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BACKGROUND: Computational research methods, such as finite element analysis (FEA) and musculoskeletal multi-body simulation (MBS), are important in musculoskeletal biomechanics because they enable a better understanding of the mechanics of the musculoskeletal system, as well as the development and evaluation of orthopaedic implants. These methods are used to analyze clinically relevant issues in various anatomical regions, such as the hip, knee, shoulder joints and spine. Preoperative simulation can improve surgical planning in orthopaedics and predict individual results. EXAMPLES FROM PRACTICE: In this article, the methods of FE analysis and MBS are explained using two practical examples, and the activities of the "Numerical Simulation" cluster of the "Musculoskeletal Biomechanics Research Network (MSB-NET)" are presented in more detail. An outlook classifies numerical simulation in the age of artificial intelligence and draws attention to the relevance of simulation in the (re)approval of implants.

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From the molecular level up to a blood vessel, thrombosis and hemostasis involves many interconnected biochemical and biophysical processes over a wide range of length and time scales. Computational modeling has gained eminence in offering insights into these processes beyond what can be obtained from in vitro or in vivo experiments, or clinical measurements. The multiscale and multiphysics nature of thrombosis has inspired a wide range of modeling approaches that aim to address how a thrombus forms and dismantles. Here, we review recent advances in computational modeling with a focus on platelet-based thrombosis. We attempt to summarize the diverse range of modeling efforts straddling the wide-spectrum of physical phenomena, length scales, and time scales; highlighting key advancements and insights from existing studies. Potential information gleaned from models is discussed, ranging from identification of thrombus-prone regions in patient-specific vasculature to modeling thrombus deformation and embolization in response to fluid forces. Furthermore, we highlight several limitations of current models, future directions in the field, and opportunities for clinical translation, to illustrate the state-of-the-art. There are a plethora of opportunity areas for which models can be expanded, ranging from topics of thromboinflammation to platelet production and clearance. Through successes demonstrated in existing studies described here, as well as continued advancements in computational methodologies and computer processing speeds and memory, in silico investigations in thrombosis are poised to bring about significant knowledge growth in the years to come.

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Abstract: Severe acute respiratory infection (SARI) outbreaks occur annually, with seasonal peaks varying among geographic regions. Case notification is important to prepare healthcare networks for patient attendance and hospitalization. Thus, health managers need adequate resource planning tools for SARI seasons. This study aims to predict SARI outbreaks based on models generated with machine learning using SARI hospitalization notification data. In this study, data from the reporting of SARI hospitalization cases in Brazil from 2013 to 2020 were used, excluding SARI cases caused by COVID-19. These data were prepared to feed a neural network configured to generate predictive models for time series. The neural network was implemented with a pipeline tool. Models were generated for the five Brazilian regions and validated for different years of SARI outbreaks. By using neural networks, it was possible to generate predictive models for SARI peaks, volume of cases per season, and for the beginning of the pre-epidemic period, with good weekly incidence correlation (R2 = 0.97; 95%CI: 0.95-0.98, for the 2019 season in the Southeastern Brazil). The predictive models achieved a good prediction of the volume of reported cases of SARI; accordingly, 9,936 cases were observed in 2019 in Southern Brazil, and the prediction made by the models showed a median of 9,405 (95%CI: 9,105-9,738). The identification of the period of occurrence of a SARI outbreak is possible using predictive models generated with neural networks and algorithms that employ time series.

