fueron consideradas estructuras tubulares compuestas por múltiples eslabones que están sometidos a cargas de presión, que se reflejan como concentradores de esfuerzos. Resultados El estudio permitió visualizar cómo se ajusta la geometría del stent a las diferentes cargas, obteniéndose una aproximación a la respuesta de interacción "sólido-sólido" entre el stent y la pared arterial. Así, se caracterizó el patrón de esfuerzos y se planteó un modelo conceptual que explica su incidencia mecánica en la interacción stent-vaso, para inferir en la funcionalidad del diseño del dispositivo. Conclusiones El modelo conceptual planteado permite determinar la relación entre las condiciones de interacción mecánicas del stent, y advierte sobre los efectos en lo que sería la operación del dispositivo en el ambiente vascular.
Abstract Objective The accumulated evidence on angioplasty techniques with stents has raised a controversy about the factors that influence the final vascular response. Indeed, several studies have shown there might be re-stenosis between 30% to 40% about 6 months after placement, relating to the design of the device as one of the main causes. This paper proposes the functional characterization of endovascular stents, analyzing its mechanical influence in the vascular system and predicting implicit traumatic factors in the vessel. Methods A structural analysis was made for several computational models of endovascular stents using Finite Element Analysis in order to predict the mechanical behavior and the vascular trauma. In this way, the stents were considered as tubular devices composed of multiple links under radial pressure loads, reflecting stress concentration effects. Results The analysis allowed to visualize how the geometry of stents is adjusted under several load conditions, in order to obtain the response of "solid-solid" interaction between the stent and the arterial wall. Thus, an analysis was performed in order to calculate stress, and a conceptual model that explains its mechanical impact on the stent-vessel interaction, was raised, to infer on the functionality from the design of the devices. Conclusions The proposed conceptual model allows to determine the relationship between the conditions of mechanical interaction of the stents, and warns about the effects in what would be the operation of the device on the vascular environment.
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Humanos , Vasos Sanguíneos/lesões , Stents/efeitos adversos , Análise de Elementos Finitos , Modelos Cardiovasculares , Fenômenos BiomecânicosRESUMO
OBJECTIVE: The accumulated evidence on angioplasty techniques with stents has raised a controversy about the factors that influence the final vascular response. Indeed, several studies have shown there might be re-stenosis between 30% to 40% about 6 months after placement, relating to the design of the device as one of the main causes. This paper proposes the functional characterization of endovascular stents, analyzing its mechanical influence in the vascular system and predicting implicit traumatic factors in the vessel. METHODS: A structural analysis was made for several computational models of endovascular stents using Finite Element Analysis in order to predict the mechanical behavior and the vascular trauma. In this way, the stents were considered as tubular devices composed of multiple links under radial pressure loads, reflecting stress concentration effects. RESULTS: The analysis allowed to visualize how the geometry of stents is adjusted under several load conditions, in order to obtain the response of "solid-solid" interaction between the stent and the arterial wall. Thus, an analysis was performed in order to calculate stress, and a conceptual model that explains its mechanical impact on the stent-vessel interaction, was raised, to infer on the functionality from the design of the devices. CONCLUSIONS: The proposed conceptual model allows to determine the relationship between the conditions of mechanical interaction of the stents, and warns about the effects in what would be the operation of the device on the vascular environment.
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Vasos Sanguíneos/lesões , Análise de Elementos Finitos , Modelos Cardiovasculares , Stents/efeitos adversos , Fenômenos Biomecânicos , HumanosRESUMO
Fluorescent permeant charged probes are commonly used for monitoring the trans-membrane potential in lipid vesicles and biological membranes, which has been earlier described by various mathematical models. In the present study, we developed a more complex model based on the computational step-by-step analysis of the influence of various factors, such as the membrane surface potential, ionic strength, and the aggregation properties of cationic cyanine probe DiSC3(5) in the membrane and aqueous phases, in addition to the Nernstian distribution of the probe across the membrane and the hydrophobic interaction with the lipid bilayer. The final full model allows prediction of the optimal experimental conditions for monitoring the trans-membrane potential, such as the probe/lipid ratio and the concentration of liposomes, with a given percentage of negatively charged phospholipids in the membrane, the ionic strength of the aqueous media, the "membrane-water" partition coefficient and the aggregation properties of the probe, as well as the most adequate mode of fluorescence measurement. In agreement with many experimental studies, this model showed high voltage sensitivity of the quantity of the aqueous phase DiSC3(5) monomers, showing its almost exponential decrease with an increase in the trans-membrane potential value. The model also demonstrated the highest voltage sensitivity of the ratio of the quantity of DiSC3(5) monomers in the aqueous phases to that in the membrane phase. A new combined parameter, the logarithmic function of this ratio, demonstrated almost linear changes within a wide range of the trans-membrane potential changes.
