Investigation of mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model and experimental optimization/validation

Abstract

Herein, we have developed a novel approach to investigate the mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model, experimental optimization of key parameters and experimental data validation of the predictive power of the model. The advantages of this study are that the impact of mechanical stimulation on bone regeneration in a porous biodegradable CaP scaffold is considered, experimental design is used to investigate the optimal combination of growth factors loaded on the porous biodegradable CaP scaffold to promote bone regeneration and the training, testing and analysis of the model are carried out by using experimental data, a data-mining algorithm and related sensitivity analysis. The results reveal that mechanical stimulation has a great impact on bone regeneration in a porous biodegradable CaP scaffold and the optimal combination of growth factors that are encapsulated in nanospheres and loaded into porous biodegradable CaP scaffolds layer-by-layer can effectively promote bone regeneration. Furthermore, the model is robust and able to predict the development of bone regeneration under specified conditions.

Progress in molecular docking

“…Considering the approximation capacity of the scoring function and incomplete collection of conformations, the molecular docking score of inactive molecules will be improperly so high that implicates false positive [24][25][26]. Furthermore, if the actual compound and the compound in database are significantly different in physical properties, the molecular docking score will be abnormal [27].…”

Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis

“…For example, the SAH intervention experiment sample size was too small for us to demonstrate high predictive accuracy for the model. In future work, we will integrate more recent bioinformatics research algorithms (Zhang et al, 2016(Zhang et al, , 2017a(Zhang et al, , 2018(Zhang et al, , 2019aGao et al, 2017; and data into the system to overcome the problems.…”

“…First, we use SAH intervention experiments to screen out candidate genes that are susceptible to LCN2, then employ Fisher's exact test (Xie et al, 2011;Li et al, 2017;Xia et al, 2017;Zhang et al, 2019b) to choose signaling pathways from among the candidates under different experimental conditions. Second, we use E-Bayes (Carlin and Louis, 2010), SVM-RFE (Duan et al, 2005), SPCA (Zou et al, 2006), and statistical tests (Zhang et al, 2016(Zhang et al, , 2018(Zhang et al, , 2019b(Zhang et al, ,d, 2020Xiao et al, 2019) to investigate key genes from experimental data by considering both SAH and LCN2 as factors. Third, we integrate the logistic regression (LR), support vector machine (SVM), and Naive-Bayes algorithms (Xia et al, 2017;Zhang et al, 2017aZhang et al, , 2019a into an ensemble learning model (Gao et al, 2017;Zhang et al, 2019b) to build a model for early SAH prediction.…”

Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge

“…The main contributions and innovations of this work are summarized as follows: (a) this is the first research work combining a hybrid chaos system with the BBO algorithm to train MLPs; (b) the method named OBL was used in the mutation operator of HCBBO to improve the convergence of the algorithm; and (c) the results demonstrate that HCBBO has better convergence capabilities than BBO, PSO, and GA. In the future, we will apply the trained neural networks to analyze the big medical data and integrate more novel data mining algorithms [29,[31][32][33][34][35] into HCBBO.…”