Resumo: Surtos de síndrome respiratória aguda grave (SRAG) ocorrem anualmente, com picos sazonais variando entre regiões geográficas. A notificação dos casos é importante para preparar as redes de atenção à saúde para o atendimento e internação dos pacientes. Portanto, os gestores de saúde precisam ter ferramentas adequadas de planejamento de recursos para as temporadas de SRAG. Este estudo tem como objetivo prever surtos de SRAG com base em modelos gerados com aprendizado de máquina usando dados de internação por SRAG. Foram incluídos dados sobre casos de hospitalização por SRAG no Brasil de 2013 a 2020, excluindo os casos causados pela COVID-19. Estes dados foram preparados para alimentar uma rede neural configurada para gerar modelos preditivos para séries temporais. A rede neural foi implementada com uma ferramenta de pipeline. Os modelos foram gerados para as cinco regiões brasileiras e validados para diferentes anos de surtos de SRAG. Com o uso de redes neurais, foi possível gerar modelos preditivos para picos de SRAG, volume de casos por temporada e para o início do período pré-epidêmico, com boa correlação de incidência semanal (R2 = 0,97; IC95%: 0,95-0,98, para a temporada de 2019 na Região Sudeste). Os modelos preditivos obtiveram uma boa previsão do volume de casos notificados de SRAG; dessa forma, foram observados 9.936 casos em 2019 na Região Sul, e a previsão feita pelos modelos mostrou uma mediana de 9.405 (IC95%: 9.105-9.738). A identificação do período de ocorrência de um surto de SRAG é possível por meio de modelos preditivos gerados com o uso de redes neurais e algoritmos que aplicam séries temporais.

Resumen: Brotes de síndrome respiratorio agudo grave (SRAG) ocurren todos los años, con picos estacionales que varían entre regiones geográficas. La notificación de los casos es importante para preparar las redes de atención a la salud para el cuidado y hospitalización de los pacientes. Por lo tanto, los gestores de salud deben tener herramientas adecuadas de planificación de recursos para las temporadas de SRAG. Este estudio tiene el objetivo de predecir brotes de SRAG con base en modelos generados con aprendizaje automático utilizando datos de hospitalización por SRAG. Se incluyeron datos sobre casos de hospitalización por SRAG en Brasil desde 2013 hasta 2020, salvo los casos causados por la COVID-19. Se prepararon estos datos para alimentar una red neural configurada para generar modelos predictivos para series temporales. Se implementó la red neural con una herramienta de canalización. Se generaron los modelos para las cinco regiones brasileñas y se validaron para diferentes años de brotes de SRAG. Con el uso de redes neurales, se pudo generar modelos predictivos para los picos de SRAG, el volumen de casos por temporada y para el inicio del periodo pre-epidémico, con una buena correlación de incidencia semanal (R2 = 0,97; IC95%: 0,95-0,98, para la temporada de 2019 en la Región Sudeste). Los modelos predictivos tuvieron una buena predicción del volumen de casos notificados de SRAG; así, se observaron 9.936 casos en 2019 en la Región Sur, y la predicción de los modelos mostró una mediana de 9.405 (IC95%: 9.105-9.738). La identificación del periodo de ocurrencia de un brote de SRAG es posible a través de modelos predictivos generados con el uso de redes neurales y algoritmos que aplican series temporales.

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Computational modelling was used to assess the capability of a deterministic and a probabilistic method to predict the incidence of AIS3+ injuries in passenger car occupants by comparing the predictions of the methods to the actual injuries observed in real-world crashes. The likelihood of sustaining an injury was first calculated using a computer model for a selected set of injury criteria in different impact conditions based on real-world crashes; AIS3+ injuries were then predicted using each method separately. Regardless of the method, the number of serious injuries was over-predicted. It was also noted that the used injury criteria suggested the occurrence of specific injuries that were not observed in the real world. Although both methods are susceptible to be adapted to improve their predictions, the question of the suitability of using some of the most commonly accepted injury criteria used with crash test dummies for injury assessment with human body models deserves further research.

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Objetivo. Analizar el rendimiento biomecánico de las placas de tibia proximal utilizadas en fracturas de platillos tibiales evaluado a través de modelos de elementos finitos. Métodos. Se realizará una búsqueda exhaustiva en PubMed/Medline, Embase, Lilacs, Web of Science y Google Scholar. No se utilizará ninguna restricción de idioma o estado de publicación. Dos revisores examinarán de forma independiente los posibles artículos elegibles, de acuerdo con los criterios de selección predefinidos. Se incluirán los estudios que evalúen el rendimiento de los platillos tibiales proximales utilizados en las fracturas del platillo tibial evaluadas mediante el análisis de elementos finitos. La extracción de datos sobre las características del estudio, los métodos, los resultados y la evaluación del riesgo de sesgo se realizará mediante un formulario estandarizado. Considerando el diseño de estudio no se requiere evaluación por comité de ética. Los resultados de esta revisión se difundirán a través de la publicación en revistas revisadas por pares, redes sociales y congresos de la especialidad. Se espera que los resultados de esta revisión permitan optimizar los resultados del manejo quirúrgico de las fracturas de platillos tibiales. Número de registro PROSPERO: CRD42023396015.

