In this study, a novel nanofibrous hybrid scaffold based on silk fibroin (SF) and different weight ratios of kappa-carrageenan (k-CG) (1, 3, and 5 mg of k-CG in 1 mL of 12 wt% SF solution) was prepared using electrospinning and genipin (GP) as a crosslinker. The presence of k-CG in SF nanofibers was analyzed and confirmed using Fourier transform infrared spectroscopy (FTIR). In addition, X-ray diffraction (XRD) analysis confirmed that GP could cause SF conformation to shift from random coils or α-helices to β-sheets and thereby facilitate a more crystalline and stable structure. The ultimate tensile strength (UTS) and Young’s modulus of the SF mats were enhanced after crosslinking with GP from 3.91 ± 0.2 MPa to 8.50 ± 0.3 MPa and from 9.17 ± 0.3 MPa to 31.2 ± 1.2 MP, respectively. Notably, while the mean fiber diameter, wettability, and biodegradation rate of the SF nanofibers increased with increasing k-CG content, a decreasing effect was determined in terms of UTS and Young’s modulus. Additionally, better cell viability and proliferation were observed on hybrid scaffolds with the highest k-CG content. Osteogenic differentiation was determined from alkaline phosphatase (ALP) activity and Alizarin Red staining and expression of osteogenic marker genes. To this end, we noticed that k-CG enhanced ALP activity, calcium deposition, and expression of osteogenic genes on the hybrid scaffolds. Overall, hybridization of SF and k-CG can introduce a promising scaffold for bone regeneration; however, more biological evaluations are required.
Calcium phosphate cements (CPCs) offer a promising solution for treating bone defects due to their osteoconductive, injectable, biocompatible, and bone replacement properties. However, their brittle nature restricts their utilization to non-load-bearing applications. In this study, the impact of hybrid silk fibroin (SF) and kappa-carrageenan (k-CG) nanofibers as reinforcements in CPC was investigated. The CPC composite was fabricated by incorporating electrospun nanofibers in 1, 3, and 5% volume fractions. The morphology, mineralization, mechanical properties, setting time, injectability, cell adhesion, and mineralization of the CPC composites were analyzed. The results demonstrated that the addition of the nanofibers improved the CPC mixture, leading to an increase in compressive strength (14.8 ± 0.3 MPa compared to 8.1 ± 0.4 MPa of the unreinforced CPC). Similar improvements were seen in the bending strength and work fracture (WOF). The MC3T3-E1 cell culture experiments indicated that cells attached well to the surfaces of all cement samples and tended to join their adjacent cells. Additionally, the CPC composites showed higher cell mineralization after a culture period of 14 days, indicating that the SF/k-CG combination has potential for applications as a CPC reinforcement and bone cell regeneration promoter.
Cloud computing provides a set of resources and services for customers on the Internet on demand and based on a pay as you go model. Cloud providers are looking to decrease costs and increase profits. Therefore, resource management and provisioning are very important for cloud providers. Automated scaling can be used to provide resources for user requests. Auto‐scaling can decrease the total operational costs for providers, although it does have its own cost and time overheads. In this paper, a new solution is presented for resource provisioning on multi‐layered cloud applications based on MAPE‐K loop. A weighted ensemble prediction model is proposed to estimate the resources utilization in each cloud layer. In addition, accuracy of the model and a regularization technique are used to regulate the weights of the models in the proposed hybrid prediction model. Furthermore, a decision tree‐based algorithm is presented to analyze status of the resources to make scaling decision. In addition, we propose a resource allocation algorithm that is based on Virtual Machine priority and request deadline in order to allocate requests on suitable resources. The experimental results indicate that the proposed algorithm has the best performance among its counterparts.
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