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Bone mesenchymal come cellular material attenuate resiniferatoxin-induced neuralgia via inhibiting TRPA1-PKCδ-P38/MAPK-p-P65 walkway in these animals.
This process could be behind neurodegeneration in Alzheimer's and Parkinson's diseases, as well as behind the induced cell death during cancer treatment.Glucagon, the main hormone responsible for increasing blood glucose levels, is secreted by pancreatic alphacells in a Ca2+ dependent process associated to membrane potential oscillations developed by the dynamic operation of K+, Na+ and Ca2+ channels. The mechanisms behind membrane potential and Ca2+ oscillations in alpha-cells are still under debate, and some new research works have used alpha-cell models to describe electrical activity. In this paper we studied the dynamics of electrical activity of three alpha-cell models using the Lead Potential Analysis method and Bifurcation Diagrams. Our aim is to highlight the differences in their dynamic behavior and therefore, in their response to glucose. Both issues are relevant to understand the stimulus-secretion coupling in alpha-cells and then, the mechanisms behind their dysregulation in Type 2 Diabetes.Clinical Relevance - A reliable description of the electrophysiological mechanisms in pancreatic alpha-cells is key to understand and treat the dysregulation of these cells in patients with Type 2 Diabetes.3D scaffolds for tissue engineering typically need to adopt a dynamic culture to foster cell distribution and survival throughout the scaffold. It is, therefore, crucial to know fluids' behavior inside the scaffold architecture, especially for complex porous ones. Here we report a comparison between simulated and measured permeability of a porous 3D scaffold, focusing on different modeling parameters. The scaffold features were extracted by microcomputed tomography (µCT) and representative volume elements were used for the computational fluid-dynamic analyses. The objective was to investigate the sensitivity of the model to the degree of detail of the µCT image and the elements of the mesh. These findings highlight the pros and cons of the modeling strategy adopted and the importance of such parameters in analyzing fluid behavior in 3D scaffolds.Pain is a protective physiological system essential for survival. However, it can malfunction and create a debilitating disease known as chronic pain (CP), which is primarily treated with drugs that can produce negative side effects (e.g., opioid addiction). Peripheral nerve stimulation (PNS) is a promising alternative therapy; it has fewer negative side effects but has been associated with suboptimal efficacy since its mechanisms are unclear, and the current therapies are primarily open-loop (i.e. Selleck GS-4224 manual adjustment). To adapt to the needs of the user, the next step in advancing PNS therapies is to "close the loop" by using feedback to adjust the stimulation in real-time. A critical step in developing closed-loop PNS treatment is a deeper understanding of pain processing in the dorsal horn (DH) of the spinal cord, which is the first central relay station on the pain pathway. Mechanistic models of the DH have been developed to investigate modulation mechanisms but are non-linear, high-dimensional, and thus difficult to analyze. In this paper, we propose a novel application of structured uncertainty to model and analyze the nonlinear dynamical nature of the DH, and provide the foundation for developing robust PNS controllers using µ-synthesis. Using electrophysiological DH recordings from both naive and nerve-injured rats during windup stimulation, we build two separate models, which contains a linear time-invariant nominal (average) model, and structured uncertainty to quantify the nonlinear deviations in response from the nominal model. Using the structured uncertainty, we analyze the naive and injured models to discover underlying DH dynamics not identifiable using traditional methods, such as spike counting.Computation of Fractional Flow Reserve (FFR) through computational fluid dynamics (CFD) is used to guide intervention and often uses a number of clinically-derived metrics, but these patient-specific data could be costly and difficult to obtain. Understanding which parameters can be approximated from population averages and which parameters need to be patient-specific is important and remains largely unexplored. In this study, we performed a global sensitivity study on two 1D models of FFR to identify the most influential patient parameters. Our results indicated that vessel compliance, cardiac cycle period, flow rate, density, viscosity, and elastic modulus contributed minimally to the variance in FFR and may be approximated from population averages. On the other hand, outlet resistance (i.e., microvascular resistance), stenosis degree, and percent stenosis length contributed the most to FFR computation and needed to be tuned to the patient of interest. Selective measuring of patient-specific parameters may significantly reduce costs and streamline the simulation pipeline without reducing accuracy.Correct torquing of bone screws is important to prevent fixation failures and ensure positive patient outcomes. It has been proposed that an automatic model-based method may be able to determine the patient-specific material properties of bone, and provide objective and quantitative torquing recommendations. Models have been previously proposed for identifying the bone material properties, but have not been experimentally tested for accuracy. Here we used these models to perform parameter identification on experimental data using a variety of materials (rigid polyurethane foams) and screws. The identified values were then compared to the values from the datasheet, and matched with a reasonable accuracy for medium-density foam. It was found that for the lower-density foam, the model slightly under-predicted the strength, and for the highest density foam there was a large under-prediction. This suggests that with appropriate calibration, this method is good, but may only be applicable to lower-to-medium strength materials. More thorough testing is required to confirm