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Benzotiazóis/metabolismo , Carbocianinas/metabolismo , Simulação por Computador , Corantes Fluorescentes/metabolismo , Potenciais da Membrana , Benzotiazóis/química , Carbocianinas/química , Membrana Celular/metabolismo , Dimerização , Corantes Fluorescentes/química , Lipossomos/metabolismoRESUMO
Os modelos computacionais do cérebro humano podem ser classificados em modelos para tratamento de dados (i.e. mapas de coordenadas cerebrais) e modelos de princípios operacionais (modelos conexionistas). A literatura relativa a ambos é vasta e muito variada, o que dificulta a realização de revisões sistemáticas. O objetivo do presente artigo é contribuir para o suprimento desta demanda e fornecer uma visão dos desenvolvimentos mais interessantes em andamento. Método: além dos métodos tradicionais de revisão, foi utilizado um programa para reconhecimento e quantificação de padrões em sistemas complexos (i.e. genomas). Resultados: entre os 2749 estudos do campo, aprendizado e memória são os tópicos mais recorrente, modelados em 0.14 e 0.9 dos estudos; Epilepsia, esquizofrenia e Parkinson são as doenças mais modeladas computacionalmente; Alzheimer é a quarta, sendo por sua vez mais relacionada à MRI (r=0.5435) do que à MEG (r=0.2033), PET (r=0.4718) e EEG (r=0.1766). Entre os 87 estudos 2007-2009 em modelos de princípios operacionais, o conceito mais proeminente é o de arquiteturas de pequenos mundos. O projeto mais excitante em desenvolvimento é o Blue Brain Project, que objetiva a criação de um modelo computacional do cérebro todo
Computational models of the human brain may be classified as data treatment models (brain maps) and operational-inspired models (connectionist models). The literature on both is extensive and varied, rendering difficulties to systematic reviews. This paper aims to contribute to outfit such demand, and to provide an overview of the most exciting projects currently under development. Method: besides traditional revising methods, a data mining program used to acknowledge patterns in complex databases (e.g. genomes) was used. Results: among the 2749 studies, learning and memory were the most recurrent topics, modeled in 0.14 and 0.9 of the studies. Epilepsy, schizophrenia and Parkinson are the most computationally simulated diseases; Alzheimer is the fourth, and more correlated to MRI (r=0.5435) than to MEG 9r=0.2033), PET (r=0.4718) and EEG (r=0.1766). Among the 87 2007-2009 studies on operational-inspired models, small world architectures is the most prominent concept. The most exciting project under development is the Blue Brain Project, which aims to provide a computational model of the whole brain
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Humanos , Psicologia , Simulação por Computador , Cérebro , Neuroanatomia , NeurofisiologiaRESUMO
Os modelos computacionais do cérebro humano podem ser classificados em modelos para tratamento de dados (i.e. mapas de coordenadas cerebrais) e modelos de princípios operacionais (modelos conexionistas). A literatura relativa a ambos é vasta e muito variada, o que dificulta a realização de revisões sistemáticas. O objetivo do presente artigo é contribuir para o suprimento desta demanda e fornecer uma visão dos desenvolvimentos mais interessantes em andamento. Método: além dos métodos tradicionais de revisão, foi utilizado um programa para reconhecimento e quantificação de padrões em sistemas complexos (i.e. genomas). Resultados: entre os 2749 estudos do campo, aprendizado e memória são os tópicos mais recorrente, modelados em 0.14 e 0.9 dos estudos; Epilepsia, esquizofrenia e Parkinson são as doenças mais modeladas computacionalmente; Alzheimer é a quarta, sendo por sua vez mais relacionada à MRI (r=0.5435) do que à MEG (r=0.2033), PET (r=0.4718) e EEG (r=0.1766). Entre os 87 estudos 2007-2009 em modelos de princípios operacionais, o conceito mais proeminente é o de arquiteturas de pequenos mundos. O projeto mais excitante em desenvolvimento é o Blue Brain Project, que objetiva a criação de um modelo computacional do cérebro todo.(AU)
Computational models of the human brain may be classified as data treatment models (brain maps) and operational-inspired models (connectionist models). The literature on both is extensive and varied, rendering difficulties to systematic reviews. This paper aims to contribute to outfit such demand, and to provide an overview of the most exciting projects currently under development. Method: besides traditional revising methods, a data mining program used to acknowledge patterns in complex databases (e.g. genomes) was used. Results: among the 2749 studies, learning and memory were the most recurrent topics, modeled in 0.14 and 0.9 of the studies. Epilepsy, schizophrenia and Parkinson are the most computationally simulated diseases; Alzheimer is the fourth, and more correlated to MRI (r=0.5435) than to MEG 9r=0.2033), PET (r=0.4718) and EEG (r=0.1766). Among the 87 2007-2009 studies on operational-inspired models, small world architectures is the most prominent concept. The most exciting project under development is the Blue Brain Project, which aims to provide a computational model of the whole brain.(AU)