Adding mechanobiological cell features to finite element analysis of an immediately loaded dental implant.

Finite element analysis (FEA) has been used to analyze the behavior of dental materials, mainly in implantology. However, FEA is a mechanical analysis and few studies have tried to simulate the biological characteristics of the healing process of loaded implants. This study used the rule of mixtures to simulate the biological healing process of immediate implants in an alveolus socket and bone-implant junction interface through FEA. Three-dimensional geometric models of the structures were obtained, and material properties were derived from the literature. The rule of mixtures was used to simulate the healing periods-immediate and early loading, in which the concentration of each cell type, based on in vivo studies, influenced the final elastic moduli. A 100 N occlusal load was simulated in axial and oblique directions. The models were evaluated for maximum and minimum principal strains, and the bone overload was assessed through Frost's mechanostat. There was a higher strain concentration in the healing regions and cortical bone tissue near the cervical portion. The bone overload was higher in the immediate load condition. The method used in this study may help to simulate the biological healing process and could be useful to relate FEA results to clinical practice.

Particulate matter forecast and prediction in Curitiba using machine learning.

Introduction: Air quality is directly affected by pollutant emission from vehicles, especially in large cities and metropolitan areas or when there is no compliance check for vehicle emission standards. Particulate Matter (PM) is one of the pollutants emitted from fuel burning in internal combustion engines and remains suspended in the atmosphere, causing respiratory and cardiovascular health problems to the population. In this study, we analyzed the interaction between vehicular emissions, meteorological variables, and particulate matter concentrations in the lower atmosphere, presenting methods for predicting and forecasting PM2.5. Methods: Meteorological and vehicle flow data from the city of Curitiba, Brazil, and particulate matter concentration data from optical sensors installed in the city between 2020 and 2022 were organized in hourly and daily averages. Prediction and forecasting were based on two machine learning models: Random Forest (RF) and Long Short-Term Memory (LSTM) neural network. The baseline model for prediction was chosen as the Multiple Linear Regression (MLR) model, and for forecast, we used the naive estimation as baseline. Results: RF showed that on hourly and daily prediction scales, the planetary boundary layer height was the most important variable, followed by wind gust and wind velocity in hourly or daily cases, respectively. The highest PM prediction accuracy (99.37%) was found using the RF model on a daily scale. For forecasting, the highest accuracy was 99.71% using the LSTM model for 1-h forecast horizon with 5 h of previous data used as input variables. Discussion: The RF and LSTM models were able to improve prediction and forecasting compared with MLR and Naive, respectively. The LSTM was trained with data corresponding to the period of the COVID-19 pandemic (2020 and 2021) and was able to forecast the concentration of PM2.5 in 2022, in which the data show that there was greater circulation of vehicles and higher peaks in the concentration of PM2.5. Our results can help the physical understanding of factors influencing pollutant dispersion from vehicle emissions at the lower atmosphere in urban environment. This study supports the formulation of new government policies to mitigate the impact of vehicle emissions in large cities.

Tradescantia response to air and soil pollution, stamen hair cells dataset and ANN color classification.

Tradescantia plant is a complex system that is sensible to environmental factors such as water supply, pH, temperature, light, radiation, impurities, and nutrient availability. It can be used as a biomonitor for environmental changes; however, the bioassays are time-consuming and have a strong human interference factor that might change the result depending on who is performing the analysis. We have developed computer vision models to study color variations from Tradescantia clone 4430 plant stamen hair cells, which can be stressed due to air pollution and soil contamination. The study introduces a novel dataset, Trad-204, comprising single-cell images from Tradescantia clone 4430, captured during the Tradescantia stamen-hair mutation bioassay (Trad-SHM). The dataset contain images from two experiments, one focusing on air pollution by particulate matter and another based on soil contaminated by diesel oil. Both experiments were carried out in Curitiba, Brazil, between 2020 and 2023. The images represent single cells with different shapes, sizes, and colors, reflecting the plant's responses to environmental stressors. An automatic classification task was developed to distinguishing between blue and pink cells, and the study explores both a baseline model and three artificial neural network (ANN) architectures, namely, TinyVGG, VGG-16, and ResNet34. Tradescantia revealed sensibility to both air particulate matter concentration and diesel oil in soil. The results indicate that Residual Network architecture outperforms the other models in terms of accuracy on both training and testing sets. The dataset and findings contribute to the understanding of plant cell responses to environmental stress and provide valuable resources for further research in automated image analysis of plant cells. Discussion highlights the impact of turgor pressure on cell shape and the potential implications for plant physiology. The comparison between ANN architectures aligns with previous research, emphasizing the superior performance of ResNet models in image classification tasks. Artificial intelligence identification of pink cells improves the counting accuracy, thus avoiding human errors due to different color perceptions, fatigue, or inattention, in addition to facilitating and speeding up the analysis process. Overall, the study offers insights into plant cell dynamics and provides a foundation for future investigations like cells morphology change. This research corroborates that biomonitoring should be considered as an important tool for political actions, being a relevant issue in risk assessment and the development of new public policies relating to the environment.