this and determine the reliable density range.Clinical relevance Accurate material property identification is required to provide effective torque recommendations for bone screws. This work quantifies the accuracy of two proposed models for material property identification.Bone screws are used in orthopaedic procedures to fix implants and stabilise fractures. These procedures require care, as improperly torquing the screws can lead to implant failure or tissue damage, potentially requiring revision surgery or causing further disability. It was proposed that automated torque-limit identification may allow clinical decision support to control the screw torque, and lead to improved patient outcomes. This work extends a previous model of the screw insertion process to model complex thread geometries used for bone screws; consideration was made for the variable material properties and behaviours of bone to allow further tuning in the future. The new model was simulated and compared with the original model. The model was found to be in rough agreement with the earlier model, but was distinct, and could model thread features that the earlier model could not, such as the fillets and curves on the bone screw profile. The new model shows promise in modelling the more advanced thread geometries of bone screws with higher accuracy.Clinical relevance This work extends a self tapping screw model to support complex thread shapes, as common in bone screws, allowing more accurate modelling of the clinically relevant geometries.Correctly torquing bone screws is important to prevent fixation failures and ensure positive patient outcomes. It has been proposed that an automatic model-based method may be able to determine the patient-specific material properties of bone, and provide objective and quantitative torquing recommendations. One major part of developing this system is the modelling of the bone-screwing process, and the self-tapping screwing process in general. In this paper, we investigate the relationship between screw insertion torque (Nm) and speed of insertion (RPM). A weak positive correlation was found below approximately 30 RPM. Further research should focus on increasing the precision of the methodology, and this testing must be extended to ex-vivo animal bone testing in addition to the polyurethane foam substitute used here.Clinical relevance To maximise the accuracy of torque recommendations, the model should account for all important factors. This study investigates and attempts to quantify the relationship between screw insertion speed and torque for later inclusion in modelling if significant.Continuous glucose monitoring (CGM) sensors are minimally-invasive sensors used in diabetes therapy to monitor interstitial glucose concentration. The measurements are collected almost continuously (e.g. every 5 min) and permit the detection of dangerous hypo/hyperglycemic episodes. Modeling the various error components affecting CGM sensors is very important (e.g., to generate realistic scenarios for developing and testing CGM-based applications in type 1 diabetes simulators). In this work we focus on data gaps, which are portions of missing data due to a disconnection or a temporary sensor error. A dataset of 167 adults monitored with the Dexcom (San Diego, CA) G6 sensor is considered. After the evaluation of some statistics (the number of gaps for each sensor, the gap distribution over the monitoring days and the data gap durations), we develop a two-state Markov model to describe such statistics about data gap occurrence. Statistics about data gaps are compared between real data and simulated data generated by the model with a Monte Carlo simulation. Results show that the model describes quite accurately the occurrence and the duration of data gaps observed in real data.Doxorubicin (DOXO) is a well-established chemotherapy drug for treatment of different tumors, ranging from breast cancer, melanoma to multiple myeloma (MM). Here, we present a coupled experimental/modeling approach to study DOXO pharmacokinetics in MM cells, investigate its distribution among the extracellular and intracellular compartments during time. Three model candidates are considered and identified. Model selection is performed based on its ability to describe the data both qualitatively and in terms of quantitative indexes. The most parsimonious model consists of a nonlinear structure with a saturation-threshold control of intracellular DOXO efflux by the DOXO bound to the cellular DNA. This structure could explain the hypothesis that MM cells are drug-resistant, likely due to the involvement of P-glycoproteins.The proposed model is able to predict the intracellular (free and bound) DOXO and suggests the presence of a saturation-threshold drug-resistant mechanism.Clinical Relevance- The model can be used to properly understand and guide further experimental setup, e.g., to investigate multiple myeloma cell variability among different cell lines.SARS-CoV-2 has emerged to cause the outbreak of COVID-19, which has expanded into a worldwide human pandemic. Although detailed experimental data on animal experiments would provide insight into drug efficacy, the scientists involved in these experiments would be exposed to severe risks. In this context, we propose a computational framework for studying infection dynamics that can be used to capture the growth rate of viral replication and lung epithelial cell in presence of SARS-CoV-2. Specifically, we formulate the model consisting of a system of non-linear ODEs that can be used for visualizing the infection dynamics in a cell population considering the role of T cells and Macrophages. The major contribution of the proposed simulation method is to utilize the infection progression model in testing the efficacy of the drugs having various mechanisms and analyzing the effect of time of drug administration on virus clearance.Clinical Relevance-The proposed computational framework incorporates viral infection dynamics and role of immune response in Covid-19 that can be used to test the impact of drug efficacy and time of drug administration on infection mitigation.
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