Acute Myocardial Infarction (AMI) is the most common cause of mortality, and cardiac troponin I (cTnI) is the gold standard for AMI diagnosis. Some aptamers have been reported to detect cTnI, but there is rarely any s...
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Acute Myocardial Infarction (AMI) is the most common cause of mortality, and cardiac troponin I (cTnI) is the gold standard for AMI diagnosis. Some aptamers have been reported to detect cTnI, but there is rarely any structural information on their binding with cTnI. There are many studies involving electrochemical aptasensors with methylene blue (MB) as a redox indicator that does not provide any significant new information. Our study, however, involves the computational modeling of the complexes of a documented 40-mer ssDNA-cTnI aptamer with its target and MB. Here, MB was used as a model system to investigate the rather complex interaction modes between small redox molecules and a cTnI-aptamer. Understanding these modes is an important facet in designing electrochemical aptasensors to ascertain the effectiveness of a redox indicator. In our study, docking and molecular dynamics (MD) were used to provide information on the stability, fluctuations, and interaction analysis of the complexes. Based on computational evaluations, we understand how the structure of the cTnI-aptamer steers the MB releasing after inducing cTnI. In order to evaluate the sensing performance of the cTnI-aptamer in the presence of MB experimentally, after covalent immobilization of the 5-amino terminated cTnI-aptamer at a pre-functionalized carboxylated multi-walled carbon nanotube (COOH-MWCNT) glassy carbon electrode, some techniques including spectroscopy and voltammetry were employed.
作者：
Auzmendi, JeronimoMoffatt, LucianoRamos, Alberto JavierUniv Buenos Aires
Facultad MedicinaLaboratorio Neuropatol Mol Robertis" UBACONICET Paraguay 2155 3er piso (1121) Buenos Aires Argentina Univ Buenos Aires
Facultad Ciencias Exactas Naturales Instituto Quim Fis Materiales Medio Ambiente EnergiaCONICET Ricardo Guiraldes 2160 (1428) Buenos Aires Argentina
Reactive astrogliosis occurs upon focal brain injury and in neurodegenerative diseases. The mechanisms that propagate reactive astrogliosis to distal parts of the brain, in a rapid wave that activates astrocytes and o...
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Reactive astrogliosis occurs upon focal brain injury and in neurodegenerative diseases. The mechanisms that propagate reactive astrogliosis to distal parts of the brain, in a rapid wave that activates astrocytes and other cell types along the way, are not completely understood. It is proposed that damage-associated molecular patterns (DAMP) released by necrotic cells from the injury core have a major role in the reactive astrogliosis initiation but whether they also participate in reactive astrogliosis propagation remains to be determined. We here developed a Bayesian computational model to define the most probable model for reactive astrogliosis propagation. Starting with experimental data from GFAP-immunostained reactive astrocytes, we defined five types of astrocytes based on morphometrical cues and registered the position of each reactive astrocyte cell type in the hemisphere ipsilateral to the injured site after 3 and 7 days post-ischemia. We developed equations for the changes in DAMP concentration (due to diffusion, binding to receptors or degradation), soluble mediators secretion, and for the evolution reactive astrogliosis. We tested four predefined models based on abovementioned previous hypothesis and modifications to it. Our results showed that DAMP diffusion alone has not justified the reactive astrogliosis propagation as previously assumed. Only two models succeeded in accurately reproducing the experimentally measured data and they highlighted the role of microglia and the glial secretion of soluble mediators to sustain the reactive signal and activating neighboring astrocytes. Thus, our in silico analysis proposes that glial cells behave as repeater stations of the injury signal in order to propagate reactive astrogliosis.
Radiomics is an emerging field that involves extraction and quantification of features from medical images. These data can be mined through computational analysis and models to identify predictive image biomarkers tha...
