Biodegradation of an injectable treated dentin matrix hydrogel as a novel pulp capping agent for dentin regeneration.

BACKGROUND: A novel injectable mixture termed treated dentin matrix hydrogel (TDMH) has been introduced for restoring dentin defect in DPC. However, no study evaluated its physiological biodegradation. Therefore, the present study aimed to assess scaffold homogeneity, mechanical properties and biodegradability in vitro and in vivo and the regenerated dentin induced by TDMH as a novel pulp capping agent in human permanent teeth. METHODS: Three TDMH discs were weighted, and dry/wet ratios were calculated in four slices from each disc to evaluate homogeneity. Hydrogel discs were also analyzed in triplicate to measure the compressive strength using a universal testing machine. The in vitro degradation behavior of hydrogel in PBS at 37 °C for 2 months was also investigated by monitoring the percent weight change. Moreover, 20 intact fully erupted premolars were included for assessment of TDMH in vivo biodegradation when used as a novel injectable pulp capping agent. The capped teeth were divided into four equal groups according to extraction interval after 2-, 8-, 12- and 16-weeks, stained with hematoxylin-eosin for histological and histomorphometric evaluation. Statistical analysis was performed using F test (ANOVA) and post hoc test (p = 0.05). RESULTS: No statistical differences among hydrogel slices were detected with (p = 0.192) according to homogeneity. TDMH compression modulus was (30.45 ± 1.11 kPa). Hydrogel retained its shape well up to 4 weeks and after 8 weeks completely degraded. Histological analysis after 16 weeks showed a significant reduction in TDMH area and a simultaneous significant increase in the new dentin area. The mean values of TDMH were 58.8% ± 5.9 and 9.8% ± 3.3 at 2 and 16 weeks, while the new dentin occupied 9.5% ± 2.8 at 2 weeks and 82.9% ± 3.8 at 16 weeks. CONCLUSIONS: TDMH was homogenous and exhibited significant stability and almost completely recovered after excessive compression. TDMH generally maintained their bulk geometry throughout 7 weeks. The in vivo response to TDMH was characterized by extensive degradation of the hydrogel and dentin matrix particles and abundant formation of new dentin. The degradation rate of TDMH matched the rate of new dentin formation. TRIAL REGISTRATION: PACTR201901866476410: 30/1/2019.

Solving text clustering problem using a memetic differential evolution algorithm.

The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution (DE) mutation strategy with the memetic algorithm (MA). This hybridization intends to enhance the quality of text clustering and improve the exploitation and exploration capabilities of the algorithm. Our experimental results based on six standard text clustering benchmark datasets (i.e. the Laboratory of Computational Intelligence (LABIC)) have shown that the MDETC algorithm outperformed other compared clustering algorithms based on AUC metric, F-measure, and the statistical analysis. Furthermore, the MDETC is compared with the state of art text clustering algorithms and obtained almost the best results for the standard benchmark datasets.

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems.

The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.