Objetive. To analyze the biomechanical performance of proximal tibial plates used in tibial plate fractures evaluated through finite element modeling. Methods. A comprehensive search will be conducted in PubMed/Medline, Embase, Lilacs, Web of Science, and Google Scholar. No language or publication status restrictions will be used. Two reviewers will independently review potential eligible articles according to predefined selection criteria. Studies evaluating the performance of proximal tibial splints used in tibial splint fractures assessed by finite element analysis will be included. Data extraction on study characteristics, methods, results, and risk of bias assessment will be performed using a standardized form. Considering the study design, evaluation by an ethics committee is not required. The results of this review will be disseminated through publication in peer-reviewed journals, social networks and specialty congresses. It is expected that the results of this review will allow optimizing the results of the surgical management of tibial plate fractures. PROSPERO registration number: CRD42023396015.

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Prolongation of the action potential duration (APD) could prevent reentrant arrhythmias if prolongation occurs at the fast excitation rates of tachycardia with minimal prolongation at slow excitation rates (i.e., if prolongation is positive rate-dependent). APD prolongation by current anti-arrhythmic agents is either reverse (larger APD prolongation at slow rates than at fast rates) or neutral (similar APD prolongation at slow and fast rates), which may not result in an effective anti-arrhythmic action. In this report we show that, in computer models of the human ventricular action potential, the combined modulation of both depolarizing and repolarizing ion currents results in a stronger positive rate-dependent APD prolongation than modulation of repolarizing potassium currents. A robust positive rate-dependent APD prolongation correlates with an acceleration of phase 2 repolarization and a deceleration of phase 3 repolarization, which leads to a triangulation of the action potential. A positive rate-dependent APD prolongation decreases the repolarization reserve with respect to control, which can be managed by interventions that prolong APD at fast excitation rates and shorten APD at slow excitation rates. For both computer models of the action potential, ICaL and IK1 are the most important ion currents to achieve a positive rate-dependent APD prolongation. In conclusion, multichannel modulation of depolarizing and repolarizing ion currents, with ion channel activators and blockers, results in a robust APD prolongation at fast excitation rates, which should be anti-arrhythmic, while minimizing APD prolongation at slow heart rates, which should reduce pro-arrhythmic risks.

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Protein structures may be used to draw functional implications at the residue level, but how sensitive are these implications to the exact structure used? Calculation of the effects of SARS-CoV-2 S-protein mutations based on experimental cryo-electron microscopy structures have been abundant during the pandemic. To understand the precision of such estimates, we studied three distinct methods to estimate stability changes for all possible mutations in 23 different S-protein structures (3.69 million ΔΔG values in total) and explored how random and systematic errors can be remedied by structure-averaged mutation group comparisons. We show that computational estimates have low precision, due to method and structure heterogeneity making results for single mutations uninformative. However, structure-averaged differences in mean effects for groups of substitutions can yield significant results. Illustrating this protocol, functionally important natural mutations, despite individual variations, average to a smaller stability impact compared to other possible mutations, independent of conformational state (open, closed). In summary, we document substantial issues with precision in structure-based protein modeling and recommend sensitivity tests to quantify these effects, but also suggest partial solutions to the problem in the form of structure-averaged "ensemble" estimates for groups of residues when multiple structures are available.