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Radiomics is an emerging field that involves extraction and quantification of features from medical images. These data can be mined through computational analysis and models to identify predictive image biomarkers that characterize intra-tumoral dynamics throughout the course of treatment. This is particularly difficult in gliomas, where heterogeneity has been well established at a molecular level as well as visually in conventional imaging. Thus, acquiring clinically useful features remains difficult due to temporal variations in tumor dynamics. Identifying surrogate biomarkers through radiomics may provide a non-invasive means of characterizing biologic activities of gliomas. We present an extensive literature review of radiomics-based analysis, with a particular focus on computational modeling, machine learning, and fractal-based analysis in improving differential diagnosis and predicting clinical outcomes. Novel strategies in extracting quantitative features, segmentation methods, and their clinical applications are producing promising results. Moreover, we provide a detailed summary of the morphometric parameters that have so far been proposed as a means of quantifying imaging characteristics of gliomas. Newly emerging radiomic techniques via machine learning and fractal-based analyses holds considerable potential for improving diagnostic and prognostic accuracy of gliomas. Key points center dot Radiomic features can be mined through computational analysis to produce quantitative imaging biomarkers that characterize intra-tumoral dynamics throughout the course of treatment. center dot Surrogate image biomarkers identified through radiomics could enable a non-invasive means of characterizing biologic activities of gliomas. center dot With novel analytic algorithms, quantification of morphological or sub-regional tumor features to predict survival outcomes is producing promising results. center dot Quantifying intra-tumoral heterogeneity may improve grading and m
The leukotriene B-4 receptors BLT1 and BLT2 are promising targets for the treatment of allergic and inflammatory diseases. However, no working model of ligand binding to either of these receptors has been developed so...
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The leukotriene B-4 receptors BLT1 and BLT2 are promising targets for the treatment of allergic and inflammatory diseases. However, no working model of ligand binding to either of these receptors has been developed so far. Under the assumption that homologous receptors bind their ligands in a similar way, computational modeling of agonist binding to BLT1 and BLT2 was performed using fully flexible docking in Galaxy7TM. For both receptors, the carboxyl group of the ligand forms a salt bridge with an arginine residue, while the tail hydroxyl groups form hydrogen bonds with three amino acid residues. The differential specificity of ligands to BLT1 and BLT2 is explained by the replacement of histidine with tyrosine. In BLT1, the histidine residue binds the 5-OH group of the ligand, while the tyrosine residue in BLT2 repels it. The presented models are in agreement with experimental data and may be useful for developing new BLT1- and BLT2-targeted drugs.
This study focuses on science teachers' first encounter with computational modeling in professional development workshops. It examines the factors shaping the teachers' self-efficacy and attitudes towards inte...
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This study focuses on science teachers' first encounter with computational modeling in professional development workshops. It examines the factors shaping the teachers' self-efficacy and attitudes towards integrating computational modeling within inquiry-based learning modules for 9th grade physics. The learning modules introduce phenomena, the analysis of measurement data, and offer a method for coordinating the experimental findings with a theory-based computational model. Teachers' attitudes and self-efficacy were studied using survey questions and workshop activity transcripts. As expected, prior experience in physics teaching was related to teachers' self-efficacy in teaching physics in 9th grade. Also, teachers' prior experience with programming was strongly related to their self-efficacy regarding the programming component of model construction. Surprisingly, the short interaction with computational modeling increased the group's self-efficacy, and the average rating of understanding and enjoyment was similar among teachers with and without prior programming experience. Qualitative data provides additional insights into teachers' predispositions towards the integration of computational modeling into the physics teaching.
Atrial fibrillation(AF)is one of the most common arrhythmias,associated with high morbidity,mortality,and healthcare costs,and it places a significant burden on both individuals and ***-arrhythmic drugs are the most c...
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Atrial fibrillation(AF)is one of the most common arrhythmias,associated with high morbidity,mortality,and healthcare costs,and it places a significant burden on both individuals and ***-arrhythmic drugs are the most commonly used strategy for treating ***,drug therapy faces challenges because of its limited efficacy and potential side *** ablation is widely used as an alternative treatment for ***,because the mechanism of AF is not fully understood,the recurrence rate after ablation remains *** addition,the outcomes of ablation can vary significantly between medical institutions and patients,especially for persistent ***,the issue of which ablation strategy is optimal is still far from *** modeling has the advantages of repeatable operation,low cost,freedom from risk,and complete control,and is a useful tool for not only predicting the results of different ablation strategies on the same model but also finding optimal personalized ablation targets for clinical reference and even *** review summarizes three-dimensional computational modeling simulations of catheter ablation for AF,from the early-stage attempts such as Maze III or circumferential pulmonary vein isolation to the latest advances based on personalized substrate-guided ***,we summarize current developments and challenges and provide our perspectives and suggestions for future directions.
