Background: Due to the complicated and time-consuming physiological procedure of bone healing, certain graft materials have been frequently used to enhance the reconstruction of the normal bone architecture. However, owing to the limitations of these graft materials, some pharmaceutical alternatives are considered instead. Chitosan is a biopolymer with many distinguishing characteristics that make it one of the best materials to be used as a drug delivery system for simvastatin. Simvastatin is a cholesterol lowering drug, and an influencer in bone formation process, because it stimulates osteoblasts differentiation, bone morphogenic protein 2, and vascular endothelial growth factor. Objectives: histological, histochemical and histomorphometrical analyses were carried out to evaluate the effect of local application of chitosan simvastatin nanoparticles (ChSimN) on bone healing. Materials and Methods: New Zealand rabbits (n=14) were used in this study. Two defects were made: one on the right side (the experimental side) that received ChSimN and the other one on the left side (the control side), which left to heal spontaneously. Seven rabbits were sacrificed after 2 weeks of the experiments, while the others after 4 weeks. Bone samples were taken for histological and histomorphometric study after the sacrifice. Results: The histological study, using both H&E and Masson’s Trichrome stain, revealed that the ChSimN group recorded an increased amount of bone formation at both time points. Histomorphometrical analysis recorded a significant increment in bone marrow and trabecular areas in the ChSimN group. Conclusion: ChSimN had a pronounced effect on bone formation.
Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep
... Show MoreIn this paper, a fusion of K models of full-rank weighted nonnegative tensor factor two-dimensional deconvolution (K-wNTF2D) is proposed to separate the acoustic sources that have been mixed in an underdetermined reverberant environment. The model is adapted in an unsupervised manner under the hybrid framework of the generalized expectation maximization and multiplicative update algorithms. The derivation of the algorithm and the development of proposed full-rank K-wNTF2D will be shown. The algorithm also encodes a set of variable sparsity parameters derived from Gibbs distribution into the K-wNTF2D model. This optimizes each sub-model in K-wNTF2D with the required sparsity to model the time-varying variances of the sources in the s
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