Simulation of bone remodeling around a femoral prosthesis using a model that accounts for biological and mechanical interactions.

The present study focuses on a model for three-dimensional bone remodeling of the human femur that considers cellular dynamics to determine the volume fraction of new bone. The model considers the interaction among responsive osteoblasts, active osteoblasts, and osteoclasts, as well as signaling molecules and parathyroid hormone (PTH). The stimulus of the model has a systemic origin due to the PTH effect, and a local origin due to the action of cytokines, growth factors, and mechanical stimuli near the site of the bone cells. The present work considers that the mechanical stimulus that activates cellular activity is obtained from stresses acting on the bone tissue and the number of daily loading cycles. In addition to simulating the bone modeling process in an intact femur, the numerical model is used to simulate bone adaptation in relation to the stress shielding phenomenon after the implantation of a femoral prosthesis. The results showed that the simulations provide a distribution of bone density that is similar to a radiograph and, in addition, allows the visualization of osteoblast and osteoclast dynamics in bone adaptation response after prosthesis implantation.

Bulk Fill flow resin contraction using 3D finite element model and calibration by Fiber Bragg Grating measurement.

Contraction due to polymerization of dental resin can cause failure on the adhesive interfaces, can lead to problems such as the appearance of gaps in the interfaces, postoperative sensitivity, pulp damage and the return of the caries. The objective of this work is the study of stresses on the dental adhesive that are generated by the process shrinkage of resin associated with biting forces. A laboratory experiment measured the strains and temperature inside the FiltekTM Bulk Fill Flow resin during the process of polymerization using Fiber Bragg Grating sensors in an ex vivo tooth. From tomographic images a three-dimensional geometric model of the tooth was reconstructed. A pre-tension was calibrated to simulate the residual contraction on the resin 3 D model. Finally, an Finite Element Method analysis was performed to access the adhesive stresses at the interface enamel/dentin with the adhesive, considering as loading the residual polymerization contraction of the dental resin and also biting loads. The model was able to represented the strain obtained in the laboratory experiment. The results of the stress analysis shows that the outer regions of adhesive are more prone to failure, as veried by dental surgeons in clinical practice.

Evaluation of bone remodeling around single dental implants of different lengths: a mechanobiological numerical simulation and validation using clinical data.

Algorithmic models have been proposed to explain adaptive behavior of bone to loading; however, these models have not been applied to explain the biomechanics of short dental implants. Purpose of present study was to simulate bone remodeling around single implants of different lengths using mechanoregulatory tissue differentiation model derived from the Stanford theory, using finite elements analysis (FEA) and to validate the theoretical prediction with the clinical findings of crestal bone loss. Loading cycles were applied on 7-, 10-, or 13-mm-long dental implants to simulate daily mastication and bone remodeling was assessed by changes in the strain energy density of bone after a 3, 6, and 12 months of function. Moreover, clinical findings of marginal bone loss in 45 patients rehabilitated with same implant designs used in the simulation (n = 15) were computed to validate the theoretical results. FEA analysis showed that although the bone density values reduced over time in the cortical bone for all groups, bone remodeling was independent of implant length. Clinical data showed a similar pattern of bone resorption compared with the data generated from mathematical analyses, independent of implant length. The results of this study showed that the mechanoregulatory tissue model could be employed in monitoring the morphological changes in bone that is subjected to biomechanical loads. In addition, the implant length did not influence the bone remodeling around single dental implants during the first year of loading.