Perceivable objects are customarily termed as concepts and their representations (localist-distributed, modality-specific, or experience-dependent) are ingrained in our lives. Despite a considerable amount of computat...
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Perceivable objects are customarily termed as concepts and their representations (localist-distributed, modality-specific, or experience-dependent) are ingrained in our lives. Despite a considerable amount of computational modeling research focuses on concrete concepts, no comprehensible method for abstract concepts has hitherto been considered. concepts can be viewed as a blend of concrete concepts. We use this view in our proposed model, Regulated Activation Network (RAN), by learning representations of non-convex abstract concepts without supervision via a hybrid model that has an evolving topology. First, we describe the RAN's modeling process through a Toy-data problem yielding a performance of 98.5%(ca.) in a classification task. Second, RAN's model is used to infer psychological and physiological biomarkers from students' active and inactive states using sleep-detection data. The RAN's capability of performing classification is shown using five UCI benchmarks, with the best outcome of 96.5% (ca.) for Human Activity recognition data. We empirically demonstrate the proposed model using standard performance measures for classification and establish RAN's competency with five classifiers. We show that the RAN adeptly performs classification with a small amount of data and simulate cognitive functions like activation propagation and learning.
Transcranial Direct-Current Stimulation (tDCS), as a safe and non-invasive neuromodulator, has been recently the center of attention for the treatment and management of neurological disorders. However, the intervening...
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Transcranial Direct-Current Stimulation (tDCS), as a safe and non-invasive neuromodulator, has been recently the center of attention for the treatment and management of neurological disorders. However, the intervening mechanisms of tDCS are not completely understood, and in consequence, it was not possible or advisable to utilize it as an effective treatment. In this work, by integrating a neural mass model of the thalamocortical system, which can generate and induce different types of seizures, and the physiological aspects of the interaction between tDCS and the brain, such as permissible current and how it must be involved in different neural populations, we have proposed a basic model. Then, by connecting several units of the basic model and applying a learning rule to calculate properly the connectivity weights between units, a multi-zone model is established. Our simulation results explained the ever-increasing connectivity between pre-and post-stimulation, an accepted feature of tDCS. Then, we described computationally (based on bifurcation analysis) how this change in connectivity can lead to a reduction in epileptic susceptibility.
As more people suffering from mobility impairments, there are growing needs for rehabilitation. Rehabilitation robots have been proven to be effective in assisting patients in their rehabilitation process. However, ma...
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ISBN:
(纸本)9781665463812
As more people suffering from mobility impairments, there are growing needs for rehabilitation. Rehabilitation robots have been proven to be effective in assisting patients in their rehabilitation process. However, many existing rehabilitation robots are costly so the accessibility to the patients in need is limited. Our team has been working on a proof-of-concept soft robot design that could be used for finger rehabilitation. Our eventual goal for this soft robot concept is to lower the barrier to access rehabilitation. Hence, our soft robot is designed unlike most other existing soft robots, which are actuated by external components. Our soft robot is completely untethered and actuated by heat. Using a phase changing material (PCM) sealed in elastomer compartments as the working fluid for our soft robot, when heat is applied, the PCM begins to change phase and the pressure inside the sealed elastomer compartments increases to bend our soft robot. Because the bending of our soft robot is closely related to the pressure increase inside of the elastomer compartments, we use computational modeling to investigate that correlation. In this paper, we present the CFD (computational Fluid Dynamics) and FEA (Finite Element Analysis) simulation models we studied. CFD simulation helps us investigate how the pressure to increase inside of the elastomer compartments when PCM changes phase and FEA modeling further identifies the bending angles for a given pressure inside of the elastomer compartments. These modeling results would aid the development of our soft robot prototypes in the future.
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