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Mathematical models of the cardiovascular system have come a long way since they were first introduced in the early 19th century. Driven by a rapid development of experimental techniques, numerical methods, and computer hardware, detailed models that describe physical scales from the molecular level up to organs and organ systems have been derived and used for physiological research. Mathematical and computational models can be seen as condensed and quantitative formulations of extensive physiological knowledge and are used for formulating and testing hypotheses, interpreting and directing experimental research, and have contributed substantially to our understanding of cardiovascular physiology. However, in spite of the strengths of mathematics to precisely describe complex relationships and the obvious need for the mathematical and computational models to be informed by experimental data, there still exist considerable barriers between experimental and computational physiological research. In this review, we present a historical overview of the development of mathematical and computational models in cardiovascular physiology, including the current state of the art. We further argue why a tighter integration is needed between experimental and computational scientists in physiology, and point out important obstacles and challenges that must be overcome in order to fully realize the synergy of experimental and computational physiological research.

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Pharmacological agents that prolong action potential duration (APD) to a larger extent at slow rates than at the fast excitation rates typical of ventricular tachycardia exhibit reverse rate dependence. Reverse rate dependence has been linked to the lack of efficacy of class III agents at preventing arrhythmias because the doses required to have an antiarrhythmic effect at fast rates may have pro-arrhythmic effects at slow rates due to an excessive APD prolongation. In this report, we show that, in computer models of the ventricular action potential, APD prolongation by accelerating phase 2 repolarization (by increasing IKs ) and decelerating phase 3 repolarization (by blocking IKr and IK1 ) results in a robust positive rate dependence (i.e., larger APD prolongation at fast rates than at slow rates). In contrast, APD prolongation by blocking a specific potassium channel type results in reverse rate dependence or a moderate positive rate dependence. Interventions that result in a strong positive rate dependence tend to decrease the repolarization reserve because they require substantial IK1 block. However, limiting IK1 block to ~50% results in a strong positive rate dependence with moderate decrease in repolarization reserve. In conclusion, the use of a combination of IKs activators and IKr and IK1 blockers could result in APD prolongation that potentially maximizes antiarrhythmic effects (by maximizing APD prolongation at fast excitation rates) and minimizes pro-arrhythmic effects (by minimizing APD prolongation at slow excitation rates).

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This chapter introduces the basis of computational chemistry and discusses how computational methods have been extended from physical to biological properties, and toxicology in particular, modeling. Since about three decades, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Animal and wet experiments, aimed at providing a standardized result about a biological property, can be mimicked by modeling methods, globally called in silico methods, all characterized by deducing properties starting from the chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (quantitative structure-activity relationships), and models that check relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. Virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

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Accurate prediction of protein stability changes upon mutation (ΔΔG) is increasingly important to evolution studies, protein engineering, and screening of disease-causing gene variants but is challenged by biases in training data. We investigated 45 linear regression models trained on data sets that account systematically for destabilization bias and mutation-type bias BM . The models were externally validated on three test data sets probing different pathologies and for internal consistency (symmetry and neutrality). Model structure and performance substantially depended on training data and even fitting method. We developed two final models: SimBa-IB for typical natural mutations and SimBa-SYM for situations where stabilizing and destabilizing mutations occur to a similar extent. SimBa-SYM, despite is simplicity, is essentially non-biased (vs. the Ssym data set) while still performing well for all data sets (R ~ 0.46-0.54, MAE = 1.16-1.24 kcal/mol). The simple models provide advantage in terms of interpretability, use and future improvement, and are freely available on GitHub.

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GOAL: The impact of hyperthermia (HT) method on tumor drug uptake with thermosensitive liposomes (TSL) is not well understood. METHODS: We created realistic three-dimensional (3-D) computer models that simulate TSL-encapsulated doxorubicin (TSL-DOX) delivery in mouse tumors with three HT methods (thermistor probe (T), laser (L) and water bath (WB), at 15 min and 60 min HT duration), with corroborating in vivo studies. RESULTS: Average computer model-predicted tumor drug concentrations (µg/g) were 8.8(T, 15 min), 21.0(T, 60 min), 14.1(L, 15 min), 25.2(L, 60 min), 9.4(WB, 15 min), and 8.7(WB, 60 min). Tumor fluorescence was increased by 2.6 × (T) and 1.6 × (L) when HT duration was extended from 15 to 60 min (p < 0.05), with no increase for WB HT. Pharmacokinetic analysis confirmed that water bath HT causes rapid depletion of encapsulated TSL-DOX in systemic circulation due to the large heated tissue volume. CONCLUSIONS: Untargeted large volume HT causes poor tumor drug uptake from TSL.

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The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.

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BACKGROUND: Cardiac Resynchronization Therapy (CRT) in dyssynchronous heart failure patients is ineffective in 20-30% of cases. Sub-optimal left ventricular (LV) pacing location can lead to non-response, thus there is interest in LV lead location optimization. Invasive acute haemodynamic response (AHR) measurements have been used to optimize the LV pacing location during CRT implantation. In this manuscript, we aim to predict the optimal lead location (AHR>10%) with non-invasive computed tomography (CT) based measures of cardiac anatomical and mechanical properties, and simulated electrical activation times. METHODS: Non-invasive measurements from CT images and ECG were acquired from 34 patients indicated for CRT upgrade. The LV lead was implanted and AHR was measured at different pacing sites. Computer models of the ventricles were used to simulate the electrical activation of the heart, track the mechanical motion throughout the cardiac cycle and measure the wall thickness of the LV on a patient specific basis. RESULTS: We tested the ability of electrical, mechanical and anatomical indices to predict the optimal LV location. Electrical (RV-LV delay) and mechanical (time to peak contraction) indices were correlated with an improved AHR, while wall thickness was not predictive. A logistic regression model combining RV-LV delay and time to peak contraction was able to predict positive response with 70 ± 11% accuracy and AUROC curve of 0.73. CONCLUSION: Non-invasive electrical and mechanical indices can predict optimal epicardial lead location. Prospective analysis of these indices could allow clinicians to test the AHR at fewer pacing sites and reduce time, costs and risks to patients.

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LAY SUMMARY: The goal of delivering personalized lung cancer screening is a worthy one. It is inspiring to envision a future in which screening decisions are informed by the best available evidence, tailored to an individual's specific characteristics, and consistent with their preferences and values. At the societal level, tradeoffs between effectiveness, efficiency and equity are inevitable and will need to be balanced exquisitely, with ample input from patients and other stakeholders. Tools such as the ENGAGE framework will continue to enlighten and to shape the ongoing conversation.

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To study the effects of the control temperature, ablation time, and the background tissue surrounding the tumor on the size of the ablation zone on radiofrequency ablation (RFA) of osteoid osteoma (OO). Finite element models of non-cooled temperature-controlled RFA of typical OOs were developed to determine the resulting ablation radius at control temperatures of 70, 80, and 90°C. Three different geometries were used, mimicking common cases of OO. The ablation radius was obtained by using the Arrhenius equation to determine cell viability. Ablation radii were larger for higher temperatures and also increased with time. All geometries and control temperatures tested had ablation radii larger than the tumor. The ablation radius developed rapidly in the first few minutes for all geometries and control temperatures tested, developing slowly towards the end of the ablation. Resistive heating and the temperature distribution showed differences depending on background tissue properties, resulting in differences in the ablation radius on each geometry. The ablation radius has a clear dependency not only on the properties of the tumor but also on the background tissue. Lower background tissue's electrical conductivity and blood perfusion rates seem to result in larger ablation zones. The differences observed between the different geometries suggest the need for patient-specific planning, as the anatomical variations could cause significantly different outcomes where models like the one here presented could help to guarantee safe and successful tumor ablations.

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This article is an insight into interdisciplinary topics in the field of civil engineering, morphology, architecture, mechanics, and computer programming. A novel method for shaping unconventional complex roofs in which regular folded units transformed into various shells are used as a complex substitute material is proposed. The original method's algorithm for building systems of planes defining diversified polyhedral networks in the three-dimensional space by means of division coefficients of the subsequently determined vertices is presented. The algorithm is based on the proportions between the lengths of the edges of the reference network, the location and shape of the ruled shell units included in the designed complex roof structure, so it is intuitive. The shell units are made up of nominally flat folded sheets transformed effectively into shell forms whose static-strength properties are controlled by geometric quantities characteristic of ruled surfaces. The presented original approach to the shaping of the shell roof structures determining specific complex building forms allows us to go beyond the limitations related to the orthotropic structure of the folded roof sheeting and the shape transformations.

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Background: Mechanistic computer models for calculation of total and regional deposition of aerosols in the lungs are important tools for predicting or understanding clinical studies and for facilitating development of pharmaceutical inhalation products. Validation of such models must be indirect since generational in vivo data are lacking. Planar scintigraphy is probably the most common method addressing regional lung deposition in humans. Scintigraphic regions of interest (ROI) contain mixtures of airway generations and can therefore not be directly compared to model results. We propose a method to translate computed deposition per generation to deposition in scintigraphic ROI to be able to compare computed results with corresponding results obtained in humans. Methods: The total and regional lung deposition computed by the one-dimensional algebraic typical-path software Mimetikos Preludium was compared for 18 study legs in 14 published deposition studies involving 9 dry powder inhaler brands to the activity in planar scintigraphic ROIs (oropharyngeal, central [C], intermediate, and peripheral [P]) using for the computed regional lung distribution a generic mapping of the contribution of each airway generation to the ROIs. Results: The computed oropharyngeal and total lung deposition correlated with high significance (p < 0.0001) to the scintigraphic results with a near one-to-one relationship. For the regional lung distribution, computed C, P, and P/C results correlated with high significance (p < 0.01) to the corresponding scintigraphic measures. The computed C (P) deposition was on average about 28% lower (8% higher) than the mean scintigraphic results. The computed P/C ratio was on average 29% higher than the mean scintigraphic ratio. Conclusions: The results indicate that both the computational deposition model and the mapping algorithm are valid. The small underprediction of the C region merits further investigations. We believe that this method may prove useful also for the validation of computational fluid particle dynamic lung deposition models.

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BACKGROUND: In the process of developing an implant, computer simulation involving finite element (FE) methods allows the early identification of design-related issues, thus reducing the development process to a minimum. In addition, the FE simulation is used for selecting testing combinations in order to provide the relevant authority with proof of a "worst-case" construct scenario for the subsequent experimental fatigue test. RESULTS: Research studies with FE simulations show that implant positioning may affect mechanical loads under certain circumstances and, therefore, influence the preclinical evaluation of the prostheses. DISCUSSION: Although the FE simulation currently contributes significantly to preclinical testing, a standardization of the calculation models allowing comparability of results is lacking. Furthermore, the development of new dynamic and realistic models is necessary in order to identify complex damage modes that currently cannot be reproduced experimentally. When considering everyday clinical life in particular, models that can reproduce intraoperative kinematic changes and the resulting incorrect loads of the implant, as well as address these problems by changing the position or design of the prosthesis, are necessary and would help in future.

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OBJECTIVES: Despite more than 40 years of experience performing the Bethesda assay (BA), poor intra- and interlaboratory precision remains the biggest laboratory challenge to date. METHODS: The BA procedure was modeled using stochastic simulation techniques to determine the precision of the BA up to dilutions of 1:4,096, to estimate the minimum significant relative change at various inhibitor titers, and to understand the laboratory procedural variables that could significantly affect the performance of the BA at high dilutions. RESULTS: Selecting the lowest dilution tube with a residual activity closest to 25% for calculating the reported Bethesda titer (BT), using a factor activity assay with a coefficient of variation less than or equal to 7.5% in the range of 15% to 50% factor activity level, performing the factor activity measurement in replicates, and minimizing pipette volumetric error resulted in the lowest imprecision in the reported BT. The factor neutralization kinetics of the inhibitor appear to have little impact on the precision of the assay if the incubation time is greater than 90 minutes. CONCLUSIONS: This in silico model will assist future laboratory efforts in standardizing the quantification of specific coagulation factor inhibitors and improving the precision of the